ET 51 to 100

Question 1

What are two metrics that you can use to evaluate a regression model? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

A. coefficient of determination (R2)
B. F1 score
C. root mean squared error (RMSE)
D. area under curve (AUC)
E. balanced accuracy

Answer 1

A. coefficient of determination (R2)
C. root mean squared error (RMSE)

: R-squared (R2), or Coekcient of determination represents the predictive power of the model as a value between -inf and 1.00. 1.00 means there is a perfect +t, and the +t can be arbitrarily poor so the scores can be negative.
C: RMS-loss or Root Mean Squared Error (RMSE) (also called Root Mean Square Deviation, RMSD), measures the difference between values predicted by a model and the values observed from the environment that is being modeled.
Incorrect Answers:
B: F1 score also known as balanced F-score or F-measure is used to evaluate a classi+cation model.
D: aucROC or area under the curve (AUC) is used to evaluate a classi+cation model.
Reference:
https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/metrics

Question 2

Predicting how many vehicles will travel across a bridge on a given day is an example of _________.

A. classification
B. clustering
C. regression

Answer 2

C. regression

Regression is a machine learning task that is used to predict the value of the label from a set of related features. Reference:
https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/tasks

Question 3

You need to use Azure Machine Learning designer to build a model that will predict automobile prices.
Which type of modules should you use to complete the model? To answer, drag the appropriate modules to the correct locations. Each module may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.

Answer 3

Box 1: Select Columns in Dataset
For Columns to be cleaned, choose the columns that contain the missing values you want to change. You can choose multiple columns, but you must use the same replacement method in all selected columns.

Box 2: Split data -
Splitting data is a common task in machine learning. You will split your data into two separate datasets. One dataset will train the model and the other will test how well the model performed.

Box 3: Linear regression -
Because you want to predict price, which is a number, you can use a regression algorithm. For this example, you use a linear regression model.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-designer-automobile-price-train-score

Question 4

Which type of machine learning should you use to identify groups of people who have similar purchasing habits?

A. classification
B. regression
C. clustering

Answer 4

A. classification

Clustering is a machine learning task that is used to group instances of data into clusters that contain similar characteristics. Clustering can also be used to identify relationships in a dataset
Reference:
https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/tasks

Question 5

____________ can be used to predict the sale price of auctioned items.

A. Classification
B. Clustering
C. Regression

Answer 5

C. Regression

Regression is a machine learning task that is used to predict the value of the label from a set of related features.
Reference:
https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/tasks

Question 6

Which metric can you use to evaluate a classification model?

A. true positive rate
B. mean absolute error (MAE)
C. coefficient of determination (R2)
D. root mean squared error (RMSE)

Answer 6

A. true positive rate

What does a good model look like?
An ROC curve that approaches the top left corner with 100% true positive rate and 0% false positive rate will be the best model. A random model would display as a flat line from the bottom left to the top right corner. Worse than random would dip below the y=x line.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#classification

Question 7

Which two components can you drag onto a canvas in Azure Machine Learning designer? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. dataset
B. compute
C. pipeline
D. module

Answer 7

A. dataset
D. module

You can drag-and-drop datasets and modules onto the canvas.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer

Question 8

You need to create a training dataset and validation dataset from an existing dataset.
Which module in the Azure Machine Learning designer should you use?

A. Select Columns in Dataset
B. Add Rows
C. Split Data
D. Join Data

Answer 8

C. Split Data

A common way of evaluating a model is to divide the data into a training and test set by using Split Data, and then validate the model on the training data.
Use the Split Data module to divide a dataset into two distinct sets.
The studio currently supports training/validation data splits
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-configure-cross-validation-data-splits

Question 9

Question #59 Topic 1
DRAG DROP -
Match the types of machine learning to the appropriate scenarios.
To answer, drag the appropriate machine learning type from the column on the left to its scenario on the right. Each machine learning type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Select and Place:

Answer 9

Box 1: Regression -
In the most basic sense, regression refers to prediction of a numeric target.
Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable.
You use this module to de+ne a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions.
Box 2: Clustering -
Clustering, in machine learning, is a method of grouping data points into similar clusters. It is also called segmentation.
Over the years, many clustering algorithms have been developed. Almost all clustering algorithms use the features of individual items to +nd similar items. For example, you might apply clustering to +nd similar people by demographics. You might use clustering with text analysis to group sentences with similar topics or sentiment.
Box 3: Classi+cation -
Two-class classi+cation provides the answer to simple two-choice questions such as Yes/No or True/False. Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression

Question 10

Question #60
___________ is the calculated probability of a correct image classification.

Answer 10

Accuracy

Question 11

Question #61

Ensuring an AI system does not provide a prediction when important fields contain unusual or missing values is __________ principle for responsible AI.

A. an inclusiveness
B. a privacy and safety
C. a reliability and safety
D. a transparency

Answer 11

C. a reliability and safety

Reference: https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai

Question 12

Question #62

Ensuring that the numeric variables in training data on a similar scale is an example of ___________.

A. data ingestion
B. feature engineering
C. feature selection
D. model training

Answer 12

C. feature selection

Reference: https://docs.microsoft.com/en-us/azure/architecture/data-science-process/create-features

Question 13

Question #63

Assigning classes to images before training a classification model is an example of _________.

A. evaluation
B. feature engineering
C. hyperparameter tuning
D. labeling

Answer 13

D. labeling

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-label-data

Question 14

Question #64 Topic 1
HOTSPOT -
You have an Azure Machine Learning model that predicts product quality. The model has a training dataset that contains 50,000 records. A sample of the data is shown in the following table Exhibit A. For each of the following statements, select Yes if the statement is true. Otherwise, select No from Exhibit B.

Answer 14

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/component-reference/filter-based-feature-selection

Question 15

Question #65 Topic 1
HOTSPOT -
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

Answer 15

Reference: https://docs.microsoft.com/en-us/learn/modules/create-regression-model-azure-machine-learning-designer/5-create-training-pipeline https://docs.microsoft.com/en-us/learn/modules/create-classi+cation-model-azure-machine-learning-designer/introduction https://docs.microsoft.com/en-us/learn/modules/create-clustering-model-azure-machine-learning-designer/1-introduction

Question 16

Question #66 Topic 1
Which two actions are performed during the data ingestion and data preparation stage of an Azure Machine Learning process? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A. Calculate the accuracy of the model.
B. Score test data by using the model.
C. Combine multiple datasets.
D. Use the model for real-time predictions.
E. Remove records that have missing values.

Answer 16

C. Combine multiple datasets.
E. Remove records that have missing values.

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-data-ingestion https://docs.microsoft.com/en-us/azure/architecture/data- science-process/prepare-data

Question 17

Question #67
You need to predict the animal population of an area. Which Azure Machine Learning type should you use?

A. regression
B. clustering
C. classification

Answer 17

A. regression

Regression is a supervised machine learning technique used to predict numeric values.
Reference: https://docs.microsoft.com/en-us/learn/modules/create-regression-model-azure-machine-learning-designer/1-introduction

Question 18

Question #68 Topic 1
Which two languages can you use to write custom code for Azure Machine Learning designer? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

A. Python
B. R
C. C#
D. Scala

Answer 18

A. Python
B. R

Use Azure Machine Learning designer for customizing using Python and R code. Reference: https://azure.microsoft.com/en-us/services/machine-learning/designer/#features

Question 19

Question #69 Topic 1
HOTSPOT -
For each of the following statements, select Yes if the statement is true. Otherwise, select No.

Answer 19

Box1:Yes-
For regression problems, the label column must contain numeric data that represents the response variable. Ideally the numeric data represents a continuous scale.
Box2:No-
K-Means Clustering -
Because the K-means algorithm is an unsupervised learning method, a label column is optional.
If your data includes a label, you can use the label values to guide selection of the clusters and optimize the model. If your data has no label, the algorithm creates clusters representing possible categories, based solely on the data.
Box3:No-
For classi+cation problems, the label column must contain either categorical values or discrete values. Some examples might be a yes/no rating, a disease classi+cation code or name, or an income group. If you pick a noncategorical column, the component will return an error during training.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/component-reference/train-model https://docs.microsoft.com/en- us/azure/machine-learning/component-reference/k-means-clustering

Question 20

Question #70 Topic 1
Your company wants to build a recycling machine for bottles. The recycling machine must automatically identify bottles of the correct shape and reject all other items.
Which type of AI workload should the company use?

A. anomaly detection
B. conversational AI
C. computer vision
D. natural language processing

Answer 20

C. computer vision

Azure’s Computer Vision service gives you access to advanced algorithms that process images and return information based on the visual features you’re interested in. For example, Computer Vision can determine whether an image contains adult content, find specific brands or objects, or find human faces.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview

Question 21

Question #71
Topic 1
HOTSPOT -
For each of the following statements, select Yes if the statement is true. Otherwise, select No.

Answer 21

Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/get-started-build-detector

Question 22

Question #72
Topic 1
In which two scenarios can you use the Form Recognizer service? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

A. Extract the invoice number from an invoice.
B. Translate a form from French to English.
C. Find image of product in a catalog.
D. Identify the retailer from a receipt.

Answer 22

A. Extract the invoice number from an invoice.
D. Identify the retailer from a receipt.

Reference: https://azure.microsoft.com/en-gb/services/cognitive-services/form-recognizer/#features

Question 23

Question 73

Counting the number of animals in an area based on a video feed is an example of _________.

A. Forecasting
B. computer vision
C. conversational AI
D. anomaly detection

Answer 23

B. computer vision

Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview https://docs.microsoft.com/en-us/azure/cognitive- services/computer-vision/intro-to-spatial-analysis-public-preview

Question 24

Question #74
Topic 1
HOTSPOT -
You have a database that contains a list of employees and their photos.
You are tagging new photos of the employees.
For each of the following statements select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

Answer 24

Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/face/overview https://docs.microsoft.com/en-us/azure/cognitive- services/face/concepts/face-detection

Question 25

Question #75
Topic 1
You need to develop a mobile app for employees to scan and store their expenses while travelling. Which type of computer vision should you use?

A. semantic segmentation
B. image classification
C. object detection
D. optical character recognition (OCR)

Answer 25

D. optical character recognition (OCR)

Azure’s Computer Vision API includes Optical Character Recognition (OCR) capabilities that extract printed or handwritten text from images. You can extract text from images, such as photos of license plates or containers with serial numbers, as well as from documents - invoices, bills, +nancial reports, articles, and more.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-recognizing-text

Question 26

Question #76 Topic 1
HOTSPOT -
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Hot Area:

Answer 26

Box1:Yes-
Custom Vision functionality can be divided into two features. Image classification applies one or more labels to an image. Object detection is similar, but it also returns the coordinates in the image where the applied label(s) can be found.
Box2:Yes-
The Custom Vision service uses a machine learning algorithm to analyze images. You, the developer, submit groups of images that feature and lack the characteristics in question. You label the images yourself at the time of submission. Then, the algorithm trains to this data and calculates its own accuracy by testing itself on those same images.
Box3:No-
Custom Vision service can be used only on graphic files.
Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/Custom-Vision-Service/overview

Question 27

Question #77
Topic 1
You are processing photos of runners in a race.
You need to read the numbers on the runners’ shirts to identity the runners in the photos. Which type of computer vision should you use?

A. facial recognition
B. optical character recognition (OCR)
C. image classification
D. object detection

Answer 27

C. image classification

Optical character recognition (OCR) allows you to extract printed or handwritten text from images and documents. Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview-ocr

Question 28

Question #78

Match the types of machine learning to the appropriate scenarios.
To answer, drag the appropriate machine learning type from the column on the left to its scenario on the right. Each machine learning type may be used once, more than once, or not at all.

A. Facial detection
B. Facial recognition
C. Image classification
D. Object detection
E. Optical character recognition (OCR)
F. Semantic segmentation

1. Separate images of polar bears and brown bears
2. Determine the location of a bear in a photo
3. Determine which pixels in an image are part of a bear

Answer 28

1. Separate images of polar bears and brown bears – C. Image classification
2. Determine the location of a bear in a photo – D. Object detection
3. Determine which pixels in an image are part of a bear – F. Semantic segmentation

Question 29

Question #79 Topic 1

You use drones to identify where weeds grow between rows of crops to send an instruction for the removal of the weeds. This is an example of which type of computer vision?

A. object detection
B. optical character recognition (OCR) C. scene segmentation

Answer 29

A. object detection

Object detection is similar to tagging, but the API returns the bounding box coordinates for each tag applied. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image.
Incorrect Answers:
B: Optical character recognition (OCR) allows you to extract printed or handwritten text from images and documents.
C: Scene segmentation determines when a scene changes in video based on visual cues. A scene depicts a single event and it’s composed by a series of consecutive shots, which are semantically related.
Reference:
https://docs.microsoft.com/en-us/ai-builder/object-detection-overview https://docs.microsoft.com/en-us/azure/cognitive-services/computer- vision/overview-ocr https://docs.microsoft.com/en-us/azure/azure-video-analyzer/video-analyzer-for-media-docs/video-indexer-overview

Question 30

Question #80 Topic 1

Match the facial recognition tasks to the appropriate questions.
To answer, drag the appropriate task from the column on the left to its question on the right. Each task may be used once, more than once, or not at all.

A. grouping
B. identification
C. similarity
D. Verification

1. Do two images of a face belong to the same person?
2. Does this person looking like other people?
3. Do all the faces belong together?
4. Who is this person in thsi group of people?

Answer 30

1. Do two images of a face belong to the same person? – D. Verification
2. Does this person looking like other people? – C. similarity
3. Do all the faces belong together? – A. grouping
4. Who is this person in this group of people? – B. identification

Question 31

Question #81 Topic 1

Match the types of computer vision workloads to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.

A. Facial recognition
B. Image classification
C. Object detection
D. OCR

1. Identify celebrities in images.
2. Extract movie title names from movie poster images.
3. Locate vehicles in images.

Answer 31

1. Identify celebrities in images. – A. Facial recognition
2. Extract movie title names from movie poster images. – D. OCR
3. Locate vehicles in images. – C. Object detection

Question 32

Question #82

You need to determine the location of cars in an image so that you can estimate the distance between the cars.

Which type of computer vision should you use?

A. optical character recognition (OCR)
B. object detection
C. image classification
D. face detection

Answer 32

B. object detection

Object detection is similar to tagging, but the API returns the bounding box coordinates (in pixels) for each object found. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image. You can use this
what percentage of questions from this dump appeared in the real test?
functionality to process the relationships between the objects in an image. It also lets you determine whether there are multiple instances of
the same tag in an image.

https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection

Question 33

Question #83

You can use the _________ service to train an object detection model by using your own images.

A. Computer Vision
B. Custom Vision
C. Form Recognizer
D. Video Indexer

Answer 33

B. Custom Vision

Azure Custom Vision is a cognitive service that lets you build, deploy, and improve your own image classi+ers. An image classi+er is an AI service that applies labels (which represent classes) to images, according to their visual characteristics. Unlike the Computer Vision service, Custom Vision allows you to specify the labels to apply.
Note: The Custom Vision service uses a machine learning algorithm to apply labels to images. You, the developer, must submit groups of images that feature and lack the characteristics in question. You label the images yourself at the time of submission. Then the algorithm trains to this data and calculates its own accuracy by testing itself on those same images. Once the algorithm is trained, you can test, retrain, and eventually use it to classify new images according to the needs of your app. You can also export the model itself for orine use.
Incorrect Answers:
Computer Vision:
Azure’s Computer Vision service provides developers with access to advanced algorithms that process images and return information based on the visual features you’re interested in. For example, Computer Vision can determine whether an image contains adult content, +nd speci+c brands or objects, or +nd human faces.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/home

Question 34

Question #84 Topic 1
You send an image to a Computer Vision API and receive back the annotated image shown in the exhibit.

Which type of computer vision was used?

A. object detection
B. face detection
C. optical character recognition (OCR) D. image classification

Answer 34

A. object detection

Object detection is similar to tagging, but the API returns the bounding box coordinates (in pixels) for each object found. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image. You can use this functionality to process the relationships between the objects in an image. It also lets you determine whether there are multiple instances of the same tag in an image.
The Detect API applies tags based on the objects or living things identi+ed in the image. There is currently no formal relationship between the tagging taxonomy and the object detection taxonomy. At a conceptual level, the Detect API only +nds objects and living things, while the Tag API can also include contextual terms like “indoor”, which can’t be localized with bounding boxes.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection

Question 35

Question #85

What are two tasks that can be performed by using the Computer Vision service? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. Train a custom image classification model.
B. Detect faces in an image.
C. Recognize handwritten text.
D. Translate the text in an image between languages.

Answer 35

B. Detect faces in an image.
C. Recognize handwritten text.

B: Azure’s Computer Vision service provides developers with access to advanced algorithms that process images and return information based on the visual features you’re interested in. For example, Computer Vision can determine whether an image contains adult content, find specific brands or objects, or find human faces.

C: Computer Vision includes Optical Character Recognition (OCR) capabilities. You can use the new Read API to extract printed and handwritten text from images and documents.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/home

To be able to submit custom images you need Custom Vision instead of Computer Vision, so its not A
Computer Vision Service doesn’t allow to translate text, but it allows to identify text

Question 36

Question #86 Topic 1
What is a use case for classification?
A. predicting how many cups of coffee a person will drink based on how many hours the person slept the previous night.
B. analyzing the contents of images and grouping images that have similar colors
C. predicting whether someone uses a bicycle to travel to work based on the distance from home to work
D. predicting how many minutes it will take someone to run a race based on past race times

Answer 36

C. predicting whether someone uses a bicycle to travel to work based on the distance from home to work

Two-class classification provides the answer to simple two-choice questions such as Yes/No or True/False.
Incorrect Answers:
A: This is Regression.
B: This is Clustering.
D: This is Regression.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/linear-regression https://docs.microsoft.com/en- us/azure/machine-learning/studio-module-reference/machine-learning-initialize-model-clustering

Question 37

Question #87 Topic 1

What are two tasks that can be performed by using computer vision? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

A. Predict stock prices.
B. Detect brands in an image.
C. Detect the color scheme in an image D. Translate text between languages.
E. Extract key phrases.

Answer 37

B. Detect brands in an image.
C. Detect the color scheme in an image

B: Identify commercial brands in images or videos from a database of thousands of global logos. You can use this feature, for example, to discover which brands are most popular on social media or most prevalent in media product placement.
C: Analyze color usage within an image. Computer Vision can determine whether an image is black & white or color and, for color images, identify the dominant and accent colors.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview

Question 38

Question #88 Topic 1

You need to build an image tagging solution for social media that tags images of your friends automatically. Which Azure Cognitive Services service should you use?

A. Face
B. Form Recognizer
C. Text Analytics
D. Computer Vision

Answer 38

A. Face

Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/face/overview https://docs.microsoft.com/en-us/azure/cognitive- services/face/face-api-how-to-topics/howtodetectfacesinimage

Question 39

Question #89

In which two scenarios can you use the Form Recognizer service? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

A. Identify the retailer from a receipt
B. Translate from French to English
C. Extract the invoice number from an invoice
D. Find images of products in a catalog

Answer 39

A. Identify the retailer from a receipt
C. Extract the invoice number from an invoice

Reference: https://docs.microsoft.com/en-us/azure/applied-ai-services/form-recognizer/overview?tabs=v2-1

Question 40

Question #90 Topic 1
DRAG DROP -
Match the facial recognition tasks to the appropriate questions.

1. Grouping
2. Identification
3. Similarity
4. Verification

A. Do two images of a face belong to the same person?
B. Does this person look like other people?
C. Who is this person in this group of people?

Answer 40

A. Do two images of a face belong to the same person? – 4. Verification

B. Does this person look like other people? – 3. Similarity

C. Who is this person in this group of people? – 2. Identification

Box 1: veri+cation -
Identity veri+cation -
Modern enterprises and apps can use the Face identi+cation and Face veri+cation operations to verify that a user is who they claim to be.
Box 2: similarity -
The Find Similar operation does face matching between a target face and a set of candidate faces, +nding a smaller set of faces that look similar to the target face.
This is useful for doing a face search by image.
The service supports two working modes, matchPerson and matchFace. The matchPerson mode returns similar faces after +ltering for the same person by using the Verify API. The matchFace mode ignores the same-person +lter. It returns a list of similar candidate faces that may or may not belong to the same person.
Box 3: identi+cation -
Face identi+cation can address “one-to-many” matching of one face in an image to a set of faces in a secure repository. Match candidates are returned based on how closely their face data matches the query face. This scenario is used in granting building or airport access to a certain group of people or verifying the user of a device.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/face/overview

Question 41

Question #91 Topic 1

Which Computer Vision feature can you use to generate automatic captions for digital photographs?

A. Recognize text.
B. Identify the areas of interest.
C. Detect objects.
D. Describe the images.

Answer 41

D. Describe the images.

Question 42

Question #92 Topic 1
Which service should you use to extract text, key/value pairs, and table data automatically from scanned documents?

A. Custom Vision
B. Face
C. Form Recognizer
D. Language

Answer 42

C. Form Recognizer

Form Recognizer applies advanced machine learning to accurately extract text, key-value pairs, tables, and structures from documents.

Reference:
https://azure.microsoft.com/en-us/services/form-recognizer/

Question 43

Question #93

__________ extracts text from handwritten documents.

A. Object detection
B. Facial recognition
C. Image classification
D. OCR

Answer 43

D. OCR

Handwriting OCR (optical character recognition) is the process of automatically extracting handwritten information from paper, scans and other low-quality digital documents.
Reference:
https://vidado.ai/handwriting-ocr

Question 44

Question #94

You are developing a solution that uses the Text Analytics service.
You need to identify the main talking points in a collection of documents. Which type of natural language processing should you use?

A. entity recognition
B. key phrase extraction
C. sentiment analysis
D. language detection

Answer 44

B. key phrase extraction

Broad entity extraction: Identify important concepts in text, including key
Key phrase extraction/ Broad entity extraction: Identify important concepts in text, including key phrases and named entities such as people, places, and organizations.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing

Question 45

Question #95

In which two scenarios can you use speech recognition? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

A. an in-car system that reads text messages aloud
B. providing closed captions for recorded or live videos
C. creating an automated public address system for a train station
D. creating a transcript of a telephone call or meeting

Answer 45

B. providing closed captions for recorded or live videos

D. creating a transcript of a telephone call or meeting

https://azure.microsoft.com/en-gb/services/cognitive-services/speech-to-text/#features

Question 46

Question #96

When presenting at a conference, your session is transcribed into subtitles for the audience. This is an example of __________.

A. Sentiment analysis
B. Speech recognition
C. Speech synthesis
D. translation

Answer 46

B. Speech recognition

Reference: https://azure.microsoft.com/en-gb/services/cognitive-services/speech-to-text/#features

Question 47

Question #97 Topic 1

You need to build an app that will read recipe instructions aloud to support users who have reduced vision.

Which version service should you use?

A. Text Analytics
B. Translator
C. Speech
D. Language Understanding (LUIS)

Answer 47

C. Speech

Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/text-to-speech/#features

Question 48

Question

For each of the following statements, select Yes if the statement is true. Otherwise, select No.

You can use the speech service to transcribe a call to text.

You can use the text analytics service to extract key entities from a call transcript.

You can use the speech service to translate the audio of a call to a different language.

Answer 48

all are yes

Reference:
https://docs.microsoft.com/en-gb/azure/cognitive-services/text-analytics/overview https://azure.microsoft.com/en-gb/services/cognitive- services/speech-services/

Question 49

Question #99 Topic 1

Your website has a chatbot to assist customers.
You need to detect when a customer is upset based on what the customer types in the chatbot. Which type of AI workload should you use?

A. anomaly detection
B. computer vision
C. regression
D. natural language processing

Answer 49

D. natural language processing

Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization.
Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing

Question 50

Question #100 Topic 1
You plan to develop a bot that will enable users to query a knowledge base by using natural language processing. Which two services should you include in the solution? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A. QnA Maker
B. Azure Bot Service
C. Form Recognizer
D. Anomaly Detector

Answer 50

A. QnA Maker
B. Azure Bot Service

Reference: https://docs.microsoft.com/en-us/azure/bot-service/bot-service-overview-introduction?view=azure-bot-service-4.0 https://docs.microsoft.com/en-us/azure/cognitive-services/luis/choose-natural-language-processing-service

Question 51

Question #101 Topic 1
In which two scenarios can you use a speech synthesis solution? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

A. an automated voice that reads back a credit card number entered into a telephone by using a numeric keypad B. generating live captions for a news broadcast
C. extracting key phrases from the audio recording of a meeting
D. an AI character in a computer game that speaks audibly to a player

Answer 51

A. an automated voice that reads back a credit card number entered into a telephone by using a numeric keypad
D. an AI character in a computer game that speaks audibly to a player

Azure Text to Speech is a Speech service feature that converts text to lifelike speech.

Incorrect Answers:
C: Extracting key phrases is not speech synthesis.
Reference: https://azure.microsoft.com/en-in/services/cognitive-services/text-to-speech/

Question 52

Question #102 Topic 1
HOTSPOT -
For each of the following statements, select Yes if the statement is true. Otherwise, select No.

1. You can use the translator service to translate text between languages.
2. You can use the translator service to detect the language of a given text.
3. You can use the translator service to transcribe audible speech to text.

Answer 52

1. You can use the translator service to translate text between languages. – Y
2. You can use the translator service to detect the language of a given text. – Y
3. You can use the translator service to transcribe audible speech to text. – N

The translator service provides multi-language support for text translation, transliteration, language detection, and dictionaries. Speech-to-Text, also known as automatic speech recognition (ASR), is a feature of Speech Services that provides transcription. Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/Translator/translator-info-overview https://docs.microsoft.com/en- us/legal/cognitive-services/speech-service/speech-to-text/transparency-note

Question 53

Question 103

You need to scan the news for articles about your customers and alert employees when there is a negative article. Positive articles must be added
to a press book.

Which natural language processing tasks should you use to complete the process?

Each task may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.

Answer 53

Entity Recognition
Sentiment Analysis

Box 1: Entity recognition -
the Named Entity Recognition module in Machine Learning Studio (classic), to identify the names of things, such as people, companies, or locations in a column of text.
Named entity recognition is an important area of research in machine learning and natural language processing (NLP), because it can be used to answer many real-world questions, such as:
✑ Which companies were mentioned in a news article?
✑ Does a tweet contain the name of a person? Does the tweet also provide his current location?
✑ Were specified products mentioned in complaints or reviews?

Box 2: Sentiment Analysis -
The Text Analytics API’s Sentiment Analysis feature provides two ways for detecting positive and negative sentiment. If you send a Sentiment Analysis request, the API will return sentiment labels (such as “negative”, “neutral” and “positive”) and con+dence scores at the sentence and document-level.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/named-entity-recognition https://docs.microsoft.com/en- us/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-sentiment-analysis

Question 54

Question #104 Topic 1
You are building a knowledge base by using QnA Maker.
Which file format can you use to populate the knowledge base?

A. PPTX
B. XML
C. ZIP
D. PDF

Answer 54

D. PDF

D: Content types of documents you can add to a knowledge base:
Content types include many standard structured documents such as PDF, DOC, and TXT.
Note: The tool supports the following file formats for ingestion:
✑ .tsv: QnA contained in the format Question(tab)Answer.
✑ .txt, .docx, .pdf: QnA contained as regular FAQ content–that is, a sequence of questions and answers. Incorrect Answers:
A: PPTX is the default presentation +le format for new PowerPoint presentations.
B: It is not possible to ingest xml +le directly.
Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/concepts/data-sources-and-content