AI - 900

Question 1

This is often the foundation for an AI system, and is the way we “teach” a computer model to make predictions and draw conclusions from data.

Answer 1

Machine learning

Question 3

Capabilities within AI to interpret the world visually through cameras, video, and images.

Answer 3

Computer vision

Question 4

Capabilities within AI for a computer to interpret written or spoken language, and respond in kind.

Answer 4

Natural language processing

Question 5

Capabilities within AI that deal with managing, processing, and using high volumes of data found in forms and documents.

Answer 5

Document intelligence

Question 6

Capabilities within AI to extract information from large volumes of often unstructured data to create a searchable knowledge store.

Answer 6

Knowledge mining

Question 7

Capabilities within AI that create original content in a variety of formats including natural language, image, code, and more.

Answer 7

Generative AI

Question 8

the foundation for most AI solutions

Answer 8

Machine Learning

Question 9

How does machine learning work.

Answer 9

Machines learn from data

Question 10

Machine learning models try to capture the relationship between …

Answer 10

Data

Question 11

Microsoft Azure provides the

Answer 11

Machine learning service

Question 12

Azure is

Answer 12

a cloud-based platform for creating, managing, and publishing machine learning models.

Question 13

Azure Machine Learning Studiooffers multiple authoring experiences such as

Answer 13

Automated machine learning: this feature enables non-experts to quickly create an effective machine learning model from data.

Azure Machine Learning designer: a graphical interface enabling no-code development of machine learning solutions.

Data metric visualization: analyze and optimize your experiments with visualization.

Notebooks: write and run your own code in managed Jupyter Notebook servers that are directly integrated in the studio.

Question 14

Automated machine learning:

Answer 14

this feature enables non-experts to quickly create an effective machine learning model from data.

Question 15

Azure Machine Learning designer:

Answer 15

a graphical interface enabling no-code development of machine learning solutions.

Question 16

Data metric visualization:

Answer 16

analyze and optimize your experiments with visualization

Question 17

Notebooks:

Answer 17

write and run your own code in managed Jupyter Notebook servers that are directly integrated in the studio.

Question 18

an area of AI that deals with visual processing. Let’s explore some of the possibilities that computer vision brings

Answer 18

Computer Vision

Question 19

… app is a great example of the power of computer vision. Designed for the blind and low vision community, the Seeing AI app harnesses the power of AI to open up the visual world and describe nearby people, text and objects.

Answer 19

Seeing AI

Question 20

common computer vision tasks.

Answer 20

Image classification, Object detection, Semantic segmentation, Image analysis

Question 21

… an advanced machine learning technique in which individual pixels in the image are classified according to the object to which they belong. For example, a traffic monitoring solution might overlay traffic images with “mask” layers to highlight different vehicles using specific colors.

Answer 21

Semantic segmentation

Question 22

… involves training a machine learning model to classify images based on their contents. For example, in a traffic monitoring solution you might use an image classification model to classify images based on the type of vehicle they contain, such as taxis, buses, cyclists, and so on.

Answer 22

Image classification

Question 23

machine learning models are trained to classify individual objects within an image, and identify their location with a bounding box. For example, a traffic monitoring solution might use object detection to identify the location of different classes of vehicle.

Answer 23

Object detection

Question 24

You can create solutions that combine machine learning models with advanced … techniques to extract information from images, including “tags” that could help catalog the image or even descriptive captions that summarize the scene shown in the image.

Answer 24

image analysis

Question 25

… is a specialized form of object detection that locates human faces in an image. This can be combined with classification and facial geometry analysis techniques to recognize individuals based on their facial features.

Answer 25

Face detection

Question 26

… is a technique used to detect and read text in images. You can use OCR to read text in photographs (for example, road signs or store fronts) or to extract information from scanned documents such as letters, invoices, or forms.

Answer 26

Optical character recognition

Question 27

Use … to develop computer vision solutions. The service features are available for use and testing in the…and other programming language

Answer 27

Azure AI Vision, Azure Vision Studio

Question 28

features of Azure AI Vision include:

Answer 28

Image Analysis: capabilities for analyzing images and video, and extracting descriptions, tags, objects, and text.

Face: capabilities that enable you to build face detection and facial recognition solutions.

Optical Character Recognition (OCR): capabilities for extracting printed or handwritten text from images, enabling access to a digital version of the scanned text.

Question 29

… is the area of AI that deals with creating software that understands written and spoken language.

Answer 29

Natural language processing (NLP)

Question 30

NLP software can:

Answer 30

Analyze and interpret text in documents, email messages, and other sources.

Interpret spoken language, and synthesize speech responses.

Automatically translate spoken or written phrases between languages.

Interpret commands and determine appropriate actions.


Question 31

You can use …and … to build natural language processing solutions.

Answer 31

Microsoft’sAzure AI Language, Azure AI Speech

Question 32

features of Azure AI Language include …

Answer 32

understanding and analyzing text, training conversational language models that can understand spoken or text-based commands, and building intelligent applications.


Question 33

Azure AI Speech features include …

Answer 33

speech recognition and synthesis, real-time translations, conversation transcriptions, and more.


Question 34

You can explore Azure AI Language features in the…and Azure AI Speech features in the….

Answer 34

Azure Language Studio, Azure Speech Studio

Question 35

… is the area of AI that deals with managing, processing, and using high volumes of a variety of data found in forms and documents. Document intelligence enables you to create software that can automate processing for contracts, health documents, financial forms and more

Answer 35

Document Intelligence

Question 36

You can use Microsoft’s… to build solutions that manage and accelerate data collection from scanned documents.

Answer 36

Azure AI Document Intelligence

Question 37

Features of Azure AI Document Intelligence help…

Answer 37

automate document processing in applications and workflows, enhance data-driven strategies, and enrich document search capabilities.

Question 38

You can use … to add intelligent document processing for invoices, receipts, health insurance cards, tax forms, and more. You can also use … to create custom models with your own labeled datasets. The service features are available for use and testing in the…and other programming languages.

Answer 38

prebuilt models, Azure AI Document Intelligence, Document Intelligence Studio

Question 39

… is the term used to describe solutions that involve extracting information from large volumes of often unstructured data to create a searchable knowledge store.

Answer 39

Knowledge mining

Question 40

One Microsoft knowledge mining solution is…, a private, enterprise, search solution that has tools for building indexes. The indexes can then be used for internal only use, or to enable searchable content on public facing internet assets.

Answer 40

Azure Cognitive Search

Question 41

… can utilize the built-in AI capabilities of Azure AI services such as image processing, document intelligence, and natural language processing to extract data. The product’s AI capabilities makes it possible to index previously unsearchable documents and to extract and surface insights from large amounts of data quickly.

Answer 41

Azure Cognitive Search

Question 42

… describes a category of capabilities within AI that create original content. People typically interact with generative AI that has been built into chat applications. Generative AI applications take in natural language input, and return appropriate responses in a variety of formats including natural language, image, code, and audio.

Answer 42

Generative AI

Question 43

In Microsoft Azure, you can use the… to build generative AI solutions.

Answer 43

Azure OpenAI service

Question 44

… is Microsoft’s cloud solution for deploying, customizing, and hosting generative AI models. It brings together the best of OpenAI’s cutting edge models and APIs with the security and scalability of the Azure cloud platform.

Answer 44

Azure OpenAI Service

Question 45

Azure OpenAI supports many foundation model choices that can serve different needs. The service features are available for use and testing in theAzure … and other programming languages. You can use the Azure OpenAI Studio user interface to manage, develop, and customize generative AI models.

Answer 45

OpenAI Studio

Question 46

The Challenges or Risks of AI include:

Answer 46

Bias can affect results
Errors may cause harm
Data could be exposed
Solutions may not work for everyone
Users must trust a complex system
Who’s liable for AI-driven decisions?

Question 47

six principles, designed to ensure that AI applications provide amazing solutions to difficult problems without any unintended negative consequences.

Answer 47

Fairness, Reliability and safety, Privacy and security, Inclusiveness, Transparency, Accountability

Question 48

AI systems should treat all people fairly. For example, suppose you create a machine learning model to support a loan approval application for a bank. The model should predict whether the loan should be approved or denied without bias. This bias could be based on gender, ethnicity, or other factors that result in an unfair advantage or disadvantage to specific groups of applicants.

Azure Machine Learning includes the capability to interpret models and quantify the extent to which each feature of the data influences the model’s prediction. This capability helps data scientists and developers identify and mitigate bias in the model.
Another example is Microsoft’s implementation ofResponsible AI with the Face service, which retires facial recognition capabilities that can be used to try to infer emotional states and identity attributes. These capabilities, if misused, can subject people to stereotyping, discrimination or unfair denial of services.

Answer 48

Fairness

Question 49

AI systems should perform …. For example, consider an AI-based software system for an autonomous vehicle; or a machine learning model that diagnoses patient symptoms and recommends prescriptions. Unreliability in these kinds of systems can result in substantial risk to human life.

AI-based software application development must be subjected to rigorous testing and deployment management processes to ensure that they work as expected before release.

Answer 49

Reliably and safely

Question 50

AI systems should be … and respect …. The machine learning models on which AI systems are based rely on large volumes of data, which may contain personal details that must be kept private. Even after the models are trained and the system is in production, privacy and security need to be considered. As the system uses new data to make predictions or take action, both the data and decisions made from the data may be subject to privacy or security concerns.

Answer 50

Secure, privacy

Question 51

Thru …, AI systems should empower everyone and engage people. AI should bring benefits to all parts of society, regardless of physical ability, gender, sexual orientation, ethnicity, or other factors.

Answer 51

Inclusiveness

Question 52

To achieve …, AI systems should be understandable. Users should be made fully aware of the purpose of the system, how it works, and what limitations may be expected.

Answer 52

Transparency

Question 53

People should be … for AI systems. Designers and developers of AI-based solutions should work within a framework of governance and organizational principles that ensure the solution meets ethical and legal standards that are clearly defined.

Answer 53

Accountable

Question 54

Machine learning is in many ways the intersection of two disciplines … and …

Answer 54

data science and software engineering

Question 55

The goal of machine learning is to use data to create a … model that can be incorporated into a software application or service. To achieve this goal requires collaboration between data scientists who explore and prepare the data before using it totraina machine learning model, and software developers who integrate the models into applications where they’re used to predict new data values (a process known as… ).

Answer 55

predictive, inferencing

Question 56

Fundamentally, a machine learning model is a software application that encapsulates a …to calculate an output value based on one or more input values. The process of defining that … is known as …. After the … has been defined, you can use it to predict new values in a process called….

Answer 56

function, function, training, function, inferencing

Question 57

The training data consists of past observations. In most cases, the observations include the observed … or …of the thing being observed, and the known … of the thing you want to train a model to predict (known as the…).


Answer 57

attributes, features, value, label

Question 58

You’ll often see the features referred to using the shorthand variable name…, and the label referred to as…. Usually, an observation consists of multiple feature values, soxis actually a…(an array with multiple values), like this:[x1,x2,x3,…].


Answer 58

x, y, vector

Question 59

An…is applied to the data to try to determine a relationship between the … and the …, and generalize that relationship as a calculation that can be performed on…to calculate…

Answer 59

algorithm, features, label, x, Y

Question 60

The specific algorithm used depends on the kind of … problem you’re trying to solve (more about this later), but the basic principle is to try tofitthe data to a function in which the values of the features can be used to calculate the…

Answer 60

predictive, label.


Question 61

The result of the algorithm is a…that encapsulates the calculation derived by the algorithm as afunction- let’s call itf. In mathematical notation:

y = f(x)

Answer 61

model

Question 62

The model is essentially a software program that encapsulates the … produced by the training process. You can input a set of …, and receive as an output a prediction of the corresponding …. Because the output from the model is a prediction that was calculated by the function, and not an observed value, you’ll often see the output from the function shown as…

Answer 62

function, feature values, label, ŷ

Question 63

… is a general term for machine learning algorithms in which the training data includes bothfeaturevalues and knownlabelvalues.

Answer 63

Supervisedmachine learning

Question 64

Supervised machine learning is used to train … by determining a relationship between the … and … in past observations, so that unknown … can be predicted for features in future cases.


Answer 64

models, features and labels, labels

Question 65

…is a form of supervised machine learning in which the label predicted by the model is a numeric value.

Answer 65

Regression

Question 66

…is a form of supervised machine learning in which the label represents a categorization, orclass. There are two common … scenarios.


Answer 66

Classification, classification

Question 67

In… classification, the label determines whether the observed itemis(orisn’t) an instance of a specific class. Or put another way, … classification models predict one of two mutually exclusive outcomes.

Answer 67

binary, binary

Question 68

In the … model predicts a…/…or…/…prediction for a single possible class.


Answer 68

Binary, true/false, positive/negative

Question 69

… classificationextends binary classification to predict a label that represents one of multiple possible classes.

Answer 69

Multiclass,

Question 70

In most scenarios that involve a known set of multiple classes, multiclass classification is used to predict … labels

Answer 70

mutually exclusive

Question 71

…machine learning involves training models using data that consists only offeaturevalues without any known labels.

Answer 71

Unsupervised

Question 72

… machine learning algorithms determine relationships between the features of the observations in the training data.


Answer 72

Unsupervised

Question 73

There are some … algorithms that you can use to trainmultilabelclassification models, in which there may be more than one valid label for a single observation.


Answer 73

Multiclass,

Question 74

…machine learning involves training models using data that consists only offeaturevalues without any known labels.

Answer 74

Unsupervised

Question 75

Unsupervised machine learning algorithms determine … between the features of the observations in the training data.


Answer 75

relationships

Question 76

The most common form of unsupervised machine learning is….

Answer 76

clustering

Question 77

A … algorithm identifies similarities between observations based on their …, and groups them into discrete clusters.

Answer 77

clustering, features,

Question 78

… is similar to multiclass classification; in that it categorizes observations into discrete groups. The difference is that when using classification, you already know the classes to which the observations in the training data belong.

Answer 78

clustering,

Question 79

In clustering, there’s no previously known … … and the algorithm groups the data observations based purely on similarity of features.

Answer 79

cluster label,

Question 80

In some cases, … is used to determine the set of classes that exist before training a classification model.

Answer 80

clustering

Question 81

… models are trained to predict numeric label values based on training data that includes both features and known labels.

Answer 81

Regression

Question 82

The process for training a regression model (or indeed, any … machine learning model) involves multiple iterations in which you use an appropriate algorithm (usually with some parameterized settings) to train a model, evaluate the model’s … …, and refine the model by repeating the training process with different … and … until you achieve an acceptable level of predictive accuracy.


Answer 82

supervised, predictive performance, algorithms and parameters

Question 83

Four key elements of the training process for supervised machine learning models

Answer 83

Split the training data (randomly) to create a dataset with which to train the model while holding back a subset of the data that you’ll use to validate the trained model.

Use an algorithm to fit the training data to a model. In the case of a regression model, use a regression algorithm such aslinear regression.

Use the validation data you held back to test the model by predicting labels for the features.

Compare the knownactuallabels in the validation dataset to the labels that the model predicted. Then aggregate the differences between thepredictedandactuallabel values to calculate a metric that indicates how accurately the model predicted for the validation data.


Question 84

After each train, validate, and evaluate iteration, you can repeat the process with different … and … until an acceptable evaluation metric is achieved.


Answer 84

algorithms and parameters

Question 85

A… … algorithm, works by deriving a function that produces a straight line through the intersections of thexandyvalues while minimizing the average distance between the line and the plotted points

Answer 85

linear regression,

Question 86

The … is the differences between the predicted (…)values and actual (…) values, from the validation dataset.

Answer 86

variance, ŷ, y,

Question 87

Based on the differences between the predicted and actual values, you can calculate some common metrics that are used to evaluate a regression model. They include:

Answer 87

Mean Absolute Error (MAE)

Mean Squared Error (MSE)

Root Mean Squared Error (RMSE)

Coefficient of determination (R2)

Question 88

Theabsolute errorfor each prediction, the distance either above or below the predicted outcome, can be summarized for the whole validation set as the…

Answer 88

mean absolute error(MAE)

Question 89

it may be more desirable to have a model that is consistently wrong by a small amount than one that makes fewer, but larger errors. One way to produce a metric that “amplifies” larger errors bysquaringthe individual errors and calculating the mean of the squared values. This metric is known as the… … …

Answer 89

mean squared error(MSE)

Question 90

The mean squared error helps take the magnitude of errors into account, but because itsquaresthe error values, the resulting metric no longer represents the quantity measured by the label. If we want to measure the error in terms of the number of ice creams, we need to calculate the…of the MSE; which produces a metric called, unsurprisingly,the … … … …

Answer 90

square root, Root Mean Squared Error

Question 91

The… … …(more commonly referred to asR2orR-Squared) is a metric that measures the proportion of variance in the validation results that can be explained by the model, as opposed to some anomalous aspect of the validation data.

Answer 91

coefficient of determination

Question 92

The calculation for …is more complex than for the previous metrics. It compares the sum of squared differences between predicted and actual labels with the sum of squared differences between the actual label values and the mean of actual label values.

…= 1- ∑(y-ŷ)2÷ ∑(y-ȳ)2

Answer 92

R2, R2

Question 93

The important point is that the result of R2 is a value between … and … that describes the proportion of variance explained by the model. In simple terms, the closer to … this value is, the better the model is fitting the validation data

Answer 93

0 and 1, 1

Question 94

In most real-world scenarios, a data scientist will use an iterative process to repeatedly train and evaluate a model, varying:

Answer 94

Feature selection and preparation (choosing which features to include in the model, and calculations applied to them to help ensure a better fit).

Algorithm selection (We explored linear regression in the previous example, but there are many other regression algorithms)

Algorithm parameters (numeric settings to control algorithm behavior, more accurately calledhyperparametersto differentiate them from thexandyparameters).


Question 95

Instead of calculating numeric values like a regression model, the algorithms used to train classification models calculate… …for class assignment and the evaluation metrics used to assess model performance compare the … classes to the … classes.

Answer 95

probabilityvalues, predicted, actual

Question 96

In most real scenarios, the data observations used to train and validate the binary model consist of … feature (x) values and ayvalue that is either … or ….

Answer 96

multiple, 1 or 0

Question 97

To train a binary classification model use an algorithm to fit the training data to a … that calculates theprobabilityof the class label beingtrue

Answer 97

function, true

Question 98

… is measured as a value between … and …, such that thetotalprobability forallpossible classes is ….

Answer 98

Probability, 0.0 and 1.0, 1.0

Question 99

There are many algorithms that can be used for binary classification, such aslogistic regression, which derives a…(S-shaped) function with values between … and ….

Answer 99

sigmoid, 0.0 and 1.0

Question 100

Despite its name, in machine learning,…is used for classification, not regression. The important point is thelogisticnature of the function it produces, which describes an S-shaped curve between a lower and upper value (0.0 and 1.0 when used for … …).

Answer 100

logistic regression, binary classification

Question 101

The … … … used to train binary classification models describes the probability ofybeing true (y=1) for a given value ofx. Mathematically, you can express the function like this:

f(x) = P(y=1 | x)

Answer 101

logistic regression function

Question 102

For logistical regression models, with three of six observations in the training data, we know thatyis definitelytrue, so the probability for those observations thaty=1 is…and for the other three, we know thatyis definitelyfalse, so the probability thaty=1 is…. The S-shaped curve describes the probability distribution so that plotting a value ofxon the line identifies the corresponding probability thatyis1.

Answer 102

1.0, 0.0

Question 103

The diagram for a logistical regression model also includes a horizontal line to indicate thethresholdat which a model based on this function will predicttrue(1) orfalse(0). The threshold lies at the … fory(P(y) = 0.5). For any values at this point or above, the model will predicttrue(1); while for any values below this point it will predictfalse(0). For example, for a patient with a blood glucose level of 90, the function would result in a probability value of 0.9. Since 0.9 is higher than the threshold of 0.5, the model would predicttrue(1) - in other words, the patient is predicted to have diabetes.

Answer 103

mid-point

Question 104

The first step in calculating evaluation metrics for a binary classification model is usually to create a matrix of the number of … and … predictions for each possible class label:

Answer 104

correct and incorrect

Question 105

Aconfusion matrix shows the prediction totals where:

Answer 105

ŷ=0 and y=0:True negatives(TN)
ŷ=1 and y=0:False positives(FP)
ŷ=0 and y=1:False negatives(FN)
ŷ=1 and y=1:True positives(TP)
ŷ
0. 1
————————
| | |
0. | |. |
Y. ————————
|. |. |
1. |. |. |
————————-

Question 106

The arrangement of the confusion matrix is such that correct (true) predictions are shown in a … line from … to … Often, color-intensity is used to indicate the number of predictions in each cell, so a quick glance at a model that predicts well should reveal a deeply shaded diagonal trend.

Answer 106

diagonal, top-left, bottom-right.

Question 107

The simplest metric you can calculate from the confusion matrix is… - the proportion of predictions that the model got ….

Answer 107

accuracy, right

(TN+TP) ÷ (TN+FN+FP+TP)

Question 108

Accuracy is calculated as:

Answer 108

(TN+TP) ÷ (TN+FN+FP+TP)

(2+3) ÷ (2+1+0+3)
= 5 ÷ 6
=0.83

So for our validation data, the diabetes classification model produced correct predictions 83% of the time.

Question 109

What is the problem with an accuracy model?

Answer 109

Suppose 11% of the population has diabetes. You could create a model that always predicts0, and it would achieve an accuracy of 89%, even though it makes no real attempt to differentiate between patients by evaluating their features. What we really need is a deeper understanding of how the model performs at predicting1for positive cases and0for negative cases.

Question 110

…is a metric that measures the proportion of positive cases that the model identified correctly. In other words, compared to the number of patients whohavediabetes, how many did the modelpredictto have diabetes?

The formula is:

TP ÷ (TP+FN)

Answer 110

Recall

Question 111

…is a similar metric to recall, but measures the proportion of predicted positive cases where the true label is actually positive. In other words, what proportion of the patientspredictedby the model to have diabetes actuallyhavediabetes?

The formula is:

TP ÷ (TP+FP)

Answer 111

Precision

Question 112

…is an overall metric that combined recall and precision. The formula is:

(2 x Precision x Recall) ÷ (Precision + Recall)

Answer 112

F1-score

Question 113

Another name for recall is the… … …, and there’s an equivalent metric called the… … …that is calculated asFP÷(FP+TN).

Answer 113

true positive rate(TPR), false positive rate(FPR)

Question 114

TPR and FPR metrics are often used to evaluate a model by plotting a… … … curve that compares the TPR and FPR for every possible threshold value between 0.0 and 1.0.

Answer 114

received operator characteristic(ROC)

Question 115

The … curve for a perfect model would go straight up the TPR axis on the left and then across the FPR axis at the top. Since the plot area for the curve measures 1x1, the area under this perfect curve would be 1.0 (meaning that the model is correct 100% of the time). In contrast, a diagonal line from the bottom-left to the top-right represents the results that would be achieved by randomly guessing a binary label; producing an area under the curve of 0.5. In other words, given two possible class labels, you could reasonably expect to guess correctly 50% of the time.

Answer 115

ROC

Question 116

As a supervised machine learning technique, … … follows the same iterativetrain, validate, and evaluateprocess as regression and binary classification in which a subset of the training data is held back to validate the trained model.

Answer 116

Multiclass classification

Question 117

Multiclass classification algorithms are used to calculate probability values for multiple class labels, enabling a model to predict the… …class for a given observation.

Answer 117

most probable

Question 118

To train a multiclass classification model, we need to use an algorithm to fit the training data to a function that calculates a probability value for each possible class. There are two kinds of algorithm you can use to do this:

Answer 118

One-vs-Rest (OvR) algorithms
Multinomial algorithms


Question 119

… algorithms train a binary classification function for each class, each calculating the probability that the observation is an example of the target class. Each function calculates the probability of the observation being a specific class compared toanyother class.

Answer 119

One-vs-Rest

f0(x) = P(y=0 | x)
f1(x) = P(y=1 | x)
f2(x) = P(y=2 | x)
 Each algorithm produces a sigmoid function that calculates a probability value between 0.0 and 1.0. A model trained using this kind of algorithm predicts the class for the function that produces the highest probability output.


Question 120

A … algorithm, creates a single function that returns a multi-valued output. The output is avector(an array of values) that contains theprobability distributionfor all possible classes - with a probability score for each class which when totaled adds up to 1.0:

f(x) =[P(y=0|x), P(y=1|x), P(y=2|x)]

Answer 120

multinomial

Question 121

An example of a Multinomial function is a…function, which could produce an output like the following example:

[0.2, 0.3, 0.5]

The elements in the vector represent the probabilities for classes 0, 1, and 2 respectively; so in this case, the class with the highest probability is2.

Answer 121

softmax

Question 122

You can evaluate a multiclass classifier by calculating … classification metrics for each individual class. Alternatively, you can calculate … metrics that take all classes into account.

Answer 122

binary, aggregate

Question 123

The confusion matrix for a multiclass classifier is similar to that of a binary classifier, except that it shows the number of predictions … … … ofpredicted(ŷ) andactualclass labels (y)

Answer 123

for each combination

Question 124

To calculate the overall accuracy, recall, and precision metrics, you use the total of the… … …and … metrics:

Overall accuracy= (TN+TP) ÷ (TN+FN+FP+TP)
Overall recall= TP ÷ (TP+FN)
Overall precision= TP ÷ (TP+FP)

TP: True Positive, FP: False Positive, TN: True Negative, FN: False Negative



Answer 124

TP,TN,FP, andFN

Question 125

The overall f1 score is based on … … and …. …

Answer 125

Overall precision and overall recall

Overall f1 = (2 x Precision x Recall) ÷ (Precision + Recall)

Question 126

Clusteringis a form of … machine learning in which observations are grouped into clusters based on similarities in their data values, or features. This kind of machine learning is considered … because it doesn’t make use of previously known label values to train a model. In a clustering model, the … is the cluster to which the observation is assigned, based only on its features.

Answer 126

unsupervised, unsupervised, label

For example, suppose a botanist observes a sample of flowers and records the number of leaves and petals on each flower:

There are no knownlabelsin the dataset, just twofeatures. The goal is not to identify the different types (species) of flower; just to group similar flowers together based on the number of leaves and petals.

Question 127

There are multiple algorithms you can use for clustering. One of the most commonly used algorithms isK-Meansclustering, which consists of the following steps:

Answer 127

The feature (x) values are vectorized to definen-dimensional coordinates (wherenis the number of features). In the flower example, we have two features: number of leaves (x1) and number of petals (x2). So, the feature vector has two coordinates that we can use to conceptually plot the data points in two-dimensional space ([x1,x2])

You decide how many clusters you want to use to group the flowers - call this valuek. For example, to create three clusters, you would use akvalue of 3. Thenkpoints are plotted at random coordinates. These points become the center points for each cluster, so they’re calledcentroids.

Each data point (in this case a flower) is assigned to its nearest centroid.

Each centroid is moved to the center of the data points assigned to it based on the mean distance between the points.

After the centroid is moved, the data points may now be closer to a different centroid, so the data points are reassigned to clusters based on the new closest centroid.

The centroid movement and cluster reallocation steps are repeated until the clusters become stable or a predetermined maximum number of iterations is reached.


Question 128

Since there’s no known label with which to compare the predicted cluster assignments, evaluation of a clustering model is based on how well the resulting clusters are … … … ….

Answer 128

separated from one another

Question 129

There are multiple metrics that you can use to evaluate cluster separation, including:


Answer 129

Average distance to cluster center: How close, on average, each point in the cluster is to the centroid of the cluster.

Average distance to other center: How close, on average, each point in the cluster is to the centroid of all other clusters.

Maximum distance to cluster center: The furthest distance between a point in the cluster and its centroid.

Silhouette: A value between -1 and 1 that summarizes the ratio of distance between points in the same cluster and points in different clusters (The closer to 1, the better the cluster separation).

Question 130

… …is an advanced form of machine learning that tries to emulate the way the human brain learns.

Answer 130

Deep learning

Question 131

The key to deep learning is the creation of an …… … that simulates electrochemical activity in biological neurons by using mathematical functions.

Answer 131

artificialneural network

Question 132

Artificial neural networks are made up of multiplelayersof neurons - essentially defining a … … …. This architecture is the reason the technique is referred to asdeep learningand the models produced by it are often referred to asdeep neural networks(DNNs). You can use deep neural networks for many kinds of machine learning problem, including regression and classification, as well as more specialized models for natural language processing and computer vision.

Answer 132

deeply nested function

Question 133

Just like other machine learning techniques discussed in this module, deep learning involves fitting training data to a function that can predict a label (y) based on the value of one or more features (x). The function (f(x)) is the … … of a nested function in which each layer of the neural network encapsulates functions that operate onxand the weight (w) values associated with them. The algorithm used to train the model involves iteratively feeding the … … (x) in the training data forward through the layers to calculate output values forŷ, validating the model to evaluate how far off the calculatedŷvalues are from the knownyvalues (which quantifies the level of error, orloss, in the model), and then modifying the weights (w) to reduce the loss. The trained model includes the final weight values that result in the most accurate predictions.

Answer 133

outer layer, feature values

Question 134

This is an example of a classification problem, in which the machine learning model must predict the most probable class to which an observation belongs. A classification model accomplishes this by predicting a label that consists of the probability for … ….

Answer 134

each class

In other words, y is a vector of three probability values; one for each of the possible classes:[P(y=0|x), P(y=1|x), P(y=2|x)].


Question 135

The process for inferencing a predicted penguin class using a deep learning network is:


Answer 135

The feature vector for a penguin observation is fed into the input layer of the neural network, which consists of a neuron for eachxvalue. In this example, the followingxvector is used as the input:[37.3, 16.8, 19.2, 30.0]

The functions for the first layer of neurons each calculate a weighted sum by combining thexvalue andwweight, and pass it to an activation function that determines if it meets the threshold to be passed on to the next layer.

Each neuron in a layer is connected to all of the neurons in the next layer (an architecture sometimes called afully connected network) so the results of each layer are fed forward through the network until they reach the output layer.

The output layer produces a vector of values; in this case, using asoftmaxor similar function to calculate the probability distribution for the three possible classes of penguin. In this example, the output vector is:[0.2, 0.7, 0.1]

The elements of the vector represent the probabilities for classes 0, 1, and 2. The second value is the highest, so the model predicts that the species of the penguin is1(Gentoo).


Question 136

Azure Machine Learning provides the following features and capabilities to support machine learning workloads:


Answer 136

Centralized storage and management of datasets for model training and evaluation.

On-demand compute resources on which you can run machine learning jobs, such as training a model.

Automated machine learning (AutoML), which makes it easy to run multiple training jobs with different algorithms and parameters to find the best model for your data.

Visual tools to define orchestratedpipelinesfor processes such as model training or inferencing.

Integration with common machine learning frameworks such as MLflow, which make it easier to manage model training, evaluation, and deployment at scale.

Built-in support for visualizing and evaluating metrics for responsible AI, including model explainability, fairness assessment, and others.


Question 137

The primary resource required for Azure Machine Learning is anAzure Machine Learning…

Answer 137

workspace

Question 138

Azure Machine Learning …; a browser-based portal for managing your machine learning resources and jobs.


Answer 138

studio

Question 139

In Azure Machine Learning studio, you can (among other things):


Answer 139

Import and explore data.
Create and use compute resources.
Run code in notebooks.
Use visual tools to create jobs and pipelines.
Use automated machine learning to train models.
View details of trained models, including evaluation metrics, responsible AI information, and training parameters.
Deploy trained models for on-request and batch inferencing.
Import and manage models from a comprehensive model catalog.


Question 140

… …imitates human behavior by relying on machines to learn and execute tasks without explicit directions on what to output.


Answer 140

Artificial Intelligence

Question 141

… …algorithms take in data like weather conditions and fit models to the data, to make predictions like how much money a store might make in a given day.


Answer 141

Machine learning

Question 142

… …models use layers of algorithms in the form of artificial neural networks to return results for more complex use cases. Many Azure AI services are built on deep learning models. You can check out this article to learn more about thedifference between machine learning and deep learning.

Answer 142

Deep learning

Question 143

… … models can produce new content based on what is described in the input. The OpenAI models are a collection of generative AI models that can produce language, code, and images.

Answer 143

Generative AI

Question 144

Generative AI includes:

Answer 144

Generating natural language
Generating code
Generating images

Question 145

OpenAI consists of four components:


Answer 145

Pre-trained generative AI models

Customization capabilities; the ability to fine-tune AI models with your own data

Built-in tools to detect and mitigate harmful use cases so users can implement AI responsibly

Enterprise-grade security with role-based access control (RBAC) and private networks


Question 146

OpenAI supports many common AI workloads and solves for some new ones.

Common AI workloads include machine learning, computer vision, natural language processing, conversational AI, anomaly detection, and knowledge mining.

Other AI workloads Azure OpenAI supports can be categorized by tasks they support, such as:


Answer 146

Generating Natural Language

Text completion: generate and edit text
Embeddings: search, classify, and compare text

Generating Code: generate, edit, and explain code

Generating Images: generate and edit images


Question 147

Azure AI services encompass all of what were previously known as … … and Azure Applied AI Services.


Answer 147

Cognitive Services

Question 148

Azure AI services are tools for solving AI …

Answer 148

workloads

Question 149

There are several overlapping capabilities between Azure AI Language service and Azure OpenAI Service, such as translation, … …, and keyword extraction

Answer 149

, sentiment analysis

Question 150

… is the process of optimizing a model’s performance) tuning.

Answer 150

Tuning

Question 151

Azure OpenAI Service may be more beneficial for use-cases that require highly customized… …, or for exploratory research

Answer 151

generative models

Question 152

When making business decisions about what type of model to use, it’s important to understand how time and compute needs factor into machine learning training. In order to produce an effective machine learning model, the model needs to be trained with a substantial amount of cleaned data. The ‘learning’ portion of training requires a computer to identify an algorithm that best fits the data. The complexity of the task the model needs to solve for and the desired level of model performance all factor into the … required to run through possible solutions for a best fit algorithm.

Answer 152

time

Question 153

… models that represent the latest generative models for natural language and code.

Answer 153

GPT-4

Question 154

… models that can generate natural language and code responses based on prompts.


Answer 154

GPT-3.5

Question 155

…models that convert text to numeric vectors for analysis - for example comparing sources of text for similarity.


Answer 155

Embeddings

Question 156

… models that generate images based on natural language descriptions

Answer 156

DALL-E

Question 157

… modelsalwayshave aprobabilityof reflecting true values. Higher performing models, such as models that have been fine-tuned for specific tasks, do a better job of returning responses that reflect true values. It is important to review the output of generative AI models.


Answer 157

Generative AI

Question 158

In the Azure OpenAI Studio, you can experiment with OpenAI models in …. In the… …, you can type in prompts, configure parameters, and see responses without having to code.


Answer 158

playgrounds, Completionsplayground

Question 159

In the …playground, you can use the assistant setup to instruct the model about how it should behave. The assistant will try to mimic the responses you include in tone, rules, and format you’ve defined in your system message.


Answer 159

Chat

Question 160

… …learning models are trained on words or chunks of characters known astokens. For example, the word “hamburger” gets broken up into the tokensham,bur, andger, while a short and common word like “pear” is a single token.

These tokens are mapped into vectors for a machine learning model to use for training. When a trained … … model takes in a user’s input, it also breaks down the input into tokens.

Answer 160

Natural language, natural language

Question 161

… … … models are excellent at both understanding and creating natural language.

Answer 161

Generative pre-trained transformer (GPT)

Question 162

GPT tries to infer, or guess, the context of the user’s question based on the…

Answer 162

prompt

Question 163

Natural language tasks include:


Answer 163

Task
Summarizing text
Classifying text
Generating names or phrases
Translation
Answering questions
Suggesting content

Question 164

… models have been trained on both natural language and billions of lines of code from public repositories.

Answer 164

GPT

Question 165

What’s unique about the … model family is that it’s more capable across more languages than GPT models.

Answer 165

Codex

Question 166

… can also summarize functions that are already written, explain SQL queries or tables, and convert a function from one programming language into another.

Answer 166

GPT

Question 167

OpenAI Codex is:

Answer 167

OpenAI Codex is an artificial intelligence model developed by OpenAI. It parses natural language and generates code in response. It powers GitHub Copilot, a programming autocompletion tool for select IDEs, like Visual Studio Code and Neovim.

The main difference between CodeX and ChatGPT is that CodeX focuses on code generation, while ChatGPT is designed for conversational text generation. When analyzing their computational performance, we can see that CodeX is significantly faster than ChatGPT when performing code generation. Both are owned by OpenAI.

Question 168

GitHub … integrates the power of OpenAI Codex into a plugin for developer environments like Visual Studio Code.


Answer 168

Copilot

Question 169

In addition to natural language capabilities, generative AI models can edit and create images. The model that works with images is called ….

Answer 169

DALL-E

Question 170

Image capabilities generally fall into the three categories of:

Answer 170

image creation, editing an image, and creating variations of an image.


Question 171

DALL-E can edit the image as requested by changing its style, adding or removing items, or generating new content to add. Edits are made by uploading the original image and specifying a transparent … that indicates what area of the image to edit

Answer 171

Mask

Question 172

… … … are AI capabilities that can be built into web or mobile applications, in a way that’s straightforward to implement.

Answer 172

Azure AI services

Question 173

The Azure AI … … service can be used to detect harmful content within text or images, including violent or hateful content, and report on its severity.

Answer 173

Content Safety

Question 174

The Azure AI … service can be used to summarize text, classify information, or extract key phrases.

Answer 174

Language

Question 175

Azure AI … service provides powerful speech to text and text to speech capabilities, allowing speech to be accurately transcribed into text, or text to natural sounding voice audio.

Answer 175

Speech

Question 176

Azure AI services are based on three principles that dramatically improve speed-to-market:


Answer 176

Prebuilt and ready to use
Accessed through APIs
Available on Azure


Question 177

Developers can access AI services through … …, client libraries, or integrate them with tools such as Logic Apps and Power Automate.

Answer 177

REST APIs

Question 178

AI Services are managed in the same way as other Azure services, such as platform as a service (PaaS), infrastructure as a service (IaaS), or a … … service

Answer 178

managed database

Question 179

The Azure platform and … … provide a consistent framework for all your Azure services, from creating or deleting resources, to availability and billing.

Answer 179

Resource Manager

Question 180

There are two types of AI service resources … or …

Answer 180

multi-service or single-service.

Question 181

… resource: a resource created in the Azure portal that provides access to multiple Azure AI services with a single key and endpoint. Use the resourceAzure AI serviceswhen you need several AI services or are exploring AI capabilities. When you use an Azure AI services resource, all your AI services are billed together.

Answer 181

Multi-service

Question 182

… resources: a resource created in the Azure portal that provides access to a single Azure AI service, such as Speech, Vision, Language, etc. Each Azure AI service has a unique key and endpoint. These resources might be used when you only require one AI service or want to see cost information separately.

Answer 182

Single-service

Question 183

To create an Azure AI services resource, sign in to theAzure portalwith … access and selectCreate a resource.

Answer 183

Contributor

Question 184

Once you create an Azure AI service resource, you can build applications using the … …, software development kits (SDKs), or visual studio interfaces.

Answer 184

REST API

Question 185

There are different studios for different Azure AI services, such as … …, Language Studio, Speech Studio, and the Content Safety Studio.

Answer 185

Vision Studio

Question 186

Before you can use an AI service resource, you must associate it with the … you want to use on the Settings page. Select the resource, and then selectUse Resource.

Answer 186

studio

Question 187

Most Azure AI services are accessed through a … …, although there are other ways. The API defines what information is passed between two software components: the Azure AI service and whatever is using it.

Answer 187

RESTful API

Question 188

Part of what an … does is to handle authentication. Whenever a request is made to use an AI services resource, that request must be authenticated. For example, your subscription and AI service resource is verified to ensure you have sufficient permissions to access it. This authentication process uses an endpoint and a resource key.

Answer 188

API

Question 189

The …. … protects the privacy of your resource.

Answer 189

resource key

Question 190

When you write code to access the AI service, the keys and endpoint must be included in the… ….

Answer 190

authentication header

Question 191

… … is a technique that uses mathematics and statistics to create a model that can predict unknown values.

Answer 191

Machine learning

Question 192

Mathematically, you can think of machine learning as a way of defining a … (let’s call itf) that operates on one or more,,,of something (which we’ll callx) to calculate a predicted…(y) - like this:

f(x) = y

Answer 192

function, features, label

Question 193

The specific operation that theffunction performs onxto calculateydepends on a number of factors, including the type of … you’re trying to create and the specific algorithm used to train the model.

Answer 193

model

Question 194

The… ,,, ,,,approach requires you to start with a datasetwithknown label values. Two types of supervised machine learning tasks include regression and classification.

Answer 194

supervised machine learning

Question 195

… is used to predict a continuous value; like a price, a sales total, or some other measure.


Answer 195

Regression

Question 196

… is used to determine a class label; an example of a binary class label is whether a patient has diabetes or not; an example of multi-class labels is classifying text as positive, negative, or neutral.

Answer 196

Classification

Question 197

The… machine learningapproach starts with a datasetwithoutknown label values. One type of unsupervised machine learning task is clustering.

Answer 197

unsupervised

Question 198

… is used to determine labels by grouping similar information into label groups; like grouping measurements from birds into species.

Answer 198

Clustering

Question 199

To use Azure Machine Learning, you first create a…resource in your Azure subscription.

Answer 199

workspace

Question 200

You can then use this workspace to manage data, code, … , and other artifacts related to your machine learning workloads.

Answer 200

models

Question 201

After you have created an Azure Machine Learning workspace, you can develop solutions with the Azure Machine Learning service either with developer tools or the Azure Machine Learning studio … …

Answer 201

web portal.

Question 202

Azure Machine Learning … is a web portal for machine learning solutions in Azure.

Answer 202

studio

Question 203

Azure Machine Learning includes anautomated machine learningcapability that automatically tries multiple pre-processing techniques and model-training algorithms in …. 

These automated capabilities use the power of cloud … to find the best performing supervised machine learning model for your data.

Answer 203

parallel, compute

Question 204

Automated machine learning allows you to train models without extensive data science or programming knowledge. For people with a data science and programming background, it provides a way to save time and resources by automating algorithm selection and … tuning.

Answer 204

hyperparameter

Question 205

In Azure Machine Learning, operations that you run are called …. You can configure multiple settings for your job before starting an automated machine learning ….

Answer 205

jobs, run

Question 206

The run configuration provides the information needed to specify your training … and Azure Machine Learning environment in your run … and run a training job.

Answer 206

script, configuration

Question 207

You can think of the steps in a machine learning process as:

Answer 207

Prepare data:
Train model:
Evaluate performance:
Deploy a predictive service:

Question 208

… …: Identify the features and label in a dataset. Pre-process, or clean and transform, the data as needed.


Answer 208

Prepare data:

Question 209

… …: Split the data into two groups, a training and a validation set. Train a machine learning model using the training data set. Test the machine learning model for performance using the validation data set.


Answer 209

Train model:

Question 210

… …: Compare how close the model’s predictions are to the known labels.


Answer 210

Evaluate performance:

Question 211

Deploy a … …: After you train a machine learning model, you can deploy the model as an application on a server or device so that others can use it.


Answer 211

predictive service:

Question 212

In Azure Machine Learning, data for model training and other operations is usually encapsulated in an object called a… ….

Answer 212

data asset.

Question 213

The automated machine learning capability in Azure Machine Learning supportssupervisedmachine learning models - in other words, models for which the training data includes known label values. You can use automated machine learning to train models for:


Answer 213

Classification(predicting categories orclasses)
Regression(predicting numeric values)
Time series forecasting(predicting numeric values at a future point in time)


Question 214

In Automated Machine Learning, you can select configurations for the primary metric, type of model used for training, exit criteria, and … ….

Answer 214

concurrency limits

Question 215

Additional Machine Learning configuration settings include:

Answer 215

Primary metric
Explain best model
Use all supported models
Blocked models
Training Job Time
Metric score threshold
Max concurrent iterations

Question 216

… will split data into a training set and a validation set.

Answer 216

AutoML

Question 217

The best model is identified based on the … metric you specified,

Answer 217

evaluation

Question 218

If you used … … to stop the job. Thus the “best” model the job generated might not be the best possible model, just the best one found within the time allowed for this exercise.


Answer 218

exit criteria

Question 219

A technique called…is used to calculate the evaluation metric. After the model is trained using a portion of the data, the remaining portion is used to iteratively test, or …, the trained model. The metric is calculated by comparing the predicted value from the test with the actual known value, or label.


Answer 219

cross-validation, cross-validate

Question 220

The difference between the predicted and actual value, known as the… , indicates the amount oferrorin the model.

Answer 220

residuals

Question 221

The performance metric… … … …(RMSE), is calculated by squaring the errors across all of the test cases, finding the mean of these squares, and then taking the square root.

Answer 221

root mean squared error

Question 222

With root mean squared error, the … this value is, the more accurate the model’s predictions.

Answer 222

smaller

Question 223

The… root mean squared error(NRMSE) standardizes the RMSE metric so it can be used for comparison between models which have variables on different scales.


Answer 223

normalized

Question 224

The… …shows the frequency of residual value ranges. Residuals represent variance between predicted and true values that can’t be explained by the model, in other words, errors. You should hope to see the most frequently occurring residual values clustered around zero. You want small errors with fewer errors at the extreme ends of the scale.


Answer 224

Residual Histogram

Question 225

The… vs. …chart should show a diagonal trend in which the predicted value correlates closely to the true value. The dotted line shows how a perfect model should perform. The closer the line of your model’s average predicted value is to the dotted line, the better its performance. A histogram below the line chart shows the distribution of true values.


Answer 225

Predicted, True

Question 226

In Azure Machine Learning, you can deploy a service as an … … … (ACI) or to an … … … (AKS) cluster.

Answer 226

Azure Container Instances, Azure Kubernetes Service

Question 227

For production scenarios, an … deployment is recommended, for which you must create an…. …. … ….

Answer 227

AKS, inference clustercompute target

Question 228

For testing you can use an ACI service, which is a suitable deployment target for testing, and does not require you to create an inference cluster.

Answer 228

ACI

Question 229

Regression is a supervised machine learning technique used to predict numeric values. Learn how to create regression models using Azure … ,… ….

Answer 229

Machine Learning designer

Question 230

… predicts a numericlabelor outcome based on variables, orfeatures

Answer 230

Regression

Question 231

Regression is an example of a …machine learning technique in which you train a model using data that includes both the features and known values for the label, so that the model learns tofitthe feature combinations to the label. Then, after training has been completed, you can use the trained model to predict labels for new items for which the label is unknown.

Answer 231

supervised

Question 232

A few scenarios of … machine learning are:

Using characteristics of houses, such as square footage and number of rooms, to predict home prices.

Using characteristics of farm conditions, such as weather and soil quality, to predict crop yield.

Using characteristics of a past campaign, such as advertising logs, to predict future advertisement clicks.

Answer 232

Regression

Question 233

At its core, Azure Machine Learning is a service for training and managing machine learning models, for which you need … resources on which to run the training process.

Answer 233

compute

Question 234

Compute targets are … resources on which you can run model training and data exploration processes.

Answer 234

cloud-based

Question 235

You can manage the compute targets for your data science activities in Azure … … studio.

Answer 235

Machine Learning

Question 236

There are four kinds of compute resource you can create:

Answer 236

Compute Instances:
Compute Clusters:
Kubernetes Clusters:
Attached Compute:

Question 237

… …: Development workstations that data scientists can use to work with data and models.


Answer 237

Compute Instances:

Question 238

… … : Scalable clusters of virtual machines for on-demand processing of experiment code.


Answer 238

Compute clusters

Question 239

… … : Deployment targets for predictive services that use your trained models. You can access previous versions of “inference clusters” here.


Answer 239

Kubernetes clusters:

Kubernetes, also known as K8s, is an open-source system for automating deployment, scaling, and management of containerized applications.

It groups containers that make up an application into logical units for easy management and discovery. Kubernetes builds upon 15 years of experience of running production workloads at Google, combined with best-of-breed ideas and practices from the community.

Question 240

… … : Links to existing Azure compute resources, such as Virtual Machines or Azure Databricks clusters.

Answer 240

Attached compute:

Question 241

In Azure Machine Learning studio, there are several ways to author regression machine learning models. One way is to use a visual interface called …that you can use to train, test, and deploy machine learning models. The drag-and-drop interface makes use of clearly defined inputs and outputs that can be shared, reused, and version controlled.

Answer 241

designer

Question 242

Eachdesignerproject, known as a … , has a left panel for navigation and a canvas on your right hand side. To usedesigner, identify the building blocks, or components, needed for your model, place and connect them on your canvas, and run a machine learning job.

Answer 242

pipeline

Question 243

Pipelines let you organize, manage, and reuse complex machine learning workflows across projects and users. A pipeline starts with the … from which you want to train the model.

Answer 243

dataset

Question 244

Each time you run a pipeline, the configuration of the pipeline and its results are stored in your workspace as a … ….

Answer 244

pipeline job

Question 245

An Azure Machine Learning … encapsulates one step in a machine learning pipeline.

Answer 245

component

Question 246

You can think of a component as a … … and as a building block for Azure Machine Learning pipelines.

Answer 246

programming function

Question 247

In a pipeline project, you can access data assets and components from the left panel’s … …tab

Answer 247

Asset Library

Question 248

You can create data assets on theDatapage from local files, a datastore, … …, and Open Datasets.

Answer 248

web files

Question 249

Machine Learning data assets will appear along with standard sample datasets indesigner’s… Library.

Answer 249

Asset

Question 250

An Azure Machine Learning (ML) job executes a task against a specified … target.

Answer 250

compute

Question 251

Once a job is created, Azure ML maintains a … … for the job.

Answer 251

run record

Question 252

In your designer project, you can access the status of a pipeline job using the … …tab on the left pane.

Answer 252

Submitted jobs

Question 253

The steps to train and evaluate a regression machine learning model:

Answer 253

Prepare data:
Train model:
Evaluate performance:
Deploy a predictive service:

Question 254

… … : Identify the features and label in a dataset. Pre-process, or clean and transform, the data as needed.


Answer 254

Prepare data:

Question 255

… … : Split the data into two groups, a training and a validation set. Train a machine learning model using the training data set. Test the machine learning model for performance using the validation data set.


Answer 255

Train model:

Question 256

… … : Compare how close the model’s predictions are to the known labels.


Answer 256

Evaluate Performance:

Question 257

: After you train a machine learning model, you need to convert the training pipeline into a real-time inference pipeline. Then you can deploy the model as an application on a server or device so that others can use it.


Answer 257

Deploy a predictive service:

Question 258

Azure Machine Learning designer has several pre-built components that can be used to prepare data for training. These components enable you to clean data, … … , join tables, and more.

Answer 258

normalize features

Question 259

To train a regression model, you need a dataset that includes historicalfeatures, characteristics of the entity for which you want to make a prediction, and known… ….

Answer 259

labelvalues

Question 260

The … is the quantity you want to train a model to predict.

Answer 260

Label

Question 261

It’s common practice to train the model using a subset of the data, while holding back some data with which to … the trained model. This enables you to compare the labels that the model predicts with the actual known labels in the original dataset.

Answer 261

test

Question 262

You will usedesigner’s… …component to generate the predicted class label value.

Answer 262

Score Model

Question 263

Once you connect all the … , you will want to run an experiment, which will use the data asset on the canvas to train and score a model.

Answer 263

components

Question 264

There are many performance metrics and methodologies for evaluating how well a model makes predictions. You can review evaluation metrics on the completed job page by right-clicking on the… …component.

Answer 264

Evaluate model

Question 265

… … … : The average difference between predicted values and true values. This value is based on the same units as the label, in this case dollars. The lower this value is, the better the model is predicting.


Answer 265

Mean Absolute Error (MAE)

Question 266

… … … … : The square root of the mean squared difference between predicted and true values. The result is a metric based on the same unit as the label (dollars). When compared to the MAE (above), a larger difference indicates greater variance in the individual errors (for example, with some errors being very small, while others are large).


Answer 266

Root mean squared error (RMSE):

Question 267

… … … : A relative metric between 0 and 1 based on the square of the differences between predicted and true values. The closer to 0 this metric is, the better the model is performing. Because this metric is relative, it can be used to compare models where the labels are in different units.


Answer 267

Relative squared error (RSE):

Question 268

… … … : A relative metric between 0 and 1 based on the absolute differences between predicted and true values. The closer to 0 this metric is, the better the model is performing. Like RSE, this metric can be used to compare models where the labels are in different units.


Answer 268

Relative absolute error (RAE):

Question 269

.: This metric is more commonly referred to asR-Squared, and summarizes how much of the variance between predicted and true values is explained by the model. The closer to 1 this value is, the better the model is performing.

Answer 269

Coefficient of determination (R2):

Question 270

To … your pipeline, you must first convert the training pipeline into a real-time inference pipeline. This process removes training components and adds web service inputs and outputs to handle requests.

Answer 270

Deploy

Question 271

The inference pipeline performs the same data transformations as the first pipeline fornew… .

Answer 271

Data

Question 272

Once an … … performs its data transformations, it uses the trained model to… , or predict, label values based on its features. This model will form the basis for a predictive service that you can publish for applications to use.

Answer 272

Inference pipeline, infer

Question 273

You can create an inference pipeline by selecting the menu above a … job.

Answer 273

completed

Question 274

After creating the inference pipeline, you can deploy it as an ….

Answer 274

endpoint

Question 275

In the … page, you can view deployment details, test your pipeline service with sample data, and find credentials to connect your pipeline service to a client application.

Answer 275

endpoints

Question 276

The Deployment state on theDetailstab will indicate…when deployment is successful.

Answer 276

Healthy

Question 277

On the…tab, you can test your deployed service with sample data in a JSON format. The test tab is a tool you can use to quickly check to see if your model is behaving as expected. Typically it is helpful to test the service before connecting it to an application.

Answer 277

Test

Question 278

You can find credentials for your service on the…tab. These credentials are used to connect your trained machine learning model as a service to a client application.

Answer 278

Consume

Question 279

Classification is a … machine learning technique used to predict categories orclasses. Learn how to create classification models using Azure Machine Learning designer.

Answer 279

supervised

Question 280

Classification is an example of asupervisedmachine learning technique in which you train a model using data that includes both the … and known … for the label, so that the model learns tofitthe feature combinations to the label.

Answer 280

features, values

Question 281

With a classification model, after training has been completed, you can use the trained model to predict … for new items for which the … is unknown.

Answer 281

labels, label

Question 282

Classification models can be applied to … and multi-class scenarios

Answer 282

binary

Question 283

Classification is an example of a,,,machine learning technique in which you train a model using data that includes both the features and known values for the label, so that the model learns tofitthe feature combinations to the label. Then, after training has been completed, you can use the trained model to predict labels for new items for which the label is unknown.

Answer 283

supervised

Question 284

Using historical data to predict whether text sentiment is positive, negative, or neutral is an example of a … classification model..


Answer 284

Multi-class

Question 285

Using characteristics of small businesses to predict if a new venture will succeed is an example of a … classification model.

Answer 285

Binary

Question 286

You can think of the steps to train and evaluate a classification machine learning model as:


Answer 286

Prepare data: Identify the features and label in a dataset. Pre-process, or clean and transform, the data as needed.

Train model: Split the data into two groups, a training and a validation set. Train a machine learning model using the training data set. Test the machine learning model for performance using the validation data set.

Evaluate performance: Compare how close the model’s predictions are to the known labels.

Deploy a predictive service: After you train a machine learning model, you need to convert the training pipeline into a real-time inference pipeline. Then you can deploy the model as an application on a server or device so that others can use it.


Question 287

You’ll usedesigner’s… Modelcomponent to generate the predicted class label value. Once you connect all the components, you’ll want to run an experiment, which will use the … … on the canvas to train and … a model.


Answer 287

Score, data asset, score

Question 288

You can review evaluation metrics on the completed job page by right-clicking on the… modelcomponent.


Answer 288

Evaluate

Question 289

The … … is a tool used to assess the quality of a classification model’s predictions. It compares predicted labels against actual labels.


Answer 289

confusion matrix

Question 290

In a binary classification model where you’re predicting one of two possible values, the confusion matrix is a 2x2 grid showing the predicted and actual value counts for classes1and0. It categorizes the model’s results into four types of outcomes. Using our diabetes example these outcomes can look like:


Answer 290

True Positive:
False Positive:
False Negative:
True Negative:

Question 291

… … : The model predicts the patient has diabetes, and the patient does actually have diabetes.


Answer 291

True positive:

Question 292

… … : The model predicts the patient has diabetes, but the patient doesn’t actually have diabetes.


Answer 292

False positive:

Question 293

… … : The model predicts the patient doesn’t have diabetes, but the patient actually does have diabetes.


Answer 293

False negative

Question 294

… … : The model predicts the patient doesn’t have diabetes, and the patient actually doesn’t have diabetes.


Answer 294

True negative:

Question 295

Suppose you have data for 100 patients. You create a model that predicts a patient does have diabetes 15% of the time, so itpredicts15 people have diabetes andpredicts85 people do not have diabetes. In actuality, suppose 25 peopleactuallydo have diabetes and 75 peopleactuallydo not have diabetes. This information can be presented in a confusion matrix such as the one below:


Answer 295

1. 0
   Actually True. Actually False
   ——————————————————-
   1. | | |
   Predicted True. | | |
   | 10 | 5 |
   | | |
   ——————————————————
   | | |
   0. | | |
   Predicted False. | 15 | 70 |
   | | |
   ——————————————————-

Question 296

For a multi-class classification model (where there are more than two possible classes), the same approach is used to tabulate each possible combination of actual and predicted value counts - so a model with three possible classes would result in a … matrix with a diagonal line of cells where the predicted and actual labels match.

Answer 296

3x3

Question 297

Metrics that can be derived from the confusion matrix include:


Answer 297

Accuracy:
Precision:
Recall:
F1 Score:

Question 298

… : The number of correct predictions (true positives + true negatives) divided by the total number of predictions.


Answer 298

Accuracy

Question 299

… : The number of the cases classified as positive that are actually positive: the number of true positives divided by (the number of true positives plus false positives).


Answer 299

Precision:

Question 300

… : The fraction of positive cases correctly identified: the number of true positives divided by (the number of true positives plus false negatives).


Answer 300

Recall:

Question 301

… … : An overall metric that essentially combines precision and recall.


Answer 301

F1 score:

Question 302

Theaccuracyof the model in the example is:

Answer 302

(10+70) / 100 = 80%

Question 303

Theprecisionof the model in our example is:

Answer 303

10 / (10 + 5) = 67%

Question 304

Therecallof the model in our example is

Answer 304

10 / (10 + 15) = 40%

Question 305

In the case of a … classification model, the predicted probability is a value between 0 and 1. By default, a predicted probabilityincluding or above0.5 results in a class prediction of 1, while a predictionbelowthis threshold means that there’s a greater probability of anegativeprediction (remember that the probabilities for all classes add up to 1), so the predicted class would be 0.

Answer 305

Binary

Question 306

Designer has a useful … sliderfor reviewing how the model performance would change depending on the set threshold.

Answer 306

threshold

Question 307

Another term for…isTrue positive rate, and it has a corresponding metric named… … rate, which measures the number of negative cases incorrectly identified as positive compared between the number of actual negative cases.

Answer 307

recall, False positive

Question 308

Plotting these metrics against each other for every possible binary threshold value between 0 and 1 results in a curve, known as the… curve, which stands for… … … , but most data scientists just call it a … curve).

Answer 308

ROC, receiver operating characteristic, ROC

Question 309

In an ideal model, the curve would go all the way up the left side and across the top, so that it covers the full area of the chart. The larger thearea under the curve, ofAUCmetric, (which can be any value from 0 to 1), the … the model is performing. You can review the ROC curve inEvaluation Results.

Answer 309

better

Question 310

… is a form of machine learning that is used to group similar items into clusters based on their features.

Answer 310

Clustering

Question 311

Clustering is an example of… machine learning, in which you train a model to separate items into clusters based purely on their characteristics, orfeatures. There is no previously known cluster value (orlabel) from which to train the mod

Answer 311

unsupervised

Question 312

Clustering machine learning models can be built using Azure … Learning

Answer 312

Machine

Question 313

Like supervised models, you can think of the steps to train and evaluate a clustering machine learning model as:


Answer 313

Prepare data: Identify the features in a dataset. Pre-process, or clean and transform, the data as needed.

Train model: Split the data into two groups, a training and a validation set. Train a machine learning model using the training data set. Test the machine learning model for performance using the validation data set.

Evaluate performance: These metrics can help data scientists assess how well the model separates the clusters.

Deploy a predictive service: After you train a machine learning model, you need to convert the training pipeline into a real-time inference pipeline. Then you can deploy the model as an application on a server or device so that others can use it.


Question 314

To train a clustering model, you need a dataset that includes multiple observations of the items you want to cluster, including … features that can be used to determine similarities between individual cases that will help separate them into clusters

Answer 314

numeric

Question 315

To train a clustering model, you need to apply a clustering algorithm to the data, using only the features that you have selected for clustering. You’ll train the model with a subset of the data, and use the rest to test the trained model.

The… ….algorithm groups items into the number of clusters, or centroids, you specify - a value referred to as
…

Answer 315

K-Means Clustering, K

Question 316

The K-Means algorithm works by:


Answer 316

InitializingKcoordinates as randomly selected points calledcentroidsinn-dimensional space (wherenis the number of dimensions in the feature vectors).

Plotting the feature vectors as points in the same space, and assigning each point to its closest centroid.

Moving the centroids to the middle of the points allocated to it (based on themeandistance).

Reassigning the points to their closest centroid after the move.

Repeating steps 3 and 4 until the cluster allocations stabilize or the specified number of iterations has completed.


Question 317

You will usedesigner’s… ,,, …… component to group the data into clusters. Once you connect all the components, you will want to run an experiment, which will use the data asset on the canvas to train a model.


Answer 317

Assign Data to Clusters

Question 318

You can review evaluation metrics on the completed job page by right-clicking on the …modelcomponent.

Answer 318

Evaluate

Question 319

When the experiment run has finished, selectJob details. Right click on the … Modelmodule and selectPreview data, then selectEvaluation results.

Answer 319

Evaluate

Question 320

These metrics can help data scientists assess how well the model separates the clusters. They include a row of metrics for each cluster, and a summary row for a combined evaluation. The metrics in each row are:


Answer 320

Average Distance to Other Center:
Average Distance to Cluster Center:
Number of Points:.
Maximal Distance to Cluster Center:

Question 321

… …. … … : This indicates how close, on average, each point in the cluster is to the centroids of all other clusters.


Answer 321

Average Distance to Other Center

Question 322

… … … … … : This indicates how close, on average, each point in the cluster is to the centroid of the cluster.


Answer 322

Average Distance to Cluster Center

Question 323

… … … : The number of points assigned to the cluster.


Answer 323

Number of Points

Question 324

… … … … … : The maximum of the distances between each point and the centroid of that point’s cluster. If this number is high, the cluster may be widely dispersed. This statistic in combination with theAverage Distance to Cluster Centerhelps you determine the cluster’sspread.


Answer 324

Maximal Distance to Cluster Center

Question 325

… … is an area of artificial intelligence (AI) in which software systems are designed to perceive the world visually, through cameras, images, and video. There are multiple specific types of computer vision problem that AI engineers and data scientists can solve using a mix of custom machine learning models and platform-as-a-service (PaaS) solutions - including many AI services in Microsoft Azure.

Answer 325

Computer vision

Question 326

To a computer, an image is an … of numericpixelvalues.

Answer 326

array

Question 327

In reality, most digital images are … and consist of … layers (known aschannels) that represent red, green, and blue (RGB) color hues.

Answer 327

multidimensional, three

Question 328

A common way to perform image processing tasks is to apply…that modify the pixel values of the image to create a visual effect.

Answer 328

filters

Question 329

A filter is defined by one or more arrays of pixel values, called … ….

For example, you could define filter with a 3x3 kernel as shown in this example:

-1 -1 -1
-1 8 -1
-1 -1 -1

The kernel is thenconvolvedacross the image, calculating a weighted sum for each 3.3 patch of pixels and assigning the result to a new image. It’s easier to understand how the filtering works by exploring a step-by-step example.

Answer 329

filterkernels

Question 330

Let’s start with the … image we explored previously:

0 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 255 255 255 0 0
0 0 255 255 255 0 0
0 0 255 255 255 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 0

First, we apply the filter kernel to the top left patch of the image, multiplying each pixel value by the corresponding weight value in the kernel and adding the results:

(0 x -1) + (0 x -1) + (0 x -1) +
(0 x -1) + (0 x 8) + (0 x -1) +
(0 x -1) + (0 x -1) + (255 x -1) = -255

The result (-255) becomes the first value in a new array. Then we move the filter kernel along one pixel to the right and repeat the operation:

(0 x -1) + (0 x -1) + (0 x -1) +
(0 x -1) + (0 x 8) + (0 x -1) +
(0 x -1) + (255 x -1) + (255 x -1) = -510

Again, the result is added to the new array, which now contains two values:

-255 -510

The process is repeated until the filter has been convolved across the entire image

Answer 330

grayscale

Question 331

The filter is convolved across the image, calculating a new array of values. Some of the values might be outside of the 0 to 255 pixel value range, so the values are adjusted to fit into that range.

Because of the shape of the filter, the outside edge of pixels isn’t calculated, so a padding value (usually 0) is applied. The resulting array represents a new image in which the filter has transformed the original image. In this case, the filter has had the effect of highlighting theedgesof shapes in the image.

To see the effect of the filter more clearly, here’s an example of the same filter applied to a real image:

Because the filter is convolved across the image, this kind of image manipulation is often referred to asconvolutional filtering.

The filter used in this example is a particular type of filter (called alaplacefilter) that highlights the edges on objects in an image. There are many other kinds of filter that you can use to create blurring, sharpening, color inversion, and other effects.

Question 332

the goal of computer vision is often to … …, or at least actionable insights, from images; which requires the creation of machine learning models that are trained to recognize features based on large volumes of existing images.

Answer 332

extract meaning

Question 333

One of the most common machine learning model architectures for computer vision is a… … …(CNN).

Answer 333

convolutional neural network

Question 334

CNNs use … to extract numeric feature maps from images, and then feed the feature values into a … … …to generate a label prediction. For example, in animage classificationscenario, the label represents the main subject of the image (in other words, what is this an image of?). You might train a CNN model with images of different kinds of fruit (such as apple, banana, and orange) so that the label that is predicted is the type of fruit in a given image.

Answer 334

filters, deep learning model

Question 335

During thetrainingprocess for a CNN, filter kernels are initially defined using randomly generated … …. Then, as the training process progresses, the models predictions are evaluated against known label values, and the filter weights are adjusted to improve accuracy. Eventually, the trained fruit image classification model uses the filter weights that best extract features that help identify different kinds of fruit.

Answer 335

weight values

Question 336

how a CNN for an image classification model works:

Answer 336

Images with known labels (for example, 0: apple, 1: banana, or 2: orange) are fed into the network to train the model.

One or more layers of filters is used to extract features from each image as it is fed through the network. The filter kernels start with randomly assigned weights and generate arrays of numeric values calledfeature maps.

The feature maps are flattened into a single dimensional array of feature values.

The feature values are fed into a fully connected neural network.

The output layer of the neural network uses asoftmaxor similar function to produce a result that contains a probability value for each possible class, for example [0.2, 0.5, 0.3].

During training the output probabilities are compared to the actual class label - for example, an image of a banana (class 1) should have the value [0.0, 1.0, 0.0]. The difference between the predicted and actual class scores is used to calculate thelossin the model, and the weights in the fully connected neural network and the filter kernels in the feature extraction layers are modified to reduce the loss.

The training process repeats over multipleepochsuntil an optimal set of weights has been learned. Then, the weights are saved and the model can be used to predict labels for new images for which the label is unknown.


Question 337

CNN architectures usually include multiple convolutional filter layers and additional layers to … the size of feature maps, constrain the extracted values, and otherwise manipulate the feature values. These layers have been omitted in this simplified example to focus on the key concept, which is that filters are used to extract numeric features from images, which are then used in a neural network to predict image labels.


Answer 337

reduce

Question 338

… have been at the core of computer vision solutions for many years. While they’re commonly used to solve image classification problems as described previously, they’re also the basis for more complex computer vision models. For example,object detectionmodels combine CNN feature extraction layers with the identification ofregions of interestin images to locate multiple classes of object in the same image.



Answer 338

CNNs

Question 339

In another AI discipline -… … …, another type of neural network architecture, called atransformerhas enabled the development of sophisticated models for language.

Answer 339

natural language processing(NLP)

Question 340

Transformers work by processing huge volumes of data, and encoding language…(representing individual words or phrases) as vector-basedembeddings(arrays of numeric values).

Answer 340

tokens

Question 341

… work by processing huge volumes of data, and encoding languagetokens(representing individual words or phrases) as vector-basedembeddings(arrays of numeric values).

Answer 341

Transformers

Question 342

You can think of an embedding as representing a set of dimensions that each represent some … … of the token. The embeddings are created such that tokens that are commonly used in the same context are closer together dimensionally than unrelated words.

Answer 342

semantic attribute

Question 343

Words are encoded as multi-dimensional, and plotted in a 3D space. Tokens that are semantically similar are encoded in similar positions, creating a semantic language model that makes it possible to build sophisticated NLP solutions for text analysis, translation, language generation, and other tasks.

Encoders in transformer networks create vectors with many more dimensions, defining complex semantic relationships between tokens based on linear algebraic calculations. The math involved is complex, as is the architecture of a transformer model. Our goal here is just to provide aconceptualunderstanding of how encoding creates a model that encapsulates relationships between entities.

Answer 343

vectors

Question 344

The Microsoft…model is a multi-modal model. Trained with huge volumes of captioned images from the Internet, it includes both a language encoder and an image encoder. Florence is an example of afoundationmodel. In other words, a pre-trained general model on which you can build multipleadaptivemodels for specialist tasks.

Answer 344

Florence

Question 345

The success of transformers as a way to build language models has led AI researchers to consider whether the same approach would be effective for image data. The result is the development of…models, in which the model is trained using a large volume of captioned images, with no fixedlabels. An image encoder extracts features from images based on pixel values and combines them with text embeddings created by a language encoder. The overall model encapsulates relationships between natural language token embeddings and image features, as shown here:

Answer 345

multi-modal

Question 346

You can use Florence as a foundation model for adaptive models that perform:

Answer 346

Image classification: Identifying to which category an image belongs.

Object detection: Locating individual objects within an image.

Captioning: Generating appropriate descriptions of images.

Tagging: Compiling a list of relevant text tags for an image.


Question 347

The architecture for computer vision models can be complex; and you require significant volumes of training ,,, and compute power to perform the training process.

Answer 347

images

Question 348

Microsoft’s Azure AI Vision service provides prebuilt and customizable computer vision models that are based on the … foundation model and provide various powerful capabilities.

Answer 348

Florence

Question 349

To use Azure AI Vision, you need to create a resource for it in your Azure subscription. You can use either of the following resource types:


Answer 349

Azure AI Vision: A specific resource for the Azure AI Vision service. Use this resource type if you don’t intend to use any other Azure AI services, or if you want to track utilization and costs for your Azure AI Vision resource separately.

Azure AI services: A general resource that includes Azure AI Vision along with many other Azure AI services; such as Azure AI Language, Azure AI Custom Vision, Azure AI Translator, and others. Use this resource type if you plan to use multiple AI services and want to simplify administration and development.

Question 350

After you’ve created a suitable resource in your subscription, you can submit images to the Azure AI Vision service to perform a wide range of analytical tasks.

Azure AI Vision supports multiple … … capabilities, including:

Optical character recognition (OCR) - extracting text from images.

Generating captions and descriptions of images.

Detection of thousands of common objects in images.

Tagging visual features in images

These tasks, and more, can be performed inAzure AI Vision Studio.

Answer 350

image analysis

Question 351

Azure AI Vision service can use … … … capabilities to detect text in images. For example, consider the following image of a nutrition label on a product in a grocery store:


Answer 351

optical character recognition (OCR)

Question 352

Azure AI Vision has the ability to analyze an image, evaluate the objects that are detected, and generate a … … … … … that can describe what was detected in the image. For example, consider the following image

Answer 352

human-readable phrase or sentence

Question 353

Azure AI Vision can identify thousands of … … in images.

Answer 353

common objects

Question 354

Predictions include a… … that indicates the probability the model has calculated for the predicted objects.
In addition to the detected object labels and their probabilities, Azure AI Vision returnsbounding boxcoordinates that indicate the top, left, width, and height of the object detected. You can use these coordinates to determine where in the image each object was detected.

Answer 354

confidence score

Question 355

Azure AI Vision can suggest…for an image based on its contents. These … can be associated with the image as metadata that summarizes attributes of the image and can be useful if you want to index an image along with a set of key terms that might be used to search for images with specific attributes or contents.

Answer 355

tags, tags

Question 356

Azure AI Vision builds … models on the pre-trained foundation model, meaning that you can train sophisticated models by using relatively few training images.


Answer 356

custom

Question 357

An image … model is used to predict the category, orclassof an image

Answer 357

classification

Question 358

… … models detect and classify objects in an image, returning bounding box coordinates to locate each object. In addition to the built-in object detection capabilities in Azure AI Vision, you can train a custom object detection model with your own images.

Answer 358

Object detection

Question 359

Azure AI Visionincludes numerous capabilities for understanding image content and context and extracting information from images. Azure … … … allows you to try out many of the capabilities of image analysis

Answer 359

AI Vision Studio

Question 360

Some potential uses for image classification include:


Answer 360

Product identification: performing visual searches for specific products in online searches or even, in-store using a mobile device.

Disaster investigation: identifying key infrastructure for major disaster preparation efforts. For example, identifying bridges and roads in aerial images can help disaster relief teams plan ahead in regions that are not well mapped.

Medical diagnosis: evaluating images from X-ray or MRI devices could quickly classify specific issues found as cancerous tumors, or many other medical conditions related to medical imaging diagnosis.


Question 361

To create an image classification model, you need data that consists of … and their labels.

Answer 361

features

Question 362

An image classification model is trained to match the patterns in the pixel values to a set of class …. After the model has been trained, you can use it with new sets of features to predict unknown label values.


Answer 362

labels

Question 363

Most modern image classification solutions are based on… … … that make use ofconvolutional neural networks(CNNs) to uncover patterns in the pixels that correspond to particular classes.

Answer 363

deep learningtechniques

Question 364

Training an effective CNN is a complex task that requires considerable expertise in … … and … …

Answer 364

data science, machine learning.

Question 365

Creating an image classification solution with Azure AI Custom Vision consists of two main tasks:

Answer 365

First you must use existing images to train the model, and then you must publish the model so that client applications can use it to generate predictions.

Question 366

To create an image classification system you need a resource in your Azure subscription. You can use the following types of resource:

Answer 366

Custom Vision: A dedicated resource for the custom vision service, which can betraining, aprediction, orbothresources.

Azure AI services: A general resource that includes Azure AI Custom Vision along with many other Azure AI services. You can use this type of resource fortraining,prediction, or both.


Question 367

If you choose to create a Custom Vision resource, you will be prompted to choose… ,… , orboth- and it’s important to note that if you choose “both”, thentworesources are created - one for training and one for prediction.


Answer 367

training, prediction

Question 368

It’s also possible to take a mix-and-match approach in which you use a dedicated Custom Vision resource for training, but deploy your model to an Azure AI services resource for prediction. For this to work, the training and prediction resources must be created in the same ….


Answer 368

region

Question 369

To train a … model, you must upload images to your training resource and label them with the appropriate class labels. Then, you must train the model and evaluate the training results.

You can perform these tasks in theCustom Vision portal, or if you have the necessary coding experience you can use one of the Azure AI Custom Vision service programming language-specific software development kits (SDKs).

One of the key considerations when using images for classification, is to ensure that you have sufficient images of the objects in question and those images should be of the object from many different angles.

Answer 369

classification

Question 370

Model training process is an iterative process in which Azure AI Custom Vision service repeatedly trains the model using some of the data, but holds some back to evaluate the model. At the end of the training process, the performance for the trained model is indicated by the following evaluation metrics:


Answer 370

Precision:
Recall:
Average Precision (AP):

Question 371

… : What percentage of the class predictions made by the model were correct? For example, if the model predicted that 10 images are oranges, of which eight were actually oranges, then the precision is 0.8 (80%).


Answer 371

Prcision

Question 372

… : What percentage of class predictions did the model correctly identify? For example, if there are 10 images of apples, and the model found 7 of them, then the recall is 0.7 (70%).


Answer 372

Recall

Question 373

… … … : An overall metric that takes into account both precision and recall.


Answer 373

Average Precision (AP)

Question 374

When you publish the model, you can assign it a name (the default is “IterationX”, where X is the … … … you have trained the model).


Answer 374

number of times

Question 375

To use your model, client application developers need the following information:


Answer 375

Project ID: The unique ID of the Custom Vision project you created to train the model.

Model name: The name you assigned to the model during publishing.

Prediction endpoint: The HTTP address of the endpoints for thepredictionresource to which you published the model (notthe training resource).

Prediction key: The authentication key for thepredictionresource to which you published the model (notthe training resource).

Question 376

TheAzure AI Visionservice provides useful pre-built models for working with images, but you’ll often need to train your own model for computer vision.

For example, suppose a wildlife conservation organization organization wants to track sightings of animals by using motion-sensitive cameras. The images captured by the cameras could then be used to verify the presence of particular species in a particular area and assist with conservation efforts for endangered species. To accomplish this, the organization would TheAzure AI Visionservice provides useful pre-built models for working with images, but you’ll often need to train your own model for computer vision.

For example, suppose a wildlife conservation organization organization wants to track sightings of animals by using motion-sensitive cameras. The images captured by the cameras could then be used to verify the presence of particular species in a particular area and assist with conservation efforts for endangered species. To accomplish this, the organization would benefit from animage classificationmodel that is trained to identify different species of animal in the captured photographs.

Question 377

In Azure, you can use the… … service to train an image classification model based on existing images. There are two elements to creating an image classification solution. First, you must train a model to recognize different classes using existing images. Then, when the model is trained you must publish it as a service that can be consumed by applications.

Answer 377

Custom Vision

Question 378

… … is a form of computer vision in which artificial intelligence (AI) agents can identify and locate specific types of object in an image or camera feed.

Answer 378

Object detection

Question 379

Some sample applications of object detection include:


Answer 379

Some sample applications of object detection include:

Checking for building safety: Evaluating the safety of a building by analyzing footage of its interior for fire extinguishers or other emergency equipment.

Driving assistance: Creating software for self-driving cars or vehicles withlane assistcapabilities. The software can detect whether there is a car in another lane, and whether the driver’s car is within its own lanes.

Detecting tumors: Medical imaging such as an MRI or x-rays that can detect known objects for medical diagnosis

Question 380

An object detection model returns the following information:


Answer 380

an object detection model returns the following information:

Theclassof each object identified in the image.

The probability score of the object classification (which you can interpret as theconfidenceof the predicted class being correct)

The coordinates of abounding boxfor each object.


Question 381

… … is a machine learning based form of computer vision in which a model is trained to categorize images based on the primary subject matter they contain.… ,..goes further than this to classify individual objects within the image, and to return the coordinates of a bounding box that indicates the object’s location.

Answer 381

Image classification, Object detection

Question 382

You can create an object detection machine learning model by using advanced deep learning techniques. However, this approach requires significant … and a large volume of … ….

Answer 382

expertise, training data

Question 383

Creating an object detection solution with Azure AI Custom Vision consists of three main tasks:

Answer 383

Upload and tag images
Train the model
Publish the trained model so client applications can use it to generate predictions

Question 384

Azure AI Computer Vision can use… …to suggest classes and bounding boxes for images you add to the training dataset.

Answer 384

smart tagging

Question 385

To train the model, you can use theCustom Vision … , or if you have the necessary coding experience you can use one of Azure AI Custom Vision’s programming language-specific software development kits (SDKs).

Answer 385

portal

Question 386

Azure AI Custom Vision can use the following evaluation metrics to judge the performance of the trained model:

Answer 386

Precision: What percentage of class predictions did the model correctly identify? For example, if the model predicted that 10 images are oranges, of which eight were actually oranges, then the precision is 0.8 (80%).

Recall: What percentage of the class predictions made by the model were correct? For example, if there are 10 images of apples, and the model found 7 of them, then the recall is 0.7 (70%).

Mean Average Precision (mAP): An overall metric that takes into account both precision and recall across all classes.


Question 387

To use your model, client application developers need the following information:


Answer 387

Project ID: The unique ID of the Custom Vision project you created to train the model.

Model name: The name you assigned to the model during publishing.

Prediction endpoint: The HTTP address of the endpoints for thepredictionresource to which you published the model (notthe training resource).

Prediction key: The authentication key for thepredictionresource to which you published the model (notthe training resource).

Question 388

Face detection, analysis, and recognition are important capabilities for artificial intelligence (AI) solutions. Azure AI … service in Azure makes it easy integrate these capabilities into your applications.

Answer 388

Face

Question 389

… … and analysis is an area of artificial intelligence (AI) which uses algorithms to locate and analyze human faces in images or video content.


Answer 389

Face detection

Question 390

Face detectioninvolves identifying regions of an image that contain a human face, typically by returning… …coordinates that form a rectangle around the face.

Answer 390

Bounding box,

Question 391

With… …, facial features can be used to train machine learning models to return other information, such as facial features such as nose, eyes, eyebrows, lips, and others.


Answer 391

Face analysis

Question 392

A further application of … … is to train a machine learning model to identify known individuals from their facial features. This is known asfacial recognition, and uses multiple images of an individual to train the model.

Answer 392

facial analysis

Question 393

Microsoft Azure provides multiple Azure AI services that you can use to detect and analyze faces, including:


Answer 393

Azure AI Vision, which offers face detection and some basic face analysis, such as returning the bounding box coordinates around an image.

Azure AI Video Indexer, which you can use to detect and identify faces in a video.

Azure AI Face, which offers pre-built algorithms that can detect, recognize, and analyze faces.


Question 394

The Azure Face service can return the rectangle coordinates for any human faces that are found in an image, as well as a series of attributes related to those face such as:


Answer 394

Accessories: indicates whether the given face has accessories. This attribute returns possible accessories including headwear, glasses, and mask, with confidence score between zero and one for each accessory.

Blur: how blurred the face is, which can be an indication of how likely the face is to be the main focus of the image.

Exposure: such as whether the image is underexposed or over exposed. This applies to the face in the image and not the overall image exposure.

Glasses: whether or not the person is wearing glasses.

Head pose: the face’s orientation in a 3D space.

Mask: indicates whether the face is wearing a mask.
Noise: refers to visual noise in the image. If you have taken a photo with a high ISO setting for darker settings, you would notice this noise in the image. The image looks grainy or full of tiny dots that make the image less clear.

\Occlusion: determines if there might be objects blocking the face in the image.


Question 395

To support Microsoft’sResponsible AI Standard, Azure AI Face and Azure AI Vision have aLimited Access policy.

Anyone can use the Face service to:


Answer 395

Anyone can use the Face service to:

Detect the location of faces in an image.

Determine if a person is wearing glasses.

Determine if there’s occlusion, blur, noise, or over/under exposure for any of the faces.

Return the head pose coordinat


Question 396

The Limited Access policy requires customers tosubmit an intake formto access additional Azure AI Face service capabilities including:


Answer 396

The ability to compare faces for similarity.
The ability to identify named individuals in an image.


Question 397

To use the Face service, you must create one of the following types of resource in your Azure subscription:


Answer 397

Face: Use this specific resource type if you don’t intend to use any other Azure AI services, or if you want to track utilization and costs for Face separately.

Azure AI services: A general resource that includes Azure AI Face along with many other Azure AI services such as Azure AI Content Safety, Azure AI Language, and others. Use this resource type if you plan to use multiple Azure AI services and want to simplify administration and development.


Question 398

There are some considerations that can help improve the accuracy of the detection in the images:

Answer 398

Image format - supported images are JPEG, PNG, GIF, and BMP.

File size - 6 MB or smaller.

Face size range - from 36 x 36 pixels up to 4096 x 4096 pixels. Smaller or larger faces will not be detected.

Other issues - face detection can be impaired by extreme face angles, extreme lighting, and occlusion (objects blocking the face such as a hand).

Question 399

To test the face detection capabilities of the Azure AI Face service, you will useAzure… …

Answer 399

Vision Studio

Question 400

To use the Face detect capabilities you will create an Azure AI services … resource.

Answer 400

multi-service

Question 401

… … … enables artificial intelligence (AI) systems to read text in images, enabling applications to extract information from photographs, scanned documents, and other sources of digitized text.

Answer 401

Optical character recognition (OCR)

Question 402

The ability for computer systems to process written and printed text is an area of artificial intelligence (AI) wherecomputer …intersects with… … processing.

Answer 402

vision, natural language

Question 403

The ability to extract text from images is handled by Azure AI … service.

Answer 403

Vision

Question 404

One of the services in Azure AI Vision is the… API. You can think of the … API as an OCR engine that powers text extraction from images, PDFs, and TIFF files.

Answer 404

Read, Read

Question 405

Organizations can use Azure AI … … to automate data extraction across document types, such as receipts, invoices, and more.

Answer 405

Document Intelligence

Question 406

Typically after a document is scanned, someone will still need to manually enter the extracted text into a ….
Azure AI Document Intelligence identifies the content’s structure and save the data in key, value pairs.


Answer 406

database.

Question 407

Azure AI Document Intelligenceapplies advanced machine learning to extract text, key-value pairs, tables, and structures from documents automatically and accurately. It combines state-of-the-art optical character recognition (OCR) with predictive models that can interpret form data by:


Answer 407

Matching field names to values.
Processing tables of data.
Identifying specific types of field, such as dates, telephone numbers, addresses, totals, and others.


Question 408

Azure AI Document Intelligence supports automated document processing through:


Answer 408

Prebuilt modelsthat are trained to recognize and extract data for common scenarios such as IDs, receipts, and invoices.

Custom models, which enable you to extract what are known as key/value pairs and table data from forms. 

Custom models are trained using your own data, which helps to tailor this model to your specific forms. Starting with a few samples of your forms, you can train the custom model. After the first training exercise, you can evaluate the results and consider if you need to add more samples and re-train.


Question 409

The next hands-on exercise will only step througha prebuilt receipt model. If you would like to train acustom modelyou can refer to theAzure AI Document Intelligence documentationfor quickstarts.


Question 410

To use Azure AI Document Intelligence, you need to either create a … … … or anAzure AI servicesresource in your Azure subscription. Both resource types give access to Azure AI Document Intelligence.


Answer 410

Form Recognizerresource

Question 411

Currently the prebuilt receipt model is designed to recognize common receipts in English that are common to the USA. Examples are receipts used at restaurants, retail locations, and gas stations. The model is able to extract key information from the receipt slip:


Answer 411

time of transaction
date of transaction
merchant information
taxes paid
receipt totals
other pertinent information that may be present on the receipt
all text on the receipt is recognized and returned as well


Question 412

Use the following guidelines to get the best results when using a custom model.


Answer 412

Images must be JPEG, PNG, BMP, PDF, or TIFF formats
File size must be less than 50 MB
Image size between 50 x 50 pixels and 10000 x 10000 pixels
For PDF documents, no larger than 17 inches x 17 inches

Question 413

… … is a process where you evaluate different aspects of a document or phrase, in order to gain insights into the content of that text.

Answer 413

Analyzing text

Question 414

There are some commonly used techniques that can be used to build software to analyze text, including:


Answer 414

Statistical analysis of terms used in the text. For example, removing common “stop words” (words like “the” or “a”, which reveal little semantic information about the text), and performingfrequency analysisof the remaining words (counting how often each word appears) can provide clues about the main subject of the text.

Extending frequency analysis to multi-term phrases, commonly known asN-grams(a two-word phrase is abi-gram, a three-word phrase is atri-gram, and so on).

Applyingstemmingorlemmatizationalgorithms to normalize words before counting them - for example, so that words like “power”, “powered”, and “powerful” are interpreted as being the same word.

Applying linguistic structure rules to analyze sentences - for example, breaking down sentences into tree-like structures such as anoun phrase, which itself contains nouns, verbs, adjectives, and so on.

Encoding words or terms as numeric features that can be used to train a machine learning model. For example, to classify a text document based on the terms it contains. This technique is often used to performsentiment analysis, in which a document is classified as positive or negative.

Creatingvectorizedmodels that capture semantic relationships between words by assigning them to locations in n-dimensional space. This modeling technique might, for example, assign values to the words “flower” and “plant” that locate them close to one another, while “skateboard” might be given a value that positions it much further away.


Question 415

Azure AI Language service can help simplify application development by using pre-trained models that can:

Answer 415

Determine the language of a document or text (for example, French or English).

Perform sentiment analysis on text to determine a positive or negative sentiment.

Extract key phrases from text that might indicate its main talking points.

Identify and categorize entities in the text. Entities can be people, places, organizations, or even everyday items such as dates, times, quantities, and so on.


Question 416

Azure AI … is a part of the Azure AI services offerings that can perform advanced natural language processing over raw text.


Answer 416

Language

Question 417

Use the language detection capability of Azure AI Language to identify the language in which text is written. You can submit multiple documents at a time for analysis. For each document submitted to it, the service will detect:


Answer 417

The language name (for example “English”).
The ISO 639-1 language code (for example, “en”).
A score indicating a level of confidence in the language detection.


Question 418

Notice that the language detected for review 3 is English, despite the text containing a mix of English and French. The language detection service will focus on thepredominantlanguage in the text. The service uses an algorithm to determine the predominant language, such as length of phrases or total amount of text for the language compared to other languages in the text. The predominant language will be the value returned, along with the language code. The confidence score may be less than … as a result of the mixed language text.



Answer 418

1

Question 419

using Azure AI Language to analyze the text “:-)”, results in a value ofunknownfor the language name and the language identifier, and a score ofNaN(which is used to indicate … … …).


Answer 419

not a number

Question 420

The text analytics capabilities in Azure AI Language can evaluate text and return sentiment scores and labels for each….

Answer 420

sentence

Question 421

The AI Language service returns a sentiment score in the range of 0 to 1. Values closer to 1 represent a … sentiment. Scores that are close to the middle of the range (0.5) are considered neutral or indeterminate.


Answer 421

Positive,

Question 422

a list of words in a sentence that has no structure, could result in an … score.

Answer 422

indeterminate

Question 423

If you pass text in French but tell the service the language code isenfor English, the service will return a score of precisely …


Answer 423

0.5.


Question 424

… … … is the concept of evaluating the text of a document, or documents, and then identifying the main talking points of the document(s). Consider the restaurant scenario discussed previously. Depending on the volume of surveys that you have collected, it can take a long time to read through the reviews. Instead, you can use the key phrase extraction capabilities of the Language service to summarize the main points.


Answer 424

Key phrase extraction

Question 425

Key phrase extraction can provide some context to this review by extracting the following phrases:

attentive service
great food
birthday celebration
fantastic experience
table
friendly hostess
dinner
ambiance
place

Not only can you use … … to determine that this review is positive, you can use the key phrases to identify important elements of the review.


Answer 425

sentiment analysis

Question 426

You can provide Azure AI Language with unstructured text and it will return a list of… in the text that it recognizes. Azure AI Language can also provide links to more information about that entity on the web. An entity is essentially an item of a particular type or a category; and in some cases, subtype, such as those as shown in the following table.


Answer 426

entities

Question 427

Azure AI Language also supportsentity linkingto help disambiguate entities by linking to a specific reference. For recognized entities, the service returns a URL for a relevant … article.


Answer 427

Wikipedia

Question 428

To enable speech interaction, the AI system must support two capabilities:


Answer 428

Speech recognition- the ability to detect and interpret spoken input.

Speech synthesis- the ability to generate spoken output.


Question 429

Speech … is concerned with taking the spoken word and converting it into data that can be processed - often by transcribing it into a text representation.

Answer 429

recognition

Question 430

Speech patterns are analyzed in the audio to determine recognizable patterns that are mapped to words. To accomplish this feat, the software typically uses multiple types of models, including:


Answer 430

Anacousticmodel that converts the audio signal into phonemes (representations of specific sounds).

Alanguagemodel that maps phonemes to words, usually using a statistical algorithm that predicts the most probable sequence of words based on the phonemes.


Question 431

The recognized words are typically converted to text, which you can use for various purposes, such as.


Answer 431

Providing closed captions for recorded or live videos
Creating a transcript of a phone call or meeting
Automated note dictation
Determining intended user input for further processing


Question 432

Speech synthesis is in many respects the reverse of speech recognition. It is concerned with vocalizing data, usually by converting text to speech. A speech synthesis solution typically requires the following information:


Answer 432

The text to be spoken.
The voice to be used to vocalize the speech.


Question 433

To synthesize speech, the system typically …the text to break it down into individual words, and assigns phonetic sounds to each word. It then breaks the phonetic transcription intoprosodicunits (such as phrases, clauses, or sentences) to create phonemes that will be converted to audio format. These phonemes are then synthesized as audio by applying a voice, which will determine parameters such as pitch and timbre; and generating an audio wave form that can be output to a speaker or written to a file.


Answer 433

tokenizes

Question 434

You can use the output of speech synthesis for many purposes, including:


Answer 434

Generating spoken responses to user input.
Creating voice menus for telephone systems.
Reading email or text messages aloud in hands-free scenarios.
Broadcasting announcements in public locations, such as railway stations or airports.

Question 435

Microsoft Azure offers both speech recognition and speech synthesis capabilities throughAzure AI Speechservice, which includes the following application programming interfaces (APIs):


Answer 435

TheSpeech to textAPI
TheText to speechAPI


Question 436

You can use Azure AI Speech to text API to perform real-time or … transcription of audio into a text format. The audio source for transcription can be a real-time audio stream from a microphone or an audio file.


Answer 436

batch

Question 437

The model that is used by the Speech to text API, is based on the Universal Language Model that was trained by Microsoft. The data for the model is Microsoft-owned and deployed to Microsoft Azure. The model is optimized for two scenarios, … and …. You can also create and train your own custom models including acoustics, language, and pronunciation if the pre-built models from Microsoft do not provide what you need.


Answer 437

conversational, dictation

Question 438

Real-time speech to text allows you to transcribe text in audio streams. You can use real-time transcription for presentations, demos, or any other scenario where a person is speaking.

In order for real-time transcription to work, your application will need to be listening for incoming audio from a microphone, or other audio input source such as an audio file. Your … ,.. streams the audio to the service, which returns the transcribed text.



Answer 438

application code

Question 439

Not all speech to text scenarios are real time. You may have audio recordings stored on a file share, a remote server, or even on Azure storage. You can point to audio files with a … … … URI and asynchronously receive transcription results.


Answer 439

shared access signature (SAS)

Question 440

Batch transcription should be run in an … manner because the batch jobs are scheduled on abest-effort basis. Normally a job will start executing within minutes of the request but there is no estimate for when a job changes into the running state.


Answer 440

asynchronous

Question 441

When you use the text to speech API, you can specify the … to be used to vocalize the text. This capability offers you the flexibility to personalize your speech synthesis solution and give it a specific character.


Answer 441

voice

Question 442

The service includes multiple pre-defined voices with support for multiple languages and regional pronunciation, including standardvoices as well asneuralvoices that leverage … … to overcome common limitations in speech synthesis with regard to intonation, resulting in a more natural sounding voice. You can also develop custom voices and use them with the text to speech API

Answer 442

neural networks

Question 443

A … translation is where each word is translated to the corresponding word in the target language. This approach presents some issues. For one case, there may not be an equivalent word in the target language. Another case is where literal translation can change the meaning of the phrase or not get the context correct.

Answer 443

literal

Question 444

Artificial intelligence systems must be able to understand, not only the words, but also the… … in which they are used. In this way, the service can return a more accurate translation of the input phrase or phrases. The grammar rules, formal versus informal, and colloquialisms all need to be considered.

Answer 444

semanticcontext

Question 445

… translationcan be used to translate documents from one language to another, translate email communications that come from foreign governments, and even provide the ability to translate web pages on the Internet. Many times you will see aTranslateoption for posts on social media sites, or the Bing search engine can offer to translate entire web pages that are returned in search results.

Answer 445

Text

Question 446

… translationis used to translate between spoken languages, sometimes directly (speech-to-speech translation) and sometimes by translating to an intermediary text format (speech-to-text translation).

Answer 446

Speech

Question 447

Microsoft provides Azure AI services that support translation. Specifically, you can use the following services:


Answer 447

TheAzure AI Translatorservice, which supports text-to-text translation.

TheAzure AI Speechservice, which enables speech to text and speech-to-speech translation.


Question 448

Azure AI Translator is easy to integrate in your applications, websites, tools, and solutions. The service uses a … … … model for translation, which analyzes the semantic context of the text and renders a more accurate and complete translation as a result.


Answer 448

Neural Machine Translation (NMT)

Question 449

Azure AI Translator supports text-to-text translation betweenmore than … languages. When using the service, you must specify the language you are translatingfromand the language you are translatingtousing ISO 639-1 language codes, such asenfor English,frfor French, andzhfor Chinese. 


Answer 449

60

Question 450

Alternatively, you can specify … … of languages by extending the language code with the appropriate 3166-1 cultural code - for example,en-USfor US English,en-GBfor British English, orfr-CAfor Canadian French.


Answer 450

cultural variants

Question 451

When using Azure AI Translator, you can specify one …language with multiple …languages, enabling you to simultaneously translate a source document into multiple languages.


Answer 451

from, to

Question 452

Azure AI Translator’s application programming interface (API) offers some optional configuration to help you fine-tune the results that are returned, including:

Answer 452

Profanity filtering. Without any configuration, the service will translate the input text, without filtering out profanity. Profanity levels are typically culture-specific but you can control profanity translation by either marking the translated text as profane or by omitting it in the results.

Selective translation. You can tag content so that it isn’t translated. For example, you may want to tag code, a brand name, or a word/phrase that doesn’t make sense when localized.



Question 453

Azure AI Speech includes the following APIs:


Answer 453

Speech to text- used to transcribe speech from an audio source to text format.

Text to speech- used to generate spoken audio from a text source.

Speech Translation- used to translate speech in one language to text or speech in another.


Question 454

You can use the … … … to translate spoken audio from a streaming source, such as a microphone or audio file, and return the translation as text or an audio stream. This enables scenarios such as real-time closed captioning for a speech or simultaneous two-way translation of a spoken conversation.


Answer 454

Speech TranslationAPI

Question 455

As with Azure AI Translator, you can specify one source language and one or more … languages to which the source should be translated with Azure AI Speech. You can translate speech intoover 60 languages.


Answer 455

target

Question 456

The source language must be specified using the … … and … … format, such ases-USfor American Spanish. This requirement helps ensure that the source is understood properly, allowing for localized pronunciation and linguistic idioms.


Answer 456

extended language, culture code

Question 457

To work with conversational language understanding, you need to take into account three core concepts:…, …, and ….

Answer 457

utterances,entities, andintents.

Question 458

An … is an example of something a user might say, and which your application must interpret.

Answer 458

utterance

Question 459

An … is an item to which an utterance refers.

Answer 459

entity

Question 460

An … represents the purpose, or goal, expressed in a user’s utterance.

Answer 460

intent

Question 461

If the intent is to turn a device on; so in your conversational language understanding application, you might define aTurnOnintent that is related to the given ….

Answer 461

utterancesd

Question 462

A conversational language understanding application defines a model consisting of intents and entities. … are used to train the model to identify the most likely … and the … to which it should be applied based on a given input.

Answer 462

Utterances, intent, entities

Question 463

There are numerous … used for each of the intents. The intent should be a concise way of grouping the … tasks.

Answer 463

utterances, utterance

Question 464

You should consider always using the…intent to help handle utterances that do not map any of the utterances you have entered.

Answer 464

None

Question 465

In a conversational language understanding application, theNoneintent is created but left empty on purpose. TheNoneintent is a … intent and can’t be deleted or renamed.

Answer 465

required

Question 466

After defining the entities and intents with sample utterances in your conversational language understanding application, you can train a language model to predict intents and entities from user input - even if it doesn’t match the sample utterances exactly.
You can then use the model from a client application to retrieve … and respond appropriately.

Answer 466

predictions

Question 467

For each of the authoring and prediction tasks, you need a resource in your Azure subscription. You can use the following types of resource:

Answer 467

Language: A resource that enables you to build apps with industry-leading natural language understanding capabilities without machine learning expertise. You can use a language resource forauthoringandprediction.

Azure AI services: A general resource that includes conversational language understanding along with many other Azure AI services. You can only use this type of resource forprediction.

Question 468

After you’ve created an … resource, you can use it to author and train a conversational language understanding application by defining the entities and intents that your application will predict as well as utterances for each intent that can be used to train the predictive model.

Answer 468

Authoring

Question 469

Conversational language understanding provides a comprehensive collection of … …that include pre-defined intents and entities for common scenarios; which you can use as a starting point for your model. You can also create your own entities and intents.

Answer 469

prebuiltdomains

Question 470

When you create entities and intents, you can do so in any ,,,.

Answer 470

order

Question 471

You can write code to define the elements of your model, but in most cases it’s easiest to author your model using the Azure AI Language …

Answer 471

Portal

Question 472

Best practice is to use the Azure AI Language portal for authoring and to use the … for runtime predictions.

Answer 472

SDK

Question 473

Define intents based on … a user would want to perform with your application. For each intent, you should include a variety of utterances that provide examples of how a user might express the intent.
If an intent can be applied to multiple entities, be sure to include sample utterances for each potential entity; and ensure that each entity is identified in the utterance.

Answer 473

actions

Question 474

There are four types of entities:

Answer 474

Machine-Learned: Entities that are learned by your model during training from context in the sample utterances you provide.

List: Entities that are defined as a hierarchy of lists and sublists. For example, adevicelist might include sublists forlightandfan. For each list entry, you can specify synonyms, such aslampforlight.

RegEx: Entities that are defined as aregular expressionthat describes a pattern - for example, you might define a pattern like[0-9]{3}-[0-9]{3}-[0-9]{4}for telephone numbers of the form555-123-4567.

Pattern.any: Entities that are used withpatternsto define complex entities that may be hard to extract from sample utterances.

Question 475

After you have defined the intents and entities in your model, and included a suitable set of sample utterances; the next step is to train the model. Training is the process of using your sample utterances to teach your model to match natural language expressions that a user might say to probable intents and entities.

After training the model, you can test it by submitting text and reviewing the predicted intents. Training and testing is an … process. After you train your model, you test it with sample utterances to see if the intents and entities are recognized correctly. If they’re not, make updates, retrain, and test again.

Answer 475

iterative

Question 476

When you are satisfied with the results from the training and testing, you can publish your Conversational Language Understanding application to a … resource for consumption.

Client applications can use the model by connecting to the endpoint for the prediction resource, specifying the appropriate authentication key; and submit user input to get predicted intents and entities. The predictions are returned to the client application, which can then take appropriate action based on the predicted intent.

Answer 476

prediction

Question 477

Create a custom question answering knowledge base with Azure AI Language and create a bot with Azure AI … … that answers user questions.

Answer 477

Bot Service

Question 478

To implement a bot conversation solution, you need:

Answer 478

Aknowledge baseof question and answer pairs - usually with some built-in natural language processing model to enable questions that can be phrased in multiple ways to be understood with the same semantic meaning.

Abot servicethat provides an interface to the knowledge base through one or more channels.

Question 479

You can easily create a user support bot solution on Microsoft Azure using a combination of two core services:

Answer 479

Azure AI Language: includes a custom question answering feature that enables you to create a knowledge base of question and answer pairs that can be queried using natural language input.Note
The question answering capability in Azure AI Language is a newer version of the QnA Maker service - which is still available as a separate service.

Azure AI Bot Service: provides a framework for developing, publishing, and managing bots on Azure.

Question 480

The first challenge in creating a user support bot is to use Azure AI … to create a knowledge base. You can use theLanguage Studio’s custom question answering feature to create, train, publish, and manage knowledge bases.

Answer 480

Language

Question 481

You can write code to create and manage knowledge bases using the Azure AI Language REST API or SDK. However, in most scenarios it is easier to use the … Studio.

Answer 481

Language

Question 482

To create a knowledge base, you must first provision a…resource in your Azure subscription

Answer 482

Language

Question 483

After provisioning a Language resource, you can use the Language Studio’s custom question answering feature to create a knowledge base that consists of …-…-… pairs.

Answer 483

question-and-answer

Question 484

These questions and answers can be:

Answer 484

Generated from an existing FAQ document or web page.
Entered and edited manually.

Question 485

In many cases, a knowledge base is created using a combination of all of these techniques; starting with a base dataset of questions and answers from an existing … document and extending the knowledge base with additional …. entries.

Answer 485

FAQ, manual

Question 486

Questions in the knowledge base can be assignedalternative phrasingto help consolidate questions with the same ….

Answer 486

meaning

Question 487

After creating a set of question-and-answer pairs, you must save it. This process analyzes your literal questions and answers and applies a built-in natural language processing model to match appropriate answers to questions, even when they are not phrased exactly as specified in your question definitions. Then you can use the built-in … interface in the Language Studio to test your knowledge base by submitting questions and reviewing the answers that are returned.

Answer 487

test

Question 488

When you’re satisfied with your knowledge base, deploy it. Then you can use it over its REST interface. To access the knowledge base, client applications require:

Answer 488

The knowledge base ID
The knowledge base endpoint
The knowledge base authorization key

Question 489

After you’ve created and deployed a knowledge base, you can deliver it to users through a ….

Answer 489

bot

Question 490

You can create a custom … by using the Microsoft Bot Framework SDK to write code that controls conversation flow and integrates with your knowledge base. However, an easier approach is to use the automatic bot creation functionality, which enables you to create a bot for your deployed knowledge base and publish it as an Azure AI Bot Service application with just a few clicks.


Answer 490

bot

Question 491

After creating your bot, you can manage it in the Azure portal, where you can:


Answer 491

Extend the bot’s functionality by adding custom code.
Test the bot in an interactive test interface.
Configure logging, analytics, and integration with other services.


Question 492

For simple updates, you can edit bot code directly in the Azure portal. However, for more comprehensive customization, you can download the … …and edit it locally; republishing the bot directly to Azure when you’re ready.

Answer 492

source code

Question 493

When your bot is ready to be delivered to users, you can connect it to … …; making it possible for users to interact with it through web chat, email, Microsoft Teams, and other common communication media.


Answer 493

multiplechannels

Question 494

… … is an artificial intelligence technique used to determine whether values in a series are within expected parameters.


Answer 494

Anomaly detection

Question 495

Azure AI Anomaly Detector is a cloud-based service that helps you monitor and detect in your historical time series and real-time data.

Answer 495

abnormalities

Question 496

Anomalies are values that are … the expected values or range of values.


Answer 496

outside

Question 497

In the graphic depicting the time series data, there’s a light shaded area that indicates the boundary, or sensitivity range. The solid blue line is used to indicate the … …. When a measured value is outside of the shaded boundary, an orange dot is used to indicate the value is considered an anomaly. The sensitivity boundary is a parameter that you can specify when calling the service. It allows you to adjust that boundary settings to tweak the results.


Answer 497

measured values

Question 498

Accurate anomaly detection leads to prompt troubleshooting, which helps to avoid revenue … and maintain brand reputation.


Answer 498

loss

Question 499

AI Anatoly detector doesn’t require you to know machine learning. You can use the … … to integrate Azure AI Anomaly Detector into your applications with relative ease.

Answer 499

REST API

Question 500

The AI anomaly detector service uses the concept of a “… …” strategy. The main parameter you need to customize is “sensitivity,” which is from 1 to 99 to adjust the outcome to fit the scenario. The service can detect anomalies in historical time series data and also in real-time data such as streaming input from IoT devices, sensors, or other streaming input sources.


Answer 500

one parameter

Question 501

By default, the upper and lower boundaries for anomaly detection are calculated using concepts known as …, …, and ….

Answer 501

expectedValue, upperMargin, andlowerMargin

The upper and lower boundaries are calculated using these three values. If a value exceeds either boundary, it will be identified as an anomaly.

Question 502

You can adjust the boundaries by applying amarginScaleto the upper and lower margins as demonstrated by the following formula.

Answer 502

upperBoundary = expectedValue + (100 - marginScale) * upperMargin

Question 503

Azure AI Anomaly Detector accepts data in JSON format. You can use any numerical data that you have recorded over time.

The key aspects of the data being sent includes the …, …, and … … that was recorded for that timestamp. An example of a JSON object that you might send to the API is shown in this code sample. The granularity is set as hourly and is used to represent temperatures in degrees celsius that were recorded at the timestamps indicated.

Answer 503

granularity, a timestamp, and a value

{
“granularity”: “hourly”,
“series”: [
{
“timestamp”: “2021-03-02T01:00:00Z”,
“value”: -10.56
},
{
“timestamp”: “2021-03-02T02:00:00Z”,
“value”: -8.30
},
{
“timestamp”: “2021-03-02T03:00:00Z”,
“value”: -10.30
},
{
“timestamp”: “2021-03-02T04:00:00Z”,
“value”: 5.95
},
]
}

Question 504

The anomaly detector service will support a maximum of … data points however, sending this many data points in the same JSON object, can result in latency for the response. You can improve the response by breaking your data points into smaller chunks (windows) and sending these in a sequence.

Answer 504

8640

Question 505

The same JSON object format for Anatoly detection is used in a streaming scenario. The main difference is that you will send a single value in each …. The streaming detection method will compare the current value being sent and the previous value sent.

Answer 505

request

Question 506

If your data has missing … in the sequence, consider the following recommendations.
Sampling occurs every few minutes and has less than 10% of the expected number of points missing. In this case, the impact should be negligible on the detection results.

If you have more than 10% missing, there are options to help “fill” the data set. Consider using a linear interpolation method to fill in the missing values and complete the data set. This will fill gaps with evenly distributed values.


Answer 506

values

Question 507

Azure AI Anomaly Detector will provide the best results if your time series data is … distributed. If the data is more randomly distributed, you can use an aggregation method to create a more even distribution data set.

Answer 507

evenly

Question 508

Azure AI Anomaly Detector supports batch processing of time series data and … anomaly detection for real-time data.

Answer 508

last-point

Question 509

… … involves applying the algorithm to an entire data series at one time. The concept of time series data involves evaluation of a data set as a batch. Use your time series to detect any anomalies that might exist throughout your data. This operation generates a model using your entire time series data, with each point analyzed using the same model.

Answer 509

Batch detection

Question 510

Batch detection is best used when your data contains:

Answer 510

Flat trend time series data with occasional spikes or dips
Seasonal time series data with occasional anomalies
Seasonality is considered to be a pattern in your data that occurs at regular intervals. Examples would be hourly, daily, or monthly patterns. When you use seasonal data, specifying a period for that pattern can help to reduce the latency in detection.


Question 511

When you use the … detection mode, Azure AI Anomaly Detector creates a single statistical model based on the entire data set passed to the service. From this model, each data point in the data set is evaluated and anomalies are identified.

Answer 511

batch

Question 512

Consider a pharmaceutical company that stores medications in storage facilities where the temperature in the facilities needs to remain within a specific range. To evaluate whether the medication remained stored in a safe temperature range in the past three months we need to know:


Answer 512

the maximum allowable temperature
the minimum allowable temperature
the acceptable duration of time for temperatures to be outside the safe range


Question 513

If you are interested in evaluating compliance over historical readings, you can extract the required time series data, package it into a JSON object, and send it to Azure AI Anomaly Detector for evaluation. You will then have a … view of the temperature readings over time.

Answer 513

historical

Question 514

Real-time detection uses streaming data by comparing previously seen data points to the … data point to determine if your latest one is an anomaly. This operation generates a model using the data points you send, and determines if the target (current) point is an anomaly. By calling the service with each new data point you generate, you can monitor your data as it’s created.

Answer 514

last

Question 515

… detection is most useful for monitoring critical storage requirements that must be acted on immediately.

Answer 515

Streaming

Question 516

Use Azure … … to make your data searchable, a cloud search service that has tools for building user-managed indexes

Answer 516

Cognitive Search

Question 517

… … is the term used to describe solutions that involve extracting information from large volumes of often unstructured data.

Answer 517

Knowledge mining

Question 518

Cognitive search indexes can be used for internal only use, or to enable searchable content on … … internet assets.


Answer 518

public-facing

Question 519

Azure Cognitive Search results contain only your data, which can include text inferred or extracted from images, or new entities and key phrases detection through … …

Answer 519

text analytics.

Question 520

Cognitive search is a … … … … solution.

Answer 520

Platform as a Service (PaaS)

Question 521

Azure Cognitive Search exists to complement existing technologies and provides a programmable search engine built on … …, an open-source software library.

Answer 521

Apache Lucene

Question 522

Cognitive search is a highly available platform offering a … uptime SLA available for cloud and on-premises assets.

Answer 522

99.9%

Question 523

Azure Cognitive Search comes with the following features:

Answer 523

Data from any source:
Full text search and analysis:
AI powered search:
Multi-lingual:
Geo-enabled:
Configurable user experience:

Question 524

: Azure Cognitive Search accepts data from any source provided in JSON format, with auto crawling support for selected data sources in Azure.


Answer 524

Data from any source

Question 525

: Azure Cognitive Search offers full text search capabilities supporting both simple query and full Lucene query syntax.


Answer 525

Full text search and analysis

Question 526

: Azure Cognitive Search has Azure AI capabilities built in for image and text analysis from raw content.


Answer 526

AI powered search

Question 527

: Azure Cognitive Search offers linguistic analysis for 56 languages to intelligently handle phonetic matching or language-specific linguistics. Natural language processors available in Azure Cognitive Search are also used by Bing and Office.


Answer 527

Multi-lingual

Question 528

: Azure Cognitive Search supports geo-search filtering based on proximity to a physical location.


Answer 528

Geo-enabled

Question 529

: Azure Cognitive Search has several features to improve the user experience including autocomplete, autosuggest, pagination, and hit highlighting.

Answer 529

Configurable user experience

Question 530

A typical Azure Cognitive Search solution starts with a data source that contains the … … you want to search. This could be a hierarchy of folders and files in Azure Storage, or text in a database such as Azure SQL Database or Azure Cosmos DB. The data format that Cognitive Search supports is JSON. Regardless of where your data originates, if you can provide it as a JSON document, the search engine can index it.

Answer 530

data artifacts

Question 531

If your data resides in supported data source, you can use an indexer to automate … …, including JSON serialization of source data in native formats.

Answer 531

data ingestion

Question 532

An indexer connects to a data source, … the data, and passes to the search engine for indexing. Most indexers support change detection, which makes data refresh a simpler exercise.

Answer 532

serializes

Question 533

Besides automating data ingestion, indexers also support … …. You can attach a skillset that applies a sequence of AI skills to enrich the data, making it more searchable. A comprehensive set of built-in skills, based on Azure AI services APIs, can help you derive new fields – for example by recognizing entities in text, translating text, evaluating sentiment, or predicting appropriate captions for images. Optionally, enriched content can be sent to a .,. … , which stores output from an AI enrichment pipeline in tables and blobs in Azure Storage for independent analysis or downstream processing.

Answer 533

AI enrichment, knowledge store

Question 534

Whether you write application code that pushes data to an index - or use an indexer that automates data ingestion and adds AI enrichment - the fields containing your content are … in an index, which can be searched by client applications. The fields are used for searching, filtering, and sorting to generate a set of results that can be displayed or otherwise used by the client application.

Answer 534

persisted

Question 535

AI … refers to embedded image and natural language processing in a pipeline that extracts text and information from content that can’t otherwise be indexed for full text search.

Answer 535

enrichment

Question 536

AI processing is achieved by adding and combining skills in a skillset. A skillset defines the operations that … and … data to make it searchable. These AI skills can be either built-in skills, such as text translation or Optical Character Recognition (OCR), or custom skills that you provide.

Answer 536

extract and enrich

Question 537

… ,,, are based on pretrained models from Microsoft, which means you can’t train the model using your own training data.

Answer 537

Built-in skills

Question 538

Skills that call the Azure AI services APIs have a dependency on those services and are billed at the Azure AI services …-…-…-… price when you attach a resource. Other skills are metered by Azure Cognitive Search, or are utility skills that are available at no charge.

Answer 538

pay-as-you-go

Question 539

Built-in skills fall into these categories:

Answer 539

Natural language processing skills: with these skills, unstructured text is mapped as searchable and filterable fields in an index.

Image processing skills: creates text representations of image content, making it searchable using the query capabilities of Azure Cognitive Search.

Question 540

Natural language processing skills include:

Answer 540

Key Phrase Extraction: uses a pre-trained model to detect important phrases based on term placement, linguistic rules, proximity to other terms, and how unusual the term is within the source data.

Text Translation Skill: uses a pre-trained model to translate the input text into various languages for normalization or localization use cases.

Question 541

Image processing skills include:

Answer 541

Image Analysis Skill: uses an image detection algorithm to identify the content of an image and generate a text description.

Optical Character Recognition Skill: allows you to extract printed or handwritten text from images, such as photos of street signs and products, as well as from documents—invoices, bills, financial reports, articles, and more.

Question 542

An Azure Cognitive … … can be thought of as a container of searchable documents. Conceptually you can think of an index as a table and each row in the table represents a document. Tables have columns, and the columns can be thought of as equivalent to the fields in a document. Columns have data types, just as the fields do on the documents.

An index is a persistent collection of JSON documents and other content used to enable search functionality. The documents within an index can be thought of as rows in a table, each document is a single unit of searchable data in the index.

Answer 542

Search index

Question 543

The index includes a definition of the structure of the data in these documents, called its schema.

Question 544

Azure Cognitive Search needs to know how you would like to search and display the fields in the documents. You specify that by assigning …, or behaviors, to these fields.

Answer 544

attributes

Question 545

For each field in the document, the index stores its …, the data …, and supported … for the field such as, is the field searchable, can the field be sorted?

Answer 545

name

type

behaviors

Question 546

The most efficient indexes use only the behaviors that are needed. If you forget to set a required behavior on a field when designing, the only way to get that feature is to ,,, the index.

Answer 546

rebuild

Question 547

In order to index the documents in Azure Storage, they need to be exported from their original file type to …. In order to export data in any format to JSON, and load it into an index, we use an …

Answer 547

JSON, indexer.

Question 548

To create search documents, you can either generate JSON documents with application code or you can use Azure’s … to export incoming documents into JSON

Answer 548

indexer

Question 549

Azure Cognitive Search lets you create and load JSON documents into an index with two approaches:

Answer 549

Push method: JSON data is pushed into a search index via either the REST API or the .NET SDK. Pushing data has the most flexibility as it has no restrictions on the data source type, location, or frequency of execution.

Pull method: Search service indexers can pull data from popular Azure data sources, and if necessary, export that data into JSON if it isn’t already in that format.

Question 550

Azure Cognitive Search’s indexer is a crawler that extracts searchable text and metadata from an external Azure data source and populates a search index using field-to-field mappings between source data and your index. Using the indexer is sometimes referred to as a ‘pull model’ approach because the service pulls data in without you having to write any code that adds data to an index. An indexer maps source fields to their matching fields in the index.

Question 551

The search services overview page has a … that lets you quickly see the health of the search service. On the dashboard, you can see how many documents are in the search service, how many indexes have been used, and how much storage is in use.

Answer 551

dashboard

Question 552

When loading new documents into an index, the progress can be monitored by clicking on the index’s associated …

Answer 552

indexer

Question 553

Once the index is ready for querying, you can then use … to verify the results. An index is ready when the first document is successfully loaded.

Answer 553

Search explorer

Question 554

Indexers only import new or updated documents, so it is normal to see zero documents indexed.
The Search explorer can perform quick searches to check the contents of an index, and ensure that you are getting expected search results. Having this tool available in the portal enables you to easily check the index by reviewing the results that are returned as … documents.

Answer 554

JSON

Question 555

You have to drop and recreate indexes if you need to make changes to field definitions. Adding new fields is supported, with all existing documents having null values. You’ll find it faster using a code-based approach to iterate your designs, as working in the portal requires the index to be deleted, recreated, and the … details to be manually filled out.

Answer 555

schema

Question 556

An approach to updating an index without affecting your users is to create a new index under a different …. You can use the same indexer and data source. After importing data, you can switch your app to use the new index.

Answer 556

name

Question 557

A knowledge store is persistent storage of enriched content. The purpose of a knowledge store is to store the data generated from AI enrichment in a container. For example, you may want to save the results of an AI skillset that generates captions from ….

Answer 557

images

Question 558

Recall that skillsets move a document through a sequence of enrichments that invoke transformations, such as recognizing entities or translating text. The outcome can be a search index, or projections in a knowledge store. The two outputs, search index and knowledge store, are mutually exclusive products of the same pipeline; derived from the same inputs, but resulting in output that is structured, stored, and used in different ….

Answer 558

applications

Question 559

While the focus of an Azure Cognitive Search solution is usually to create a searchable index, you can also take advantage of its data extraction and enrichment capabilities to persist the enriched data in a knowledge store for further analysis or ….

Answer 559

processing

Question 560

A knowledge store can contain one or more of three types of projection of the extracted data:

Answer 560

Table projections are used to structure the extracted data in a relational schema for querying and visualization

Object projections are JSON documents that represent each data entity

File projections are used to store extracted images in JPG format

Question 561

Before using an indexer to create an index, you’ll first need to make your data available in a supported data source. Supported data sources include:

Answer 561

Cosmos DB (SQL API)
Azure SQL (database, managed instance, and SQL Server on an Azure VM)
Azure Storage (Blob Storage, Table Storage, ADLS Gen2)

Question 562

Once your data is in an Azure data source, you can begin using Azure Cognitive Search. Contained within the Azure Cognitive Search service in Azure portal is the Import data wizard, which automates processes in the Azure portal to create various objects needed for the search engine. You can see it in action when creating any of the following objects using the Azure portal:

Answer 562

Data Source: Persists connection information to source data, including credentials. A data source object is used exclusively with indexers.

Index: Physical data structure used for full text search and other queries.

Indexer: A configuration object specifying a data source, target index, an optional AI skillset, optional schedule, and optional configuration settings for error handling and base-64 encoding.

Skillset: A complete set of instructions for manipulating, transforming, and shaping content, including analyzing and extracting information from image files. Except for very simple and limited structures, it includes a reference to an Azure AI services resource that provides enrichment.

Knowledge store: Stores output from an AI enrichment pipeline in tables and blobs in Azure Storage for independent analysis or downstream processing.


Question 563

To use Azure Cognitive Search, you’ll need an Azure Cognitive Search resource. You can create a resource in the Azure portal. Once the resource is created, you can manage components of your service from the resource …page in the portal.

Answer 563

Overview

Question 564

You can build Azure search indexes using the Azure portal or programmatically with the … … or software development kits (SDKs).

Answer 564

REST API

Question 565

Index and query design are closely linked. After we build the index, we can perform queries. A crucial component to understand is that the … of the index determines what queries can be answered.

Answer 565

schema

Question 566

Azure Cognitive Search queries can be submitted as an HTTP or REST API request, with the response coming back as …. Queries can specify what fields are searched and returned, how search results are shaped, and how the results should be filtered or sorted. A query that doesn’t specify the field to search will execute against all the searchable fields within the index.

Answer 566

JSON

Question 567

Azure Cognitive Search supports two types of syntax: simple and full Lucene. Simple syntax covers all of the common query scenarios, while full Lucene is useful for advanced scenarios.

Question 568

A query request is a list or words (search terms) and query operators (simple or full) of what you would like to see returned in a result set.

Question 569

Consider this simple search example:

coffee (-“busy” + “wifi”)

Answer 569

This query is trying to find content about coffee, excluding busy and including wifi.
Breaking the query into components, it’s made up of search terms, (coffee), plus two verbatim phrases,”busy”and”wifi”, and operators (-,+, and( )). The search terms can be matched in the search index in any order or location in the content. The two phrases will only match with exactly what is specified, sowi-fiwould not be a match. Finally, a query can contain a number of operators. In this example, the-operator tells the search engine that these phrases shouldNOTbe in the results. The parenthesis group terms together, and set their precedence.

By default, the search engine will match any of the terms in the query. Content containing justcoffeewould be a match. In this example, using-“busy”would lead to the search results including all content that doesn’t have the exact string “busy” in it.