AI 900 (Microsoft)

Question 1

What is AI?

Answer 1

AI is the creation of software that imitates human behaviors and capabilities.

Question 2

AI Key workloads

Answer 2

Machine learning
Anomaly detection
Computer vision
Natural language processing
Knowledge mining

Question 3

Machine learning

Answer 3

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

Question 4

Anomaly detection

Answer 4

The capability to automatically detect errors or unusual activity in a system.

Question 5

Computer vision

Answer 5

The capability of software to interpret the world visually through cameras, video, and images.

Computer vision is one of the core areas of artificial intelligence (AI), and focuses on creating solutions that enable AI applications to “see” the world and make sense of it.

Question 6

Natural language processing

Answer 6

The capability for a computer to interpret written or spoken language, and respond in kind.

Question 7

Knowledge mining

Answer 7

The capability to extract information from large volumes of often unstructured data to create a searchable knowledge store.

Question 8

Understand machine learning

Answer 8

Machine Learning is the foundation for most AI solutions.

example :

Sustainable farming techniques are essential to maximize food production while protecting a fragile environment. The Yield, an agricultural technology company based in Australia, uses sensors, data and machine learning to help farmers make informed decisions related to weather, soil and plant conditions.

Question 9

Machine learning in Microsoft Azure

Answer 9

Microsoft Azure provides the Azure Machine Learning service - a cloud-based platform for creating, managing, and publishing machine learning models. Azure Machine Learning provides the following features and capabilities:

Question 10

Azure Machine Learning features and their Capabilities:

Answer 10

• 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 and compute management:

Cloud-based data storage and compute resources that professional data scientists can use to run data experiment code at scale.

• Pipelines:

Data scientists, software engineers, and IT operations professionals can define pipelines to orchestrate model training, deployment, and management tasks.

Question 11

Anomaly detection in Microsoft Azure

Answer 11

In Microsoft Azure, the Anomaly Detector service provides an application programming interface (API) that developers can use to create anomaly detection solutions.

Question 12

Computer Vision models and capabilities

Answer 12

• Image classification

Image classification 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.

• Object detection:

Object detection 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.

• Semantic segmentation:

Semantic segmentation is 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.

• Image analysis:

You can create solutions that combine machine learning models with advanced image analysis 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.

• Face detection, analysis, and recognition:

Face detection 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.

• Optical character recognition (OCR):

Optical character recognition 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.

Question 13

Computer vision services in Microsoft Azure

Answer 13

Microsoft Azure provides the following cognitive services to help you create computer vision solutions:

• Computer Vision:

You can use this service to analyze images and video, and extract descriptions, tags, objects, and text.

• Custom Vision:

Use this service to train custom image classification and object detection models using your own images.

• Face:

The Face service enables you to build face detection and facial recognition solutions.

• Form Recognizer:

Use this service to extract information from scanned forms and invoices.

Question 14

Understand natural language processing

Answer 14

Natural language processing (NLP) is the area of AI that deals with creating software that understands written and spoken language.

Question 15

NLP enables you to create software that can:

Answer 15

• 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 16

Natural language processing in Microsoft Azure

Services and Capabilities

Answer 16

In Microsoft Azure, you can use the following cognitive services to build natural language processing solutions:

• Language

Use this service to access features for understanding and analyzing text, training language models that can understand spoken or text-based commands, and building intelligent applications.

• Translator:

Use this service to translate text between more than 60 languages.

• Speech:

Use this service to recognize and synthesize speech, and to translate spoken languages.

• Azure Bot:

This service provides a platform for conversational AI, the capability of a software “agent” to participate in a conversation. Developers can use the Bot Framework to create a bot and manage it with Azure Bot Service - integrating back-end services like Language, and connecting to channels for web chat, email, Microsoft Teams, and others.

Question 17

Understand knowledge mining

Answer 17

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

Question 18

Knowledge mining in Microsoft Azure

Answer 18

One of these knowledge mining solutions is Azure Cognitive Search, 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.

Azure Cognitive Search can utilize the built-in AI capabilities of Azure Cognitive Services such as image processing, content extraction, and natural language processing to perform knowledge mining of documents.

Question 19

Fairness

Answer 19

AI systems should treat all people fairly.

Question 20

Reliability and safety

Answer 20

AI systems should perform reliably and safely.

Question 21

Privacy and security

Answer 21

AI systems should be secure and respect privacy.

Question 22

Inclusiveness

Answer 22

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.

Question 23

Transparency

Answer 23

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.

Question 24

Accountability

Answer 24

People should be accountable 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.

Question 25

What is machine learning?

Answer 25

Machine Learning is the foundation for most artificial intelligence solutions. Creating an intelligent solution often begins with the use of machine learning to train predictive models using historic data that you have collected.

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

Question 26

What is Azure Machine Learning?

Answer 26

Azure Machine Learning is a cloud service that you can use to train and manage machine learning models.

Question 27

Types of machine learning

Answer 27

There are two general approaches to machine learning, supervised and unsupervised machine learning. In both approaches, you train a model to make predictions.

• The supervised machine learning approach requires you to start with a dataset with known label values. Two types of supervised machine learning tasks include regression and classification.
• Regression: used to predict a continuous value; like a price, a sales total, or some other measure. Regression predicts a numeric label or outcome based on variables, or features.
• Classification: used to determine a binary class label; like whether a patient has diabetes or not.

Classification is a form of machine learning that is used to predict which category, or class, an item belongs to.

• The unsupervised machine learning approach starts with a dataset without known label values. One type of unsupervised machine learning task is clustering.
• Clustering: used to determine labels by grouping similar information into label groups; like grouping measurements from birds into species.

Question 28

What is Azure Machine Learning studio?

Answer 28

Training and deploying an effective machine learning model involves a lot of work, much of it time-consuming and resource-intensive. Azure Machine Learning is a cloud-based service that helps simplify some of the tasks it takes to prepare data, train a model, and deploy a predictive service.

Question 29

Azure Machine Learning workspace

Answer 29

To use Azure Machine Learning, you first create a workspace resource in your Azure subscription. You can then use this workspace to manage data, compute resources, code, models, and other artifacts related to your machine learning workloads.

Question 30

Azure Machine Learning studio

Answer 30

Azure Machine Learning studio is a web portal for machine learning solutions in Azure. It includes a wide range of features and capabilities that help data scientists prepare data, train models, publish predictive services, and monitor their usage.

Question 31

Azure Machine Learning compute

Answer 31

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

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

In Azure Machine Learning studio, you can manage the compute targets for your data science activities. There are four kinds of compute resource you can create:

• Compute Instances:
  Development workstations that data scientists can use to work with data and models.
• Compute Clusters:
  Scalable clusters of virtual machines for on-demand processing of experiment code.
• Inference Clusters:
  Deployment targets for predictive services that use your trained models.
• Attached Compute: Links to existing Azure compute resources, such as Virtual Machines or Azure Databricks clusters.

Question 32

What is Azure Automated Machine Learning?

Answer 32

Azure Machine Learning includes an automated machine learning capability that automatically tries multiple pre-processing techniques and model-training algorithms in parallel. These automated capabilities use the power of cloud computing to find the best-performing supervised machine learning model for your data.

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 hyperparameter tuning.

Question 33

The steps in a machine learning process

Answer 33

• 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 can deploy the model as an application on a server or device so that others can use it.

Question 34

Machine Learning process

Prepare data

Answer 34

Machine learning models must be trained with existing data. Data scientists expend a lot of effort exploring and pre-processing data, and trying various types of model-training algorithms to produce accurate models, which is time consuming, and often makes inefficient use of expensive compute hardware.

Question 35

Machine Learning process

Train model

Answer 35

The automated machine learning capability in Azure Machine Learning supports supervised machine 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:

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

Question 36

Machine Learning process

Evaluate performance

Answer 36

After the job has finished you can review the best performing model. In this case, you used exit criteria 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.

The best model is identified based on the evaluation metric you specified, Normalized root mean squared error.

https://learn.microsoft.com/en-us/training/modules/use-automated-machine-learning/5-machine-learning-steps

Question 37

Machine Learning process

Deploy a predictive service

Answer 37

In Azure Machine Learning, you can deploy a service as an Azure Container Instances (ACI) or to an Azure Kubernetes Service (AKS) cluster. For production scenarios, an AKS deployment is recommended, for which you must create an inference cluster compute target. In this exercise, you’ll use an ACI service, which is a suitable deployment target for testing, and does not require you to create an inference cluster

Question 37

Deploy a predictive service

Answer 37

In Azure Machine Learning, you can deploy a service as an Azure Container Instances (ACI) or to an Azure Kubernetes Service (AKS) cluster. For production scenarios, an AKS deployment is recommended, for which you must create an inference cluster compute target. In this exercise, you’ll use an ACI service, which is a suitable deployment target for testing, and does not require you to create an inference cluster

Question 38

Some potential uses for computer vision include:

Answer 38

• Content Organization: Identify people or objects in photos and organize them based on that identification. Photo recognition applications like this are commonly used in photo storage and social media applications.
• Text Extraction: Analyze images and PDF documents that contain text and extract the text into a structured format.
• Spatial Analysis: Identify people or objects, such as cars, in a space and map their movement within that space.

Question 39

Learning objectives

Answer 39

• Identify image analysis tasks that can be performed with the Computer Vision service.
• Provision a Computer Vision resource.
• Use a Computer Vision resource to analyze an image.

Question 40

The Computer Vision service in Microsoft Azure

Answer 40

The Computer Vision service is a cognitive service in Microsoft Azure that provides pre-built computer vision capabilities. The service can analyze images, and return detailed information about an image and the objects it depicts.

Question 41

Azure resources for Computer Vision

Answer 41

Resource types:

• Computer Vision:
  A specific resource for the Computer Vision service. Use this resource type if you don’t intend to use any other cognitive services, or if you want to track utilization and costs for your Computer Vision resource separately.
• Cognitive Services:
  A general cognitive services resource that includes Computer Vision along with many other cognitive services; such as Text Analytics, Translator Text, and others. Use this resource type if you plan to use multiple cognitive services and want to simplify administration and development.

Whichever type of resource you choose to create, it will provide two pieces of information that you will need to use it:

• A key:
  that is used to authenticate client applications.
• An endpoint that provides the HTTP address at which your resource can be accessed.

Question 42

Analyzing images with the Computer Vision service

Answer 42

• Describing an image
• Tagging visual features
• Detecting objects
• Detecting brands
• Detecting faces
• Categorizing an image
• Detecting domain-specific content
• Optical character recognition
• Additional capabilities:
  • Detect image types
  • Detect image color
    schemes
  • Generate thumbnails
  • Moderate content

Question 43

What is Azure Machine Learning designer?

Answer 43

There are several ways to author regression machine learning models. One way is to use a visual interface called designer 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.

Question 44

What is Azure Machine Learning designer?

Answer 44

There are several ways to author regression machine learning models. One way is to use a visual interface called designer 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.

Each designer project, known as a pipeline, has a left panel for navigation and a canvas on your right hand side.

To use designer, identify the building blocks, or components, needed for your model, place and connect them on your canvas, and run a machine learning job.

Question 45

Pipelines

Answer 45

Pipelines let you organize, manage, and reuse complex machine learning workflows across projects and users. A pipeline starts with the dataset from which you want to train the model. Each time you run a pipeline, the configuration of the pipeline and its results are stored in your workspace as a pipeline job.

Question 46

Components

Answer 46

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

Question 47

Metrics can be derived from the confusion matrix include:

Answer 47

• Accuracy: The ratio of correct predictions (true positives + true negatives) to the total number of predictions.
• Precision: The fraction 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).
• Recall: The fraction of positive cases correctly identified (the number of true positives divided by the number of true positives plus false negatives).
• F1 Score: An overall metric that essentially combines precision and recall.

Suppose that only 3% of the population is diabetic. You could create a model that always predicts 0 and it would be 97% accurate, but it would not help correctly predict cases of diabetes. For this reason, most data scientists use other metrics like precision and recall to assess classification model performance.

Question 48

Choosing a threshold

Answer 48

A classification model predicts the probability for each possible class. In other words, the model calculates a likelihood for each predicted label. In the case of a binary classification model, the predicted probability is a value between 0 and 1. By default, a predicted probability including or above 0.5 results in a class prediction of 1, while a prediction below this threshold means that there’s a greater probability of a negative prediction (remember that the probabilities for all classes add up to 1), so the predicted class would be 0.

Question 49

ROC curve and AUC metric

(Classification)

Answer 49

Another term for recall is True positive rate, and it has a corresponding metric named False positive rate, which measures the number of negative cases incorrectly identified as positive compared the number of actual negative cases. Plotting these metrics against each other for every possible threshold value between 0 and 1 results in a curve, known as the ROC curve (ROC stands for receiver operating characteristic, but most data scientists just call it a ROC curve). 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 the area under the curve, of AUC metric, (which can be any value from 0 to 1), the better the model is performing. You can review the ROC curve in Evaluation Results.

Question 50

Inference pipeline

Answer 50

To deploy 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.

Question 51

Deployment

Answer 51

After creating the inference pipeline, you can deploy it as an endpoint. In the endpoints 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.

It will take a while for your endpoint to be deployed. The Deployment state on the Details tab will indicate Healthy when deployment is successful.

Question 52

Train model in Clustering

Answer 52

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

The K-Means algorithm works by:

1.Initializing K coordinates as randomly selected points called centroids in n-dimensional space (where n is the number of dimensions in the feature vectors).

2. Plotting the feature vectors as points in the same space, and assigning each point to its closest centroid.
3. Moving the centroids to the middle of the points allocated to it (based on the mean distance).
4. Reassigning the points to their closest centroid after the move.
5. Repeating steps 3 and 4 until the cluster allocations stabilize or the specified number of iterations has completed.

Question 53

Some potential uses for computational visual research include:

Answer 53

1- Content organization: Identify people or objects in photos and organize them based on this identification. Photo recognition apps like this are commonly used in photo storage and social media apps.

2- Text extraction: Analyze images and PDF documents that contain text and extract text in a structured format.

3- Spatial analysis: Identify people or objects, such as cars, in a space and map their movement within that space.

Question 54

Azure resources for Computer Vision

Answer 54

• Computer Vision: A specific resource for the Computer Vision service. Use this resource type if you don’t intend to use any other cognitive services, or if you want to track utilization and costs for your
  Computer Vision resource separately.
• Cognitive Services: A general cognitive services resource that includes Computer Vision along with many other cognitive services; such as Text Analytics, Translator Text, and others. Use this resource type if you plan to use multiple cognitive services and want to simplify administration and development.

Whichever type of resource you choose to create, it will provide two pieces of

information that you will need to use it:

1- A key that is used to authenticate client applications.
2- An endpoint that provides the HTTP address at which your resource can be accessed.

Question 55

Analyzing images with the Computer Vision service

Answer 55

1 - Describing an image

2 - Tagging visual features

3 - Detecting objects

4 - Detecting brands

5 - Detecting faces

6 - Categorizing an image

7 - Detecting domain-specific content
* Celebrities
* Landmarks
8 - Optical character recognition

9 - Additional capabilities
Detect image types
Detect image color schemes
Generate thumbnails
Moderate content

Question 56

Some potential uses for image classification include:

Answer 56

1. Product identification: performing visual searches for specific products in online searches or even, in-store using a mobile device.
2. 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.
3. 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 56

Some potential uses for image classification include:

Answer 56

1. Product identification: performing visual searches for specific products in online searches or even, in-store using a mobile device.
2. 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.
3. 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 57

Understand image classification

Answer 57

Image classification is a machine learning technique in which the object being classified is an image, such as a photograph.

To create an image classification model, you need data that consists of features and their labels. The existing data is a set of categorized images.

Question 58

Azure’s Custom Vision service

Answer 58

Most modern image classification solutions are based on deep learning techniques that make use of convolutional neural networks (CNNs) to uncover patterns in the pixels that correspond to particular classes. Training an effective CNN is a complex task that requires considerable expertise in data science and machine learning.

Common techniques used to train image classification models have been encapsulated into the Custom Vision cognitive service in Microsoft Azure;

You can use the Custom Vision cognitive service to train image classification models and deploy them as services for applications to use.

Question 59

Custom Vision

Answer 59

A dedicated resource for the custom vision service, which can be training, a prediction, or both resources.

Question 60

Cognitive Services:

Answer 60

A general cognitive services resource that includes Custom Vision along with many other cognitive services. You can use this type of resource for training, prediction, or both.

Question 61

You plan to use the Custom Vision service to train an image classification model. You want to create a resource that can only be used for model training, and not for prediction. Which kind of resource should you create in your Azure subscription?

Answer 61

Computer Vision

Question 62

Using the model for prediction

Answer 62

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

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

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

3 - Prediction endpoint: The HTTP address of the endpoints for the prediction resource to which you published the model (not the training resource).

4 - Prediction key: The authentication key for the prediction resource to which you published the model (not the training resource).

Question 63

Detect objects in images with the Custom Vision service

Answer 63

Object detection is a form of machine learning based computer vision in which a model is trained to recognize individual types of objects in an image, and to identify their location in the image.

You can create an object detection machine learning model by using advanced deep learning techniques.

Question 64

Uses of object detection

Answer 64

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

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

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

Question 65

An object detection model returns the following information:

Answer 65

The class of each object identified in the image.
The probability score of the object classification (which you can interpret as the confidence of the predicted class being correct)
The coordinates of a bounding box for each object.