MS Learn Practice Assessment

Question 1

Question 1 of 50

You need to identify numerical values that represent the probability of humans developing diabetes based on age and body fat percentage.
Which type of machine learning model should you use?
Select only one answer.

hierarchical clustering

linear regression
This answer is incorrect.

logistic regression

multiple linear regression

Answer 1

logistic regression

Multiple linear regression models a relationship between two or more features and a single label. Linear regression uses a single feature. Logistic regression is a type of classification model, which returns either a Boolean value or a categorical decision. Hierarchical clustering groups data points that have similar characteristics.
Fundamentals of machine learning - Training | Microsoft Learn https://learn.microsoft.com/training/modules/fundamentals-machine-learning/
What are classification models? - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/understand-classification-machine-learning/2-what-is-classification

Question 2

Question 2 of 50

Which type of machine learning algorithm finds the optimal way to split a dataset into groups without relying on training and validating label predictions?
Select only one answer.

classification

clustering

regression

supervised

Answer 2

missed the answer

Question 3

Question 1 of 50

Which type of artificial intelligence (AI) workload provides the ability to classify individual pixels in an image depending on the object that they represent?
Select only one answer.

image analysis

image classification

object detection

semantic segmentation

Answer 3

semantic segmentation

Semantic segmentation provides the ability to classify individual pixels in an image depending on the object that they represent. The other answer choices also process images, but their outcomes are different.
Understand computer vision - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/get-started-ai-fundamentals/4-understand-computer-vision

Question 4

Question 2 of 50

Which AI service can be integrated into chat applications and generate content in the form of text?
Select only one answer.

Azure AI Language

Azure AI Metrics Advisor

Azure AI Vision

Azure OpenAI

Answer 4

Azure OpenAI

Azure OpenAI is the only service capable of generating text that can be used in chat applications to create conversational experiences. The other workloads are Azure Cognitive Services used for different purposes, but not for generating text used in chat applications.
Understand generative AI - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/get-started-ai-fundamentals/6-understand-generative-ai

Question 5

Question 3 of 50

Which artificial intelligence (AI) workload scenario is an example of natural language processing (NLP)?
Select only one answer.

extracting key phrases from a business insights report

identifying objects in landscape images

monitoring for sudden increases in quantity of failed sign-in attempts

predicting whether customers are likely to buy a product based on previous purchases

Answer 5

extracting key phrases from a business insights report

predicting whether customers are likely to buy a product based on previous purchases
Extracting key phrases from text to identify the main terms is an NLP workload. Predicting whether customers are likely to buy a product based on previous purchases requires the development of a machine learning model. Monitoring for sudden increases in quantity of failed sign-in attempts is a different workload. Identifying objects in landscape images is a computer vision workload.
Analyze text with the Language service - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/analyze-text-with-text-analytics-service/

Question 6

Question 4 of 50

Which two artificial intelligence (AI) workload scenarios are examples of natural language processing (NLP)? Each correct answer presents a complete solution.
Select all answers that apply.

extracting handwritten text from online images

generating tags and descriptions for images

monitoring network traffic for sudden spikes

performing sentiment analysis on social media data

translating text between different languages from product reviews

Answer 6

performing sentiment analysis on social media data

translating text between different languages from product reviews

Translating text between different languages from product reviews is an NLP workload that uses the Azure AI Translator service and is part of Azure AI Services. It can provide text translation of supported languages in real time. Performing sentiment analysis on social media data is an NLP that uses the sentiment analysis feature of the Azure AI Service for Language. It can provide sentiment labels, such as negative, neutral, and positive for text-based sentences and documents.
Microsoft Azure AI Fundamentals: Explore natural language processing - Training | Microsoft Learn - https://learn.microsoft.com/training/paths/explore-natural-language-processing/

Question 7

Question 5 of 50

Which two artificial intelligence (AI) workload features are part of the Azure AI Vision service? Each correct answer presents a complete solution.
Select all answers that apply.

entity recognition

key phrase extraction

optical character recognition (OCR)

sentiment analysis

spatial analysis

Answer 7

optical character recognition (OCR)

spatial analysis

OCR and Spatial Analysis are part of the Azure AI Vision service. Sentiment analysis, entity recognition, and key phrase extraction are not part of the computer vision service.
Microsoft Azure AI Fundamentals: Explore computer vision – Training | Microsoft Learn - https://learn.microsoft.com/training/paths/explore-computer-vision-microsoft-azure/

Question 8

Question 6 of 50

Which principle of responsible artificial intelligence (AI) defines the framework of governance and organization principles that meet ethical and legal standards of AI solutions?
Select only one answer.

accountability

fairness

inclusiveness

transparency

Answer 8

accountability

Accountability defines the framework of governance and organizational principles, which are meant to ensure that AI solutions meet ethical and legal standards that are clearly defined. The other answer choices do not define the framework of governance and organization principles, but provide guidance regarding the ethical and legal aspects of the corresponding standards.
Understand Responsible AI - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/get-started-ai-fundamentals/8-understand-responsible-ai

Question 9

Question 7 of 50

Which principle of responsible artificial intelligence (AI) plays the primary role when implementing an AI solution that meet qualifications for business loan approvals?
Select only one answer.

accountability

fairness

inclusiveness

safety

Answer 9

fairness

Fairness is meant to ensure that AI models do not unintentionally incorporate a bias based on criteria such as gender or ethnicity. Transparency does not apply in this case since banks commonly use their proprietary models when processing loan approvals. Inclusiveness is also out of scope since not everyone is qualified for a loan. Safety is not a primary consideration since there is no direct threat to human life or health in this case.
Understand Responsible AI - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/get-started-ai-fundamentals/8-understand-responsible-ai

Question 10

Question 8 of 50

Which principle of responsible artificial intelligence (AI) is applied in the design of an AI system to ensure that users understand constraints and limitations of AI?
Select only one answer.

fairness

inclusiveness

privacy and security

transparency

Answer 10

transparency
This answer is correct.
The transparency principle states that AI systems must be designed in such a way that users are made fully aware of the purpose of the systems, how they work, and which limitations can be expected during use. The inclusiveness principle states that AI systems must empower people in a positive and engaging way. Fairness is applied to AI systems to ensure that users of the systems are treated fairly. The privacy and security principle are applied to the design of AI systems to ensure that the systems are secure and to respect user privacy.
Understand Responsible AI - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/get-started-ai-fundamentals/8-understand-responsible-ai

Question 11

Question 9 of 50

Which two principles of responsible artificial intelligence (AI) are most important when designing an AI system to manage healthcare data? Each correct answer presents part of the solution.
Select all answers that apply.

accountability

fairness

inclusiveness

privacy and security

Answer 11

accountability

privacy and security

The accountability principle states that AI systems are designed to meet any ethical and legal standards that are applicable. The system must be designed to ensure that privacy of the healthcare data is of the highest importance, including anonymizing data where applicable. The fairness principle is applied to AI systems to ensure that users of the systems are treated fairly. The inclusiveness principle states that AI systems must empower people in a positive and engaging way.
Understand Responsible AI - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/get-started-ai-fundamentals/8-understand-responsible-ai

Question 12

Question 10 of 50

Which principle of responsible artificial intelligence (AI) ensures that an AI system meets any legal and ethical standards it must abide by?
Select only one answer.

accountability

fairness

inclusiveness

privacy and security

Answer 12

accountability

The accountability principle ensures that AI systems are designed to meet any ethical and legal standards that are applicable. The privacy and security principle states that AI systems must be designed to protect any personal and/or sensitive data. The inclusiveness principle states that AI systems must empower people in a positive and engaging way. The fairness principle is applied to AI system to ensure that users of the systems are treated fairly.
Microsoft Azure AI Fundamentals: Explore computer vision - Training | Microsoft Learn - https://learn.microsoft.com/training/paths/explore-computer-vision-microsoft-azure/
Understand Responsible AI - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/get-started-ai-fundamentals/8-understand-responsible-ai

Question 13

Question 11 of 50

Which artificial intelligence (AI) technique serves as the foundation for modern image classification solutions?
Select only one answer.

semantic segmentation

deep learning

linear regression

multiple linear regression

Answer 13

deep learning

Modern image classification solutions are based on deep learning techniques. Semantic segmentation provides the ability to classify individual pixels in an image depending on the object that they represent. Both linear regression and multiple linear regression use training and validating predictions to predict numeric values, so they are not part of image classification solutions.
Machine learning for computer vision - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/analyze-images-computer-vision/2b-computer-vision-models
Fundamentals of machine learning - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/fundamentals-machine-learning/

Question 14

Question 12 of 50

Which computer vision solution provides the ability to identify a person’s age based on a photograph?
Select only one answer.

facial detection

image classification

object detection

semantic segmentation

Answer 14

facial detection

Facial detection provides the ability to detect and analyze human faces in an image, including identifying a person’s age based on a photograph. Image classification classifies images based on their contents. Object detection provides the ability to generate bounding boxes identifying the locations of different types of vehicles in an image. Semantic segmentation provides the ability to classify individual pixels in an image.
Get started with image analysis on Azure - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/analyze-images-computer-vision/2-image-analysis-azure
Understand computer vision - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/get-started-ai-fundamentals/4-understand-computer-vision

Question 15

Question 13 of 50

Which process allows you to use optical character recognition (OCR)?
Select only one answer.

digitizing medical records

identifying access control for a laptop

identifying wildlife in an image

translating speech to text

Answer 15

digitizing medical records

OCR can extract printed or handwritten text from images. In this case, it can be used to extract text from scanned medical records to produce a digital archive from paper-based documents. Identifying wildlife in an image is an example of a computer vision solution that uses object detection and is not suitable for OCR. Identifying a user requesting access to a laptop is done by taking images from the laptop’s webcam and using facial detection and recognition to identify the user requesting access. Translating speech to text is an example of using speech translation and uses the Azure AI Speech service as part of Azure AI Services.
Read text with the Computer Vision service - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/read-text-computer-vision/

Question 16

Question 14 of 50

Which process allows you to use object detection?
Select only one answer.

analyzing sentiment around news articles

extracting text from manuscripts

granting employee access to a secure building

tracking livestock in a field

Answer 16

tracking livestock in a field

Object detection can be used to track livestock animals, such as cows, to support their safety and welfare. For example, a farmer can track whether a particular animal has not been mobile. Sentiment analysis is used to return a numeric value based on the analysis of a text. Employee access to a secure building can be achieved by using facial recognition. Extracting text from manuscripts is an example of a computer vision solution that uses optical character recognition (OCR).
Machine learning for computer vision - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/analyze-images-computer-vision/2b-computer-vision-models

Question 17

Question 15 of 50

You have a set of images. Each image shows one type of bone fracture. What allows you to identify bone fractures in different X-ray images?
Select only one answer.

conversational artificial intelligence (AI)

facial detection

image classification

object detection

Answer 17

image classification

Image classification is part of computer vision and can be used to evaluate images from an X-ray machine to quickly classify specific bone fracture types. This helps improve diagnosis and treatment plans. An image classification model is trained to facilitate the categorizing of the bone fractures. Object detection is used to return identified objects in an image, such as a cat, person, or chair. Conversational AI is used to create intelligent bots that can interact with people by using natural language. Facial detection is used to detect the location of human faces in an image.
Machine learning for computer vision - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/analyze-images-computer-vision/2b-computer-vision-models

Question 18

Question 16 of 50

Which two specialized domain models are supported by using the Azure AI Vision service? Each correct answer presents a complete solution.
Select all answers that apply.

animals

cars

celebrities

landmarks

plants

Answer 18

celebrities

landmarks

The Azure AI Vision service supports the celebrities and landmarks specialized domain models. It does not support specialized domain models for animals, cars, or plants.
Get started with image analysis on Azure - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/analyze-images-computer-vision/2-image-analysis-azure

Question 19

Question 17 of 50

Which additional piece of information is included with each phrase returned by an image description task of the Azure AI Vision?
Select only one answer.

bounding box coordinates

confidence score

endpoint

key

Answer 19

confidence score

Each phrase returned by an image description task of the Azure AI Vision includes the confidence score. An endpoint and a key must be provided to access the Azure AI Vision service. Bounding box coordinates are returned by services such as object detection, but not image description.
Get started with image analysis on Azure - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/analyze-images-computer-vision/2-image-analysis-azure

Question 20

Question 18 of 50

Which two prebuilt models allow you to use the Azure AI Document Intelligence service to scan information from international passports and sales accounts? Each correct answer presents part of the solution.
Select all answers that apply.

business card model

ID document model

invoice model

language model

receipt model

Answer 20

ID document model

invoice model

The invoice model extracts key information from sales invoices and is suitable for extracting information from sales account documents. The ID document model is optimized to analyze and extract key information from US driver’s licenses and international passport biographical pages. The business card model, receipt model, and language model are not suitable to extract information from passports or sales account documents.
Analyze receipts with the Form Recognizer service - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/analyze-receipts-form-recognizer/
Document processing models - Form Recognizer - Azure Applied AI Services | Microsoft Learn - https://learn.microsoft.com/azure/applied-ai-services/form-recognizer/concept-model-overview?view=form-recog-3.0.0

Question 21

Question 19 of 50

When using the Face Detect API of the Azure AI Face service, which feature helps identify whether a human face has glasses or headwear?
Select only one answer.

face attributes

face ID

face landmarks

face rectangle

Answer 21

face attributes

Face attributes are a set of features that can be detected by the Face Detect API. Attributes such as accessories (glasses, mask, headwear etc.) can be detected. Face rectangle, face ID, and face landmarks do not allow you to determine whether a person is wearing glasses or headwear.
What is the Azure Face service? - Azure Cognitive Services | Microsoft Learn - https://learn.microsoft.com/azure/cognitive-services/computer-vision/overview-identity
Detect and analyze faces with the Face service - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/detect-analyze-faces/

Question 22

Question 20 of 50

When using the Azure AI Face service, what should you use to perform one-to-many or one-to-one face matching? Each correct answer presents a complete solution.
Select all answers that apply.

Custom Vision

face attributes

face identification

face verification

find similar faces

Answer 22

face identification

face verification

Face identification in the Azure AI Face service can address one-to-many matching of one face in an image to a set of faces in a secure repository. Face verification has the capability for one-to-one matching of a face in an image to a single face from a secure repository or a photo to verify whether they are the same individual. Face attributes, the find similar faces operation, and Azure AI Custom Vision do not verify the identity of a face.
What is the Azure Face service? - Azure Cognitive Services | Microsoft Learn - https://learn.microsoft.com/azure/cognitive-services/computer-vision/overview-identity
Detect and analyze faces with the Face service - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/detect-analyze-faces/

Question 23

Question 21 of 50

Which natural language processing (NLP) technique normalizes words before counting them?
Select only one answer.

frequency analysis

N-grams

stemming

vectorization

Answer 23

stemming

Stemming normalizes words before counting them. Frequency analysis counts how often a word appears in a text. N-grams extend frequency analysis to include multi-term phrases. Vectorization captures semantic relationships between words by assigning them to locations in n-dimensional space.
Understand Text Analytics - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/analyze-text-with-text-analytics-service/2-understand-text-analytics

Question 24

Question 22 of 50

Which natural language processing (NLP) technique assigns values to words such as plant and flower, so that they are considered closer to each other than a word such as airplane?
Select only one answer.

frequency analysis

lemmatization

N-grams

vectorization

Answer 24

vectorization

Vectorization captures semantic relationships between words by assigning them to locations in n-dimensional space. Lemmatization, also known as stemming, normalizes words before counting them. Frequency analysis counts how often a word appears in a text. N-grams extend frequency analysis to include multi-term phrases.
Understand Text Analytics - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/analyze-text-with-text-analytics-service/2-understand-text-analytics

Question 25

Question 23 of 50

What is the confidence score returned by the Azure AI Language detection service of natural language processing (NLP) for an unknown language name?
Select only one answer.

1

-1

NaN

Unknown

Answer 25

NaN

NaN, or not a number, designates an unknown confidence score. Unknown is a value with which the NaN confidence score is associated. The score values range between 0 and 1, with 0 designating the lowest confidence score and 1 designating the highest confidence score.
Get started with text analysis - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/analyze-text-with-text-analytics-service/2-get-started-azure

Question 26

Question 24 of 50

Which part of speech synthesis in natural language processing (NLP) involves breaking text into individual words such that each word can be assigned phonetic sounds?
Select only one answer.

lemmatization

key phrase extraction

tokenization

transcribing

Answer 26

tokenization

Tokenization is part of speech synthesis that involves breaking text into individual words such that each word can be assigned phonetic sounds. Transcribing is part of speech recognition, which involves converting speech into a text representation. Key phrase extraction is part of language processing, not speech synthesis. Lemmatization, also known as stemming, is part of language processing, not speech synthesis.
Recognize and synthesize speech - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/recognize-synthesize-speech/

Question 27

Question 25 of 50

Which two features of Azure AI Services allow you to identify issues from support question data, as well as identify any people and products that are mentioned? Each correct answer presents part of the solution.
Select all answers that apply.

Azure AI Bot Service

Conversational Language Understanding

key phrase extraction

named entity recognition

Azure AI Speech service

Answer 27

key phrase extraction

Azure AI Speech service

Key phrase extraction is used to extract key phrases to identify the main concepts in a text. It enables a company to identify the main talking points from the support question data and allows them to identify common issues. Named entity recognition can identify and categorize entities in unstructured text, such as people, places, organizations, and quantities. The Azure AI Speech service, Conversational Language Understanding, and Azure AI Bot Service are not designed for identifying key phrases or entities.
Key Phrase Extraction cognitive skill – Azure Cognitive Search | Microsoft Learn - https://learn.microsoft.com/azure/search/cognitive-search-skill-keyphrases
Extract insights from text with the Language service – Training | Microsoft Learn - https://learn.microsoft.com/training/modules/extract-insights-text-with-text-analytics-service/
Analyze text with the Language service – Training | Microsoft Learn - https://learn.microsoft.com/training/modules/analyze-text-with-text-analytics-service/

Question 28

Question 26 of 50

Which feature of the Azure AI Language service includes functionality that returns links to external websites to disambiguate terms identified in a text?
Select only one answer.

entity recognition

key phrase extraction

language detection

sentiment analysis

Answer 28

entity recognition

Entity recognition includes the entity linking functionality that returns links to external websites to disambiguate terms (entities) identified in a text. Key phrase extraction evaluates the text of a document and identifies its main talking points. Azure AI Language detection identifies the language in which text is written. Sentiment analysis evaluates text and returns sentiment scores and labels for each sentence.
Get started with text analysis - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/analyze-text-with-text-analytics-service/2-get-started-azure

Question 29

Question 27 of 50

Which type of translation does the Azure AI Translator service support?
Select only one answer.

speech-to-speech

speech-to-text

text-to-speech

text-to-text

Answer 29

text-to-text

The Azure AI Translator service supports text-to-text translation, but it does not support speech-to-text, text-to-speech, or speech-to-speech translation.
Get started with translation in Azure - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/translate-text-with-translation-service/2-get-started-azure

Question 30

Question 28 of 50

Which three features are elements of the Azure AI Language Service? Each correct answer presents a complete solution.
Select all answers that apply.

Azure AI Vision

Azure AI Content Moderator

Entity Linking

Personally Identifiable Information (PII) detection

Sentiment analysis

Answer 30

Entity Linking

Personally Identifiable Information (PII) detection

Sentiment analysis

Entity Linking, PII detection, and sentiment analysis are all elements of the Azure AI Service for Azure AI Language. Azure AI Vision deals with image processing. Azure AI Content Moderator is an Azure AI Services service that is used to check text, image, and video content for material that is potentially offensive.
What is Azure Cognitive Service for Language - Azure Cognitive Services | Microsoft Learn - https://learn.microsoft.com/azure/cognitive-services/language-service/overview
Microsoft Azure AI Fundamentals: Explore natural language processing - Training | Microsoft Learn - https://learn.microsoft.com/training/paths/explore-natural-language-processing/

Question 31

Question 29 of 50

When using the Azure AI Service for Language, what should you use to provide further information online about entities extracted from a text?
Select only one answer.

entity linking

key phrase extraction

named entity recognition

text translation

Answer 31

entity linking

Entity Linking identifies and disambiguates the identity of entities found in a text. Key phrase extraction is not used to extract entities and is used instead to extract key phrases to identify the main concepts in a text. Named entity recognition cannot provide a link for each entity to view further information. Text translation is part of the Azure AI Translator service.
What is entity linking in Azure Cognitive Service for Language? - Azure Cognitive Services | Microsoft Learn - https://learn.microsoft.com/azure/cognitive-services/language-service/entity-linking/overview
Analyze text with the Language service - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/analyze-text-with-text-analytics-service/

Question 32

Question 30 of 50

Which three sources can be used to generate questions and answers for a knowledge base? Each correct answer presents a complete solution.
Select all answers that apply.

a webpage

an audio file

an existing FAQ document

an image file

manually entered data

Answer 32

a webpage

an existing FAQ document

manually entered data

A webpage or an existing document, such as a text file containing question and answer pairs, can be used to generate a knowledge base. You can also manually enter the knowledge base question-and-answer pairs. You cannot directly use an image or an audio file to import a knowledge base.
Build a bot with the Language Service and Azure Bot Service - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/build-faq-chatbot-qna-maker-azure-bot-service/

Question 33

Question 31 of 50

Select the answer that correctly completes the sentence.
____________ use plugins to provide end users with the ability to get help with common tasks from a generative AI model.
Select only one answer.

Copilots

Language Understanding solutions

Question answering models

RESTful API services

Answer 33

Copilots

Copilots are often integrated into applications to provide a way for users to get help with common tasks from a generative AI model. Copilots are based on a common architecture, so developers can build custom copilots for various business-specific applications and services.
What are copilots? - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/fundamentals-generative-ai/5-copilots

Question 34

Question 32 of 50

At which layer can you apply content filters to suppress prompts and responses for a responsible generative AI solution?
Select only one answer.

metaprompt and grounding

model

safety system

user experience

Answer 34

safety system

The safety system layer includes platform-level configurations and capabilities that help mitigate harm. For example, the Azure OpenAI service includes support for content filters that apply criteria to suppress prompts and responses based on the classification of content into four severity levels (safe, low, medium, and high) for four categories of potential harm (hate, sexual, violence, and self-harm).
Mitigate potential harms - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/responsible-generative-ai/5-mitigate-harms

Question 35

Question 33 of 50

Select the answer that correctly completes the sentence.
___________ can return responses, such as natural language, images, or code, based on natural language input.
Select only one answer.

Computer vision

Deep learning

Generative AI

Machine learning

Reinforcement learning

Answer 35

Generative AI

Generative AI models offer the capability of generating images based on a prompt by using DALL-E models, such as generating images from natural language. The other AI capabilities are used in different contexts to achieve other goals.
What is generative AI? - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/fundamentals-generative-ai/2-what-is-generative-ai

Question 36

Question 34 of 50

As per the NIST AI Risk Management Framework, what is the first stage to consider when developing a responsible generative AI solution?
Select only one answer.

Identify potential harms.

Measure the presence of potential harms.

Mitigate potential harms.

Operate the solution.

Answer 36

Identify potential harms.

Identifying potential harms is the first stage when planning a responsible generative AI solution.
Plan a responsible generative AI solution - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/responsible-generative-ai/2-plan-responsible-ai

Question 37

Question 35 of 50

Which three capabilities are examples of image generation features for a generative AI model? Each correct answer presents a complete solution.
Select all answers that apply.

animation of static images

creating variations of an image

editing an image

extracting RGB values from an image

new image creation

Answer 37

creating variations of an image

editing an image

new image creation

Image generation models can take a prompt, a base image, or both, and create something new. These generative AI models can create both realistic and artistic images, change the layout or style of an image, and create variations of a provided image.
Understand OpenAI’s image generation capabilities - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/explore-azure-openai/7-understand-openai-image-generation

Question 38

Question 36 of 50

You plan to develop an image processing solution that will use DALL-E as a generative AI model.
Which capability is NOT supported by the DALL-E model?
Select only one answer.

image description

image editing

image generation

image variations

Answer 38

image description

Image description is not a capability included in the DALL-E model, therefore, it is not a use case that can be implemented by using DALL-E, while the other three capabilities are offered by DALL-E in Azure OpenAI.
Understand OpenAI’s image generation capabilities - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/explore-azure-openai/7-understand-openai-image-generation

Question 39

Question 37 of 50

Which generative AI model is used to generate images based on natural language prompts?
Select only one answer.

DALL-E

Embeddings

GPT-3.5

GPT-4

Whisper

Answer 39

DALL-E

DALL-E is a model that can generate images from natural language. GPT-4 and GPT-3.5 can understand and generate natural language and code but not images. Embeddings can convert text into numerical vector form to facilitate text similarity. Whisper can transcribe and translate speech to text.
Understand OpenAI’s image generation capabilities - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/explore-azure-openai/7-understand-openai-image-generation
Azure OpenAI Service models - Azure OpenAI | Microsoft Learn - https://learn.microsoft.com/azure/ai-services/openai/concepts/models

Question 40

Question 38 of 50

Select the answer that correctly completes the sentence.
____________ can search, classify, and compare sources of text for similarity.
Select only one answer.

Data grounding

Embeddings

Machine learning

System messages

Answer 40

Embeddings

Embeddings is an Azure OpenAI model that converts text into numerical vectors for analysis. Embeddings can be used to search, classify, and compare sources of text for similarity.
How to use Azure OpenAI - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/explore-azure-openai/4-how-to-use-azure-openai

Question 41

Question 39 of 50

Which type of machine learning algorithm assigns items to a set of predefined categories?
Select only one answer.

classification

clustering

regression

unsupervised

Answer 41

classification

Classification algorithms are used to predict a predefined category to which an input value belongs. Regression algorithms are used to predict numeric values. Clustering algorithms group data points that have similar characteristics. Unsupervised learning is a category of learning algorithms that includes clustering, but not regression or classification.
Fundamentals of machine learning - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/fundamentals-machine-learning/
What are classification models? - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/understand-classification-machine-learning/2-what-is-classification
What is clustering? - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/train-evaluate-cluster-models/2-what-is-clustering

Question 42

Question 40 of 50

A company deploys an online marketing campaign to social media platforms for a new product launch. The company wants to use machine learning to measure the sentiment of users on the Twitter platform who made posts in response to the campaign.
Which type of machine learning is this?
Select only one answer.

classification

clustering

data transformation

regression

Answer 42

classification

Classification is used to predict categories of data. It can predict which category or class an item of data belongs to. In this example, sentiment analysis can be carried out on the Twitter posts with a numeric value applied to the posts to identify and classify positive or negative sentiment. Clustering is a machine learning type that analyzes unlabeled data to find similarities in the data. Regression is a machine learning scenario that is used to predict numeric values. Data transformation is not a machine learning type.
Clustering - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/fundamentals-machine-learning/7-clustering

Question 43

Question 41 of 50

A healthcare organization has a dataset consisting of bone fracture scans that are categorized by using predefined fracture types. The organization wants to use machine learning to detect the different types of bone fractures for new scans before the scans are sent to a medical practitioner.
Which type of machine learning is this?
Select only one answer.

classification

clustering

featurization

regression

Answer 43

classification

Classification is used to predict categories of data. It can predict which category or class an item of data belongs to. In this example, a machine learning model trained by using classification with labeled data can be used to determine the type of bone fracture in a new scan that is not labeled already. Featurization is not a machine learning type. Regression is used to predict numeric values. Clustering analyzes unlabeled data to find similarities in the data.
Clustering - Training | Microsoft Learn -https://learn.microsoft.com/training/modules/fundamentals-machine-learning/7-clustering

Question 44

Question 42 of 50

A retailer wants to group together online shoppers that have similar attributes to enable its marketing team to create targeted marketing campaigns for new product launches.
Which type of machine learning is this?
Select only one answer.

classification

clustering

multiclass classification

regression

Answer 44

clustering

Clustering is a machine learning type that analyzes unlabeled data to find similarities present in the data. It then groups (clusters) similar data together. In this example, the company can group online customers based on attributes that include demographic data and shopping behaviors. The company can then recommend new products to those groups of customers who are most likely to be interested in them. Classification and multiclass classification are used to predict categories of data. Regression is a machine learning scenario that is used to predict numeric values.
Regression - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/fundamentals-machine-learning/4-regression

Question 45

Question 43 of 50

In a regression machine learning algorithm, how are features and labels handled in a validation dataset?
Select only one answer.

Features are compared to the feature values in a training dataset.

Features are used to generate predictions for the label, which is compared to the actual label values.

Labels are compared to the label values in a training dataset.

The label is used to generate predictions for features, which are compared to the actual feature values.

Answer 45

Features are used to generate predictions for the label, which is compared to the actual label values.

The label is used to generate predictions for features, which are compared to the actual feature values.
In a regression machine learning algorithm, features are used to generate predictions for the label, which is compared to the actual label value. There is no direct comparison of features or labels between the validation and training datasets.
What is regression? - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/train-evaluate-regression-models/2-what-is-regression

Question 46

Question 44 of 50

Which assumption of the multiple linear regression model should be satisfied to avoid misleading predictions?
Select only one answer.

Features are dependent on each other.

Features are independent of each other.

Labels are dependent on each other.

Labels are independent of each other.

Answer 46

Features are independent of each other.

Multiple linear regression models the relationship between several features and a single label. The features must be independent of each other, otherwise, the model’s predictions will be misleading.
Multiple linear regression and R-squared - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/understand-regression-machine-learning/4-multiple-linear-regression

Question 47

Question 45 of 50

In a regression machine learning algorithm, what are the characteristics of features and labels in a validation dataset?
Select only one answer.

known feature and label values

known feature values and unknown label values

unknown feature and label values

unknown feature values and known label values

Answer 47

known feature and label values

In a regression machine learning algorithm, a validation set contains known feature and label values.
What is regression? - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/train-evaluate-regression-models/2-what-is-regression

Question 48

Question 46 of 50

In a regression machine learning algorithm, what are the characteristics of features and labels in a training dataset?
Select only one answer.

known feature and label values

known feature values and unknown label values

unknown feature and label values

unknown feature values and known label values

Answer 48

known feature and label values

In a regression machine learning algorithm, a training set contains known feature and label values.
What is regression? - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/train-evaluate-regression-models/2-what-is-regression

Question 49

Question 47 of 50

A company is using machine learning to predict house prices based on appropriate house attributes.
For the machine learning model, which attribute is the label?
Select only one answer.

age of the house

floor space size

number of bedrooms

price of the house

Answer 49

price of the house

The price of the house is the label you are attempting to predict through the machine learning model. This is typically done by using a regression model. Floor space size, number of bedrooms, and age of the house are all input variables for the model to help predict the house price label.
Fundamentals of machine learning - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/fundamentals-machine-learning/

Question 50

Question 48 of 50

A company wants to predict household water use based on the number of people in a house, the weather temperature, and the time of year.
In terms of data labels and features, what is the label in this use case?
Select only one answer.

number of people in the house

time of year

water use

weather temperature

Answer 50

water use

Water use is the label value that you want to predict, also known as the independent variable. Number of people in the house, weather temperature, and time of year are features, and are values that are dependent on the label. Number of people in the house, weather temperature, and time of year can influence the water consumed in a household.
Fundamentals of machine learning - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/fundamentals-machine-learning/

Question 51

Question 49 of 50

Which machine learning algorithm module in the Azure Machine Learning designer is used to train a model?
Select only one answer.

Clean Missing Data

Evaluate Model

Linear Regression

Select Columns in Dataset

Answer 51

Linear Regression

Linear regression is a machine learning algorithm module used for training regression models. The Clean Missing Data module is part of preparing the data and data transformation process. Select Columns in Dataset is a data transformation component that is used to choose a subset of columns of interest from a dataset. Evaluate model is a component used to measure the accuracy of trained models.
Regression - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/fundamentals-machine-learning/4-regression

Question 52

Question 50 of 50

What is an unsupervised machine learning algorithm module for training models in the Azure Machine Learning designer?
Select only one answer.

Classification

K-Means Clustering

Linear Regression

Normalize Data

Answer 52

K-Means Clustering

K-means clustering is an unsupervised machine learning algorithm component used for training clustering models. You can use unlabeled data with this algorithm. Linear regression and classification are supervised machine learning algorithm components. You need labeled data to use these algorithms. Normalize Data is not a machine learning algorithm module.
Clustering - Training | Microsoft Learn - https://learn.microsoft.com/training/modules/fundamentals-machine-learning/7-clustering