Tag: Microsoft Certified: Azure AI Fundamentals

AI, AI-900, Artificial Intelligence (AI), Large Language Models (LLMs), Machine Learning (ML), Microsoft Certification, Natural Language Processing (NLP)

Exam Prep Hub for AI-900: Microsoft Azure AI Fundamentals

Welcome to the one-stop hub with information for preparing for the AI-900: Microsoft Azure AI Fundamentals certification exam. The content for this exam helps you to “Demonstrate fundamental AI concepts related to the development of software and services of Microsoft Azure to create AI solutions”. Upon successful completion of the exam, you earn the Microsoft Certified: Azure AI Fundamentals certification.

This hub provides information directly here (topic-by-topic as outlined in the official study guide), links to a number of external resources, tips for preparing for the exam, practice tests, and section questions to help you prepare. Bookmark this page and use it as a guide to ensure that you are fully covering all relevant topics for the AI-900 exam and making use of as many of the resources available as possible.

Audience profile (from Microsoft’s site)

This exam is an opportunity for you to demonstrate knowledge of machine learning and AI concepts and related Microsoft Azure services. As a candidate for this exam, you should have familiarity with Exam AI-900’s self-paced or instructor-led learning material.
This exam is intended for you if you have both technical and non-technical backgrounds. Data science and software engineering experience are not required. However, you would benefit from having awareness of:
- Basic cloud concepts
- Client-server applications
You can use Azure AI Fundamentals to prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate, but it’s not a prerequisite for any of them.

Skills measured at a glance (as specified in the official study guide)

  • Describe Artificial Intelligence workloads and considerations (15–20%)
  • Describe fundamental principles of machine learning on Azure (15–20%)
  • Describe features of computer vision workloads on Azure (15–20%)
  • Describe features of Natural Language Processing (NLP) workloads on Azure (15–20%)
  • Describe features of generative AI workloads on Azure (20–25%)
Click on each hyperlinked topic below to go to the preparation content and practice questions for that topic. Also, there are 2 practice exams provided below.

Describe Artificial Intelligence workloads and considerations (15–20%)

Identify features of common AI workloads

Identify guiding principles for responsible AI

Describe fundamental principles of machine learning on Azure (15-20%)

Identify common machine learning techniques

Describe core machine learning concepts

Describe Azure Machine Learning capabilities

Describe features of computer vision workloads on Azure (15–20%)

Identify common types of computer vision solution

Identify Azure tools and services for computer vision tasks

Describe features of Natural Language Processing (NLP) workloads on Azure (15–20%)

Identify features of common NLP Workload Scenarios

Identify Azure tools and services for NLP workloads

Describe features of generative AI workloads on Azure (20–25%)

Identify features of generative AI solutions

Identify generative AI services and capabilities in Microsoft Azure

AI-900 Practice Exams

We have provided 2 practice exams (with answer keys) to help you prepare:

AI-900 Practice Exam 1 (60 questions with answers)

AI-900 Practice Exam 2 (60 questions with answers)

Important AI-900 Resources

To Do’s:

  • Schedule time to learn, study, perform labs, and do practice exams and questions
  • Schedule the exam based on when you think you will be ready; scheduling the exam gives you a target and drives you to keep working on it; but keep in mind that it can be rescheduled based on the rules of the provider.
  • Use the various resources above to learn and prepare.
  • Take the free Microsoft Learn practice test, any other available practice tests, and do the practice questions in each section and the two practice tests available on this exam prep hub.

Good luck to you passing the AI-900: Microsoft Azure AI Fundamentals certification exam and earning the Microsoft Certified: Azure AI Fundamentals certification!

AI, AI-900, Microsoft Certification

Practice Questions: Identify Document Processing Workloads (AI-900 Exam Prep)

Practice Questions

Question 1

A finance team wants to automatically extract the invoice number, vendor name, and total amount from scanned PDF invoices.

Which AI workload is required?

A. Natural language processing
B. Computer vision
C. Document processing
D. Speech recognition

Correct Answer: C

Explanation: Document processing is designed to extract structured fields and data from documents such as invoices and PDFs.

Question 2

An organization wants to digitize thousands of paper forms by converting printed text into machine-readable text.

Which capability is required first?

A. Sentiment analysis
B. Optical Character Recognition (OCR)
C. Text classification
D. Language translation

Correct Answer: B

Explanation: OCR extracts printed or handwritten text from scanned documents and images, enabling further processing.

Question 3

A company processes expense receipts and needs to extract dates, merchant names, totals, and line items.

Which Azure AI service is most appropriate?

A. Azure AI Vision
B. Azure AI Language
C. Azure AI Document Intelligence
D. Azure AI Bot Service

Correct Answer: C

Explanation: Azure AI Document Intelligence (formerly Form Recognizer) is designed for receipt, invoice, and form processing.

Question 4

A business wants to extract rows and columns from tables embedded in scanned reports.

Which document processing capability is required?

A. Image classification
B. Table extraction
C. Sentiment analysis
D. Language detection

Correct Answer: B

Explanation: Table extraction identifies and extracts structured tabular data from documents.

Question 5

A healthcare provider wants to process standardized patient intake forms and store field values in a database.

Which workload best fits this scenario?

A. Computer vision only
B. Natural language processing
C. Document processing with form extraction
D. Speech AI

Correct Answer: C

Explanation: Form extraction is a document processing workload that captures structured key-value pairs from standardized forms.

Question 6

Which scenario most clearly represents a document processing workload?

A. Detecting objects in security camera footage
B. Translating chat messages between languages
C. Extracting contract terms from scanned agreements
D. Converting speech recordings to text

Correct Answer: C

Explanation: Extracting structured information from scanned contracts is a classic document processing use case.

Question 7

A system extracts handwritten notes from scanned documents.

Which capability enables this?

A. Language detection
B. Handwritten text recognition
C. Image tagging
D. Sentiment analysis

Correct Answer: B

Explanation: Handwritten text recognition is part of document processing and OCR capabilities.

Question 8

Which clue in a scenario most strongly indicates a document processing workload?

A. Audio recordings are analyzed
B. Photos are classified into categories
C. Structured data is extracted from PDFs or forms
D. Customer reviews are summarized

Correct Answer: C

Explanation: Document processing focuses on extracting structured information from documents such as PDFs, forms, and invoices.

Question 9

A developer only needs to read plain text from an image without extracting structured fields.

Which Azure AI service is sufficient?

A. Azure AI Document Intelligence
B. Azure AI Language
C. Azure AI Vision
D. Azure AI Bot Service

Correct Answer: C

Explanation: Azure AI Vision provides basic OCR capabilities suitable for simple text extraction from images.

Question 10

An organization wants to ensure responsible use of AI when processing documents that contain personal data.

Which consideration is most relevant?

A. Image resolution
B. Bounding box accuracy
C. Data privacy and access control
D. Model training speed

Correct Answer: C

Explanation: Document processing often involves sensitive information, making privacy and data protection critical considerations.

Final Exam Tip

If a scenario involves forms, invoices, receipts, contracts, PDFs, or extracting structured data from documents, the correct choice is almost always a document processing workload, commonly using Azure AI Document Intelligence.

Go to the PL-300 Exam Prep Hub main page.

AI, AI-900, Artificial Intelligence (AI), Microsoft Certification, Natural Language Processing (NLP)

Practice Questions: Identify Natural Language Processing Workloads (AI-900 Exam Prep)

Practice Questions

Question 1

A company wants to automatically determine whether customer reviews are positive, negative, or neutral.

Which AI workload is required?

A. Text classification
B. Sentiment analysis
C. Language translation
D. Speech recognition

Correct Answer: B

Explanation: Sentiment analysis evaluates the emotional tone of text, such as opinions expressed in customer reviews.

Question 2

An organization needs to route incoming support emails to the correct department based on their content.

Which NLP capability best fits this scenario?

A. Key phrase extraction
B. Text summarization
C. Text classification
D. Language detection

Correct Answer: C

Explanation: Text classification assigns predefined labels or categories to text, making it ideal for routing emails by topic.

Question 3

A legal team wants to quickly identify names of people, organizations, and locations within long contracts.

Which NLP capability should be used?

A. Sentiment analysis
B. Named entity recognition
C. Text translation
D. Optical character recognition

Correct Answer: B

Explanation: Named entity recognition (NER) extracts structured entities such as people, organizations, and locations from unstructured text.

Question 4

A global company wants to translate product descriptions from English into multiple languages while preserving meaning.

Which AI workload is most appropriate?

A. Language detection
B. Text summarization
C. Language translation
D. Speech synthesis

Correct Answer: C

Explanation: Language translation converts text from one language to another while maintaining its original intent and meaning.

Question 5

An application needs to identify the main topics discussed in thousands of customer feedback messages.

Which NLP capability should be used?

A. Sentiment analysis
B. Key phrase extraction
C. Text classification
D. Question answering

Correct Answer: B

Explanation: Key phrase extraction highlights the most important concepts and terms within text.

Question 6

A chatbot answers common customer questions using a natural conversational interface.

Which AI workload does this represent?

A. Computer vision
B. Conversational AI / NLP
C. Speech AI only
D. Anomaly detection

Correct Answer: B

Explanation: Conversational AI uses NLP to understand user intent and generate natural language responses.

Question 7

A system must determine the language of incoming customer messages before processing them further.

Which NLP capability is required?

A. Text classification
B. Language detection
C. Named entity recognition
D. Text summarization

Correct Answer: B

Explanation: Language detection identifies the language used in a text sample.

Question 8

Which input type most strongly indicates a natural language processing workload?

A. Video streams
B. Audio recordings
C. Images and photos
D. Text documents

Correct Answer: D

Explanation: NLP workloads are centered on understanding and generating text-based data.

Question 9

A manager wants a short summary of long meeting transcripts to quickly understand key points.

Which NLP capability should be used?

A. Text summarization
B. Sentiment analysis
C. Language detection
D. Text classification

Correct Answer: A

Explanation: Text summarization condenses long text into a shorter, meaningful summary.

Question 10

An organization wants to ensure responsible use of AI when analyzing employee emails.

Which consideration is most relevant for NLP workloads?

A. Image resolution
B. Model latency
C. Data privacy and bias
D. Bounding box accuracy

Correct Answer: C

Explanation: NLP systems can introduce bias and raise privacy concerns when processing personal or sensitive text data.

Final Exam Tip

If a scenario focuses on understanding, classifying, translating, summarizing, or responding to text, it is almost always a natural language processing workload.

Go to the PL-300 Exam Prep Hub main page.

AI, AI-900, Artificial Intelligence (AI), Computer Vision, Data Education & Training, Microsoft Certification

Practice Questions: Identify Computer Vision Workloads (AI-900 Exam Prep)

Practice Questions

Question 1

A retail company wants to automatically assign categories such as shirt, shoes, or hat to product photos uploaded by sellers.

Which type of AI workload is this?

A. Natural language processing
B. Image classification
C. Object detection
D. Anomaly detection

Correct Answer: B

Explanation: Image classification assigns one or more labels to an entire image. In this scenario, each product photo is classified into a category.

Question 2

A city uses traffic cameras to identify vehicles and pedestrians and draw boxes around them in each image.

Which computer vision capability is being used?

A. Image tagging
B. Image classification
C. Object detection
D. OCR

Correct Answer: C

Explanation: Object detection identifies multiple objects within an image and locates them using bounding boxes.

Question 3

A company wants to extract text from scanned invoices and store the text in a database for searching.

Which computer vision workload is required?

A. Image description
B. Optical Character Recognition (OCR)
C. Face detection
D. Language translation

Correct Answer: B

Explanation: OCR is used to extract printed or handwritten text from images or scanned documents.

Question 4

An application analyzes photos and generates captions such as “A group of people standing on a beach.”

Which computer vision capability is this?

A. Image classification
B. Image tagging and description
C. Object detection
D. Video analysis

Correct Answer: B

Explanation: Image tagging and description focuses on understanding the overall content of an image and generating descriptive text.

Question 5

A security system needs to determine whether a human face is present in images captured at building entrances.

Which workload is most appropriate?

A. Facial recognition
B. Face detection
C. Image classification
D. Speech recognition

Correct Answer: B

Explanation: Face detection determines whether a face exists in an image. Identity verification (facial recognition) is not the focus of AI-900.

Question 6

A media company wants to analyze recorded videos to identify scenes, objects, and motion over time.

Which Azure AI workload does this represent?

A. Image classification
B. Video analysis
C. OCR
D. Text analytics

Correct Answer: B

Explanation: Video analysis processes visual data across multiple frames, enabling object detection, motion tracking, and scene analysis.

Question 7

A manufacturing company wants to detect defective products by locating scratches or dents in photos taken on an assembly line.

Which computer vision workload should be used?

A. Image classification
B. Object detection
C. Anomaly detection
D. Natural language processing

Correct Answer: B

Explanation: Object detection can be used to locate defects within an image by identifying specific problem areas.

Question 8

A developer needs to train a model using their own labeled images because prebuilt vision models are not sufficient.

Which Azure AI service is most appropriate?

A. Azure AI Vision
B. Azure AI Video Indexer
C. Azure AI Custom Vision
D. Azure AI Language

Correct Answer: C

Explanation: Azure AI Custom Vision allows users to train custom image classification and object detection models using their own data.

Question 9

Which clue in a scenario most strongly indicates a computer vision workload?

A. Audio recordings are analyzed
B. Large amounts of numerical data are processed
C. Images or videos are the primary input
D. Text documents are translated

Correct Answer: C

Explanation: Computer vision workloads always involve visual input such as images or video.

Question 10

An organization wants to ensure responsible use of AI when analyzing images of people.

Which consideration is most relevant for computer vision workloads?

A. Query performance tuning
B. Data normalization
C. Privacy and consent
D. Indexing strategies

Correct Answer: C

Explanation: Privacy, consent, and bias are key responsible AI considerations when working with images and facial data.

Final Exam Tip

If a question mentions photos, images, scanned documents, cameras, or video, think computer vision first, then determine the specific capability (classification, detection, OCR, or description).

Go to the PL-300 Exam Prep Hub main page.

AI, AI-900, Artificial Intelligence (AI), Microsoft Certification, Natural Language Processing (NLP)

Identify Natural Language Processing Workloads (AI-900 Exam Prep)

Overview

Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that enables machines to understand, interpret, and generate human language. For the AI-900: Microsoft Azure AI Fundamentals exam, the goal is not to build language models, but to recognize NLP workloads, understand what problems they solve, and identify when NLP is the correct AI approach.

This topic appears under:

  • Describe Artificial Intelligence workloads and considerations (15–20%)
    • Identify features of common AI workloads

Most exam questions will be scenario-based, asking you to choose the correct AI workload based on how text is used.

What Is a Natural Language Processing Workload?

A natural language processing workload involves analyzing or generating language in written or spoken form (after speech has been converted to text).

NLP workloads typically:

  • Process unstructured text
  • Extract meaning, sentiment, or intent
  • Translate between languages
  • Generate human-like text responses

Common inputs:

  • Emails, chat messages, documents
  • Social media posts
  • Customer reviews
  • Transcribed speech

Common outputs:

  • Sentiment scores
  • Extracted keywords or entities
  • Translated text
  • Generated responses or summaries

Common Natural Language Processing Use Cases

On the AI-900 exam, NLP workloads are presented through everyday business scenarios. The following are the most important ones to recognize.

Text Classification

What it does: Categorizes text into predefined labels.

Example scenarios:

  • Classifying emails as spam or not spam
  • Routing support tickets by topic
  • Detecting abusive or inappropriate content

Key idea: The system assigns one or more labels to a piece of text.

Sentiment Analysis

What it does: Determines the emotional tone of text.

Example scenarios:

  • Analyzing customer reviews to see if feedback is positive or negative
  • Monitoring social media reactions to a product launch

Key idea: Sentiment analysis focuses on opinion and emotion, not topic.

Key Phrase Extraction

What it does: Identifies the main concepts discussed in a document.

Example scenarios:

  • Summarizing customer feedback
  • Highlighting important terms in legal or technical documents

Key idea: Key phrases help quickly understand what a document is about.

Named Entity Recognition (NER)

What it does: Identifies and categorizes entities in text.

Common entity types:

  • People
  • Organizations
  • Locations
  • Dates and numbers

Example scenarios:

  • Extracting company names from contracts
  • Identifying people and places in news articles

Language Detection

What it does: Identifies the language used in a text sample.

Example scenarios:

  • Detecting the language of customer messages before translation
  • Routing requests to region-specific support teams

Language Translation

What it does: Converts text from one language to another.

Example scenarios:

  • Translating product descriptions for global audiences
  • Providing multilingual customer support

Key idea: This workload focuses on preserving meaning, not word-for-word translation.

Question Answering and Conversational AI

What it does: Understands user questions and generates relevant responses.

Example scenarios:

  • Customer support chatbots
  • FAQ systems
  • Virtual assistants

Key idea: The system interprets intent and responds in natural language.

Text Summarization

What it does: Condenses long documents into shorter summaries.

Example scenarios:

  • Summarizing reports or meeting notes
  • Highlighting key points from articles

Azure Services Commonly Associated with NLP

For AI-900, you should recognize these services at a conceptual level.

Azure AI Language

Supports:

  • Sentiment analysis
  • Text classification
  • Key phrase extraction
  • Named entity recognition
  • Language detection
  • Summarization

This is the primary service referenced for NLP workloads on the exam.

Azure AI Translator

Supports:

  • Text translation between languages

Used specifically when scenarios mention multilingual translation.

Azure AI Bot Service

Supports:

  • Conversational AI solutions

Often appears alongside NLP services when building chatbots.

How NLP Differs from Other AI Workloads

Distinguishing NLP from other workloads is a common exam requirement.

AI Workload TypePrimary Input
Natural Language ProcessingText
Speech AIAudio
Computer VisionImages and video
Anomaly DetectionNumerical or time-series data

Exam tip: If the data is text-based and the goal is to understand meaning, sentiment, or intent, it is an NLP workload.

Responsible AI Considerations

NLP systems can introduce risks if not used responsibly.

Key considerations include:

  • Bias in language models
  • Offensive or harmful content generation
  • Data privacy when analyzing personal communications

AI-900 tests awareness, not mitigation techniques.

Exam Tips for Identifying NLP Workloads

  • Look for keywords like text, email, message, document, review, chat
  • Identify the goal: classify, analyze sentiment, extract meaning, translate, or respond
  • Ignore implementation details—focus on what problem is being solved
  • Choose the simplest AI workload that meets the scenario

Summary

For the AI-900 exam, you should be able to:

  • Recognize when a scenario represents a natural language processing workload
  • Identify common NLP use cases and capabilities
  • Associate NLP scenarios with Azure AI Language and related services
  • Distinguish NLP from speech, vision, and other AI workloads

A solid understanding of NLP workloads will significantly improve your confidence across multiple exam questions.

Go to the Practice Exam Questions for this topic.

Go to the PL-300 Exam Prep Hub main page.

AI, AI-900, Artificial Intelligence (AI), Computer Vision, Data Education & Training, Microsoft Certification

Identify Computer Vision Workloads (AI-900 Exam Prep)

Overview

Computer vision is a branch of Artificial Intelligence (AI) that enables machines to interpret, analyze, and understand visual information such as images and videos. In the context of the AI-900: Microsoft Azure AI Fundamentals exam, you are not expected to build complex models or write code. Instead, the focus is on recognizing computer vision workloads, understanding what problems they solve, and knowing which Azure AI services are appropriate for each scenario.

This topic falls under:

  • Describe Artificial Intelligence workloads and considerations (15–20%)
    • Identify features of common AI workloads

A strong conceptual understanding here will help you confidently answer many scenario-based exam questions.

What Is a Computer Vision Workload?

A computer vision workload involves extracting meaningful insights from visual data. These workloads allow systems to:

  • Identify objects, people, or text in images
  • Analyze facial features or emotions
  • Understand the content of photos or videos
  • Detect changes, anomalies, or motion

Common inputs include:

  • Images (JPEG, PNG, etc.)
  • Video streams (live or recorded)

Common outputs include:

  • Labels or tags
  • Bounding boxes around detected objects
  • Extracted text
  • Descriptions of image content

Common Computer Vision Use Cases

On the AI-900 exam, computer vision workloads are usually presented as real-world scenarios. Below are the most common ones you should recognize.

Image Classification

What it does: Assigns a category or label to an image.

Example scenarios:

  • Determining whether an image contains a cat, dog, or bird
  • Classifying products in an online store
  • Identifying whether a photo shows food, people, or scenery

Key idea: The entire image is classified as one or more categories.

Object Detection

What it does: Detects and locates multiple objects within an image.

Example scenarios:

  • Detecting cars, pedestrians, and traffic signs in street images
  • Counting people in a room
  • Identifying damaged items in a warehouse

Key idea: Unlike classification, object detection identifies where objects appear using bounding boxes.

Face Detection and Facial Analysis

What it does: Detects human faces and analyzes facial attributes.

Example scenarios:

  • Detecting whether a face is present in an image
  • Estimating age or emotion
  • Identifying facial landmarks (eyes, nose, mouth)

Important exam note:

  • AI-900 focuses on face detection and analysis, not facial recognition for identity verification.
  • Be aware of ethical and privacy considerations when working with facial data.

Optical Character Recognition (OCR)

What it does: Extracts printed or handwritten text from images and documents.

Example scenarios:

  • Reading text from scanned documents
  • Extracting information from receipts or invoices
  • Recognizing license plate numbers

Key idea: OCR turns unstructured visual text into machine-readable text.

Image Description and Tagging

What it does: Generates descriptive text or tags that summarize image content.

Example scenarios:

  • Automatically tagging photos in a digital library
  • Creating alt text for accessibility
  • Generating captions for images

Key idea: This workload focuses on understanding the overall context of an image rather than specific objects.

Video Analysis

What it does: Analyzes video content frame by frame.

Example scenarios:

  • Detecting motion or anomalies in security footage
  • Tracking objects over time
  • Summarizing video content

Key idea: Video analysis extends image analysis across time, not just a single frame.

Azure Services Commonly Associated with Computer Vision

For the AI-900 exam, you should recognize which Azure AI services support computer vision workloads at a high level.

Azure AI Vision

Supports:

  • Image analysis
  • Object detection
  • OCR
  • Face detection
  • Image tagging and description

This is the most commonly referenced service for computer vision scenarios on the exam.

Azure AI Custom Vision

Supports:

  • Custom image classification
  • Custom object detection

Used when prebuilt models are not sufficient and you need to train a model using your own images.

Azure AI Video Indexer

Supports:

  • Video analysis
  • Object, face, and scene detection in videos

Typically appears in scenarios involving video content.

How Computer Vision Differs from Other AI Workloads

Understanding what is not computer vision is just as important on the exam.

AI Workload TypeFocus Area
Computer VisionImages and videos
Natural Language ProcessingText and speech
Speech AIAudio and voice
Anomaly DetectionPatterns in numerical or time-series data

Exam tip: If the input data is visual (images or video), you are almost certainly dealing with a computer vision workload.

Responsible AI Considerations

Microsoft emphasizes responsible AI, and AI-900 includes high-level awareness of these principles.

For computer vision workloads, key considerations include:

  • Privacy and consent when capturing images or video
  • Avoiding bias in facial analysis
  • Transparency in how visual data is collected and used

You will not be tested on implementation details, but you may see conceptual questions about ethical use.

Exam Tips for Identifying Computer Vision Workloads

  • Focus on keywords like image, photo, video, camera, scanned document
  • Look for actions such as detect, recognize, classify, extract text
  • Match the scenario to the simplest appropriate workload
  • Remember: AI-900 tests understanding, not coding

Summary

To succeed on the AI-900 exam, you should be able to:

  • Recognize when a problem is a computer vision workload
  • Identify common use cases such as image classification, object detection, and OCR
  • Understand which Azure AI services are commonly used
  • Distinguish computer vision from other AI workloads

Mastering this topic will give you a strong foundation for many questions in the Describe Artificial Intelligence workloads and considerations domain.

Go to the Practice Exam Questions for this topic.

Go to the PL-300 Exam Prep Hub main page.

AI, AI-900, Artificial Intelligence (AI), Generative AI, Microsoft Certification

Practice Questions: Identify Features of Generative AI Workloads (AI-900 Exam Prep)

Practice Questions

Question 1

A user enters a prompt asking an AI system to draft a professional email summarizing a meeting.

Which type of AI workload is this?

A. Natural language processing (analysis)
B. Document processing
C. Generative AI
D. Computer vision

Correct Answer: C

Explanation: The system is creating new text content based on a prompt, which is the defining feature of generative AI.

Question 2

An AI solution produces original images based on text descriptions such as “a beach at sunset in a watercolor style.”

Which AI workload does this represent?

A. Image classification
B. Object detection
C. Generative AI
D. Computer vision only

Correct Answer: C

Explanation: Image generation creates entirely new images from text prompts, which is a core generative AI capability.

Question 3

Which characteristic most clearly distinguishes generative AI from traditional AI workloads?

A. Uses labeled training data
B. Classifies existing data
C. Generates new content
D. Requires structured input

Correct Answer: C

Explanation: Generative AI creates new outputs (text, images, code), rather than only analyzing or classifying existing data.

Question 4

A chatbot generates unique responses to user questions instead of selecting predefined answers.

Which workload is being used?

A. Rule-based automation
B. Natural language processing only
C. Generative AI
D. Speech recognition

Correct Answer: C

Explanation: Dynamic, context-aware responses that are newly generated indicate a generative AI workload.

Question 5

A company uses an AI system to summarize long reports into short executive summaries.

Why is this considered a generative AI workload?

A. It detects sentiment in the text
B. It extracts key phrases only
C. It generates new summarized text
D. It translates text between languages

Correct Answer: C

Explanation: Summarization involves generating new text that captures the meaning of the original content.

Question 6

Which Azure service is most commonly associated with generative AI workloads on the AI-900 exam?

A. Azure AI Vision
B. Azure AI Language
C. Azure AI Document Intelligence
D. Azure OpenAI Service

Correct Answer: D

Explanation: Azure OpenAI Service provides models for text, image, and code generation and is the primary generative AI service tested in AI-900.

Question 7

A developer writes prompts that specify tone, format, and examples to guide an AI model’s output.

What is this practice called?

A. Model training
B. Prompt engineering
C. Data labeling
D. Hyperparameter tuning

Correct Answer: B

Explanation: Prompt engineering is the practice of crafting prompts to influence the quality and style of generative AI outputs.

Question 8

Which scenario is least likely to use a generative AI workload?

A. Writing marketing copy
B. Generating code examples
C. Classifying customer reviews by topic
D. Creating chatbot responses

Correct Answer: C

Explanation: Classifying text by topic is a traditional NLP analysis task, not a generative AI workload.

Question 9

Which risk is especially associated with generative AI workloads?

A. Image resolution issues
B. Hallucinated or incorrect outputs
C. Poor audio quality
D. Inaccurate bounding boxes

Correct Answer: B

Explanation: Generative AI models can produce outputs that sound plausible but are incorrect, known as hallucinations.

Question 10

Which clue in a scenario most strongly indicates a generative AI workload?

A. The system analyzes scanned documents
B. The system extracts key-value pairs
C. The system generates original text or images
D. The system detects objects in images

Correct Answer: C

Explanation: The creation of new content is the clearest indicator of a generative AI workload.

Final Exam Tip

If a scenario involves creating, drafting, generating, or summarizing content, and the output is new, the correct answer is almost always generative AI, commonly associated with Azure OpenAI Service.

Go to the AI-900 Exam Prep Hub main page.

AI, AI-900, Artificial Intelligence (AI), Microsoft Certification

Practice Questions: Describe considerations for fairness in an AI solution (AI-900 Exam Prep)

Practice Questions

Question 1

Which statement best describes fairness in an AI solution?

Answer: An AI solution should treat all individuals and groups equitably and avoid systematically disadvantaging specific populations.

Explanation: Fairness focuses on preventing biased outcomes that negatively affect certain groups, regardless of overall model accuracy.

Question 2

An AI model accurately predicts loan approvals overall, but rejects applications from a specific demographic group more often than others. Which Responsible AI principle is most directly impacted?

Answer: Fairness

Explanation: Even if a model is accurate, consistently disadvantaging a specific group represents a fairness issue.

Question 3

Which factor is a common source of unfair outcomes in AI systems?

Answer: Biased or unrepresentative training data

Explanation: If training data reflects historical or societal bias, the AI model may learn and reproduce those unfair patterns.

Question 4

Which AI workload is most likely to raise fairness concerns?

Answer: All AI workloads that impact people

Explanation: Fairness applies to machine learning, computer vision, NLP, and generative AI workloads whenever decisions or outputs affect individuals or groups.

Question 5

A facial recognition system performs well for some skin tones but poorly for others. What is the primary concern?

Answer: Unfair performance across different groups

Explanation: Unequal accuracy across populations indicates a fairness issue, even if average performance is high.

Question 6

Which action helps assess fairness in an AI solution?

Answer: Comparing model outcomes across different demographic groups

Explanation: Fairness must be measured by evaluating how results differ between groups, not assumed by default.

Question 7

Which statement about fairness and accuracy is true?

Answer: A highly accurate AI model can still be unfair

Explanation: Accuracy measures correctness overall, while fairness measures equitable treatment across groups.

Question 8

Why must fairness be monitored after an AI solution is deployed?

Answer: Because data and real-world conditions can change over time

Explanation: New data patterns can introduce bias, making ongoing monitoring essential to maintain fairness.

Question 9

Which Microsoft concept groups fairness with principles such as transparency and accountability?

Answer: Responsible AI

Explanation: Fairness is one of Microsoft’s six Responsible AI principles that guide the design and use of AI solutions.

Question 10

An organization wants to ensure its AI system does not reinforce existing social inequalities. Which principle should guide this effort?

Answer: Fairness

Explanation: The goal of fairness is to prevent AI systems from amplifying historical or societal biases and inequalities.

Exam tip

For AI-900, focus on recognizing fairness issues in scenarios rather than technical mitigation techniques. If a question describes unequal treatment of people or groups, fairness is almost always the correct principle to consider.

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AI, AI-900, Artificial Intelligence (AI), Microsoft Certification

Describe considerations for fairness in an AI solution (AI-900 Exam Prep)

Overview

Fairness is one of the core guiding principles of Responsible AI and a key concept tested on the AI-900: Microsoft Azure AI Fundamentals exam. In the context of AI solutions, fairness focuses on ensuring that AI systems treat all people and groups equitably and do not create or reinforce bias.

For the AI-900 exam, you are not expected to implement fairness techniques, but you are expected to recognize fairness-related risks, understand why they matter, and identify when fairness considerations apply to an AI workload.

What does fairness mean in AI?

An AI solution is considered fair when its predictions, recommendations, or decisions do not systematically disadvantage individuals or groups based on personal characteristics.

Bias in AI systems can arise from:

  • Biased or unrepresentative training data
  • Historical or societal inequalities reflected in data
  • Imbalanced datasets (over-representation of some groups and under-representation of others)
  • Design choices made during model development

Fairness aims to reduce these issues and ensure consistent, equitable outcomes.

Examples of fairness concerns

Understanding real-world scenarios is critical for the exam. Common examples include:

  • Hiring systems that favor one gender or demographic over others
  • Loan approval models that unfairly reject applicants from certain groups
  • Facial recognition systems that perform better for some skin tones than others
  • Credit scoring systems influenced by historical discrimination

In each case, the concern is not whether the AI is accurate overall, but whether it behaves equitably across different groups.

Fairness across AI workloads

Fairness considerations apply to all types of AI workloads, including:

  • Machine learning models making predictions or classifications
  • Computer vision systems identifying people or objects
  • Natural language processing systems analyzing or generating text
  • Generative AI systems creating content or recommendations

Any AI system that impacts people directly or indirectly should be evaluated for fairness.

Measuring and assessing fairness

Fairness is not always obvious and often requires analysis. Typical approaches include:

  • Comparing model outcomes across different demographic groups
  • Evaluating error rates for different populations
  • Monitoring model performance after deployment

On the AI-900 exam, you should recognize that fairness must be assessed and monitored, not assumed.

Microsoft’s approach to fairness

Microsoft emphasizes fairness as part of its Responsible AI principles, which include:

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

Azure AI services are designed with these principles in mind, and Microsoft provides tools and guidance to help organizations identify and mitigate unfair outcomes.

Fairness vs accuracy

A key exam concept is understanding that:

  • A highly accurate model can still be unfair
  • Improving fairness may sometimes require trade-offs

The goal is to balance performance with ethical responsibility.

Key takeaways for the AI-900 exam

  • Fairness ensures AI systems do not disadvantage individuals or groups
  • Bias often originates from training data or historical inequalities
  • Fairness applies across all AI workloads
  • Fairness must be evaluated and monitored continuously
  • Microsoft treats fairness as a core Responsible AI principle

Being able to recognize fairness concerns in AI scenarios is essential for success on the AI-900 exam.

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AI, AI-900, Artificial Intelligence (AI), Microsoft Certification

Describe considerations for reliability and safety in an AI solution (AI-900 Exam Prep)

Overview

Reliability and safety are core principles of Responsible AI and an important topic on the AI-900: Microsoft Azure AI Fundamentals exam. These considerations focus on ensuring that AI systems behave as expected, perform consistently under normal and unexpected conditions, and do not cause harm to people, organizations, or systems.

For the AI-900 exam, candidates are expected to understand what reliability and safety mean, recognize scenarios where these considerations apply, and identify why they are critical when deploying AI solutions.

What do reliability and safety mean in AI?

  • Reliability refers to an AI system’s ability to perform consistently and accurately over time and across different conditions.
  • Safety refers to protecting people and systems from harm caused by incorrect, unpredictable, or inappropriate AI behavior.

An AI system should work as intended, handle edge cases gracefully, and fail safely when problems occur.

Why reliability and safety matter

AI systems are increasingly used in situations where incorrect outputs can have serious consequences. Unreliable or unsafe AI systems can:

  • Produce incorrect or misleading results
  • Behave unpredictably in unfamiliar situations
  • Cause physical, financial, or emotional harm
  • Reduce trust in AI solutions

Ensuring reliability and safety helps organizations deploy AI responsibly and confidently.

Examples of reliability and safety concerns

Understanding practical examples is essential for the exam:

  • Autonomous systems misinterpreting sensor data
  • Medical AI tools providing incorrect diagnoses
  • AI-powered chatbots giving harmful or unsafe advice
  • Computer vision systems failing in poor lighting or weather conditions
  • Generative AI systems producing harmful, misleading, or unsafe content

In these scenarios, the concern is whether the AI behaves predictably and safely, especially in edge cases.

Reliability and safety across AI workloads

Reliability and safety considerations apply to all AI workloads, including:

  • Machine learning models making predictions or classifications
  • Computer vision systems detecting objects or people
  • Natural language processing systems interpreting or generating text
  • Generative AI systems creating responses, images, or content

Any AI system that influences decisions, actions, or content should be evaluated for reliability and safety.

Designing for reliability and safety

While AI-900 does not test implementation details, it is important to recognize high-level approaches:

  • Testing AI systems under different conditions
  • Handling unexpected inputs or edge cases
  • Monitoring systems after deployment
  • Implementing safeguards to prevent harmful outputs

These practices help ensure that AI systems remain dependable over time.

Microsoft’s approach to reliability and safety

Microsoft includes reliability and safety as one of its Responsible AI principles, alongside fairness, privacy and security, inclusiveness, transparency, and accountability.

Azure AI services are designed with built-in safeguards and guidance to help organizations deploy reliable and safe AI solutions.

Key takeaways for the AI-900 exam

  • Reliability means consistent and predictable AI behavior
  • Safety focuses on preventing harm to people and systems
  • Reliability and safety apply to all AI workloads
  • AI systems should be tested, monitored, and designed to fail safely
  • Microsoft treats reliability and safety as core Responsible AI principles

Being able to identify reliability and safety concerns in AI scenarios is critical for success on the AI-900 exam.

Go to the Practice Exam Questions for this topic.

Go to the AI-900 Exam Prep Hub main page.