Tag: AI

AI, AI-900, Microsoft Certification January 31, 2026

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) January 31, 2026

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 Type	Primary Input
Natural Language Processing	Text
Speech AI	Audio
Computer Vision	Images and video
Anomaly Detection	Numerical 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 January 31, 2026

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 Type	Focus Area
Computer Vision	Images and videos
Natural Language Processing	Text and speech
Speech AI	Audio and voice
Anomaly Detection	Patterns 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 January 31, 2026

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

Overview

Generative AI is a class of Artificial Intelligence (AI) workloads that create new content rather than only analyzing or classifying existing data. On the AI-900: Microsoft Azure AI Fundamentals exam, you are expected to understand what generative AI is, what kinds of problems it solves, and how it differs from other AI workloads—not how to train large models or write code.

This topic appears under:

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

Expect conceptual and scenario-based questions that test whether you can recognize when generative AI is the appropriate approach.

What Is a Generative AI Workload?

A generative AI workload uses models that can generate new, original content based on patterns learned from large datasets.

Generative AI systems can produce:

Text (responses, summaries, stories, code)
Images (artwork, illustrations, designs)
Audio (music, speech)
Video (short clips or animations)

Key defining feature:
Unlike traditional AI that predicts or classifies, generative AI creates.

Common Generative AI Use Cases

On the AI-900 exam, generative AI is typically presented through productivity, creativity, or assistance scenarios.

Text Generation

What it does: Generates human-like text based on a prompt.

Example scenarios:

Drafting emails or reports
Writing marketing copy
Generating code snippets
Creating conversational responses

Key idea: The model produces new text rather than selecting from predefined responses.

Summarization

What it does: Creates concise summaries of longer text.

Example scenarios:

Summarizing documents or meeting notes
Condensing long articles

Exam note: Summarization may appear in both NLP and generative AI contexts. When the output is newly generated text, it is generative AI.

Question Answering and Chat Experiences

What it does: Generates natural language answers to user questions.

Example scenarios:

AI chat assistants
Knowledge-based Q&A systems

Key idea: Responses are generated dynamically rather than retrieved verbatim.

Image Generation

What it does: Creates images from text descriptions.

Example scenarios:

Generating illustrations or artwork
Creating marketing visuals

Key idea: The system produces entirely new images rather than analyzing existing ones.

Code Generation

What it does: Generates programming code from natural language prompts.

Example scenarios:

Creating sample scripts
Explaining or completing code

Azure Services Associated with Generative AI

For AI-900, service knowledge is high-level and conceptual.

Azure OpenAI Service

Supports:

Text generation
Chat-based experiences
Image generation
Code generation

This is the primary Azure service associated with generative AI workloads on the exam.

How Generative AI Differs from Other AI Workloads

Recognizing these differences is critical for AI-900.

AI Workload Type	Primary Output
Generative AI	Newly created content
Natural Language Processing	Analysis of text
Computer Vision	Analysis of images and video
Document Processing	Structured data extraction
Speech AI	Transcription or audio generation

Exam tip: If the system is creating something new (text, image, code), think generative AI.

Prompt Engineering (Conceptual Awareness)

AI-900 includes basic awareness of prompting.

Prompt engineering refers to crafting inputs that guide a generative model toward better outputs.

Examples:

Providing context
Specifying tone or format
Giving examples in the prompt

No technical depth is required, but you should understand that outputs depend on prompts.

Responsible AI Considerations

Generative AI introduces unique risks.

Key considerations include:

Hallucinations (incorrect or fabricated outputs)
Bias in generated content
Harmful or inappropriate responses
Transparency that content is AI-generated

AI-900 tests awareness, not mitigation techniques.

Exam Tips for Identifying Generative AI Workloads

Look for verbs like generate, create, draft, write, summarize
Focus on whether the output is new content
Ignore implementation details and model names
Choose generative AI when static rules or classification are insufficient

Summary

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

Recognize scenarios that require generative AI
Identify common generative AI use cases
Associate generative AI with Azure OpenAI Service
Distinguish generative AI from analytical AI workloads
Understand high-level responsible AI considerations

Go to the Practice Exam Questions for this topic.

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

AI, AI-900, Artificial Intelligence (AI), Generative AI, Microsoft Certification January 31, 2026

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 January 31, 2026

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.

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

AI, AI-900, Artificial Intelligence (AI), Microsoft Certification January 31, 2026

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.

Go to the Practice Exam Questions for this topic.

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

AI, AI-900, Artificial Intelligence (AI), Microsoft Certification January 31, 2026

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.

AI, AI-900, Artificial Intelligence (AI), Microsoft Certification January 31, 2026

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

Practice Questions

Question 1

What does reliability mean in the context of an AI solution?

Answer: The ability of an AI system to perform consistently and predictably over time and across different conditions.

Explanation: Reliability focuses on consistent behavior and dependable performance, even when conditions change.

Question 2

Which situation best represents a safety concern in an AI system?

Answer: An AI-powered medical tool providing incorrect treatment recommendations.

Explanation: Safety relates to preventing harm to people or systems caused by incorrect or unsafe AI behavior.

Question 3

An AI system works well in testing but produces unexpected results when exposed to real-world data. Which principle is most relevant?

Answer: Reliability

Explanation: Unpredictable behavior in real-world conditions indicates a lack of reliability.

Question 4

Which AI workload is most likely to require reliability and safety considerations?

Answer: All AI workloads

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

Question 5

Why is it important for AI systems to handle edge cases safely?

Answer: To prevent unexpected or harmful outcomes in unusual situations.

Explanation: Edge cases can cause failures if not handled properly, making safe behavior essential.

Question 6

A chatbot occasionally generates misleading or harmful advice. Which Responsible AI principle is most directly affected?

Answer: Reliability and safety

Explanation: Producing unsafe or unreliable content poses a risk to users and violates safety expectations.

Question 7

Which practice helps improve the reliability of an AI system?

Answer: Testing the system under different conditions and scenarios.

Explanation: Testing helps identify weaknesses and ensures consistent performance across varied inputs.

Question 8

Why should AI systems be monitored after deployment?

Answer: Because real-world data and usage patterns can change over time.

Explanation: Ongoing monitoring helps detect reliability or safety issues that emerge after deployment.

Question 9

Which Microsoft concept includes reliability and safety alongside fairness, transparency, and accountability?

Answer: Responsible AI

Explanation: Reliability and safety are core principles within Microsoft’s Responsible AI framework.

Question 10

An organization wants its AI system to fail gracefully instead of producing harmful outputs when errors occur. Which consideration does this reflect?

Answer: Safety

Explanation: Failing safely reduces the risk of harm when AI systems encounter problems or unexpected inputs.

Exam tip

On the AI-900 exam, reliability relates to consistent and predictable behavior, while safety focuses on preventing harm. Scenario-based questions often include words like unexpected, incorrect, harmful, or unpredictable.

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

AI, AI-900, Artificial Intelligence (AI), Microsoft Certification January 31, 2026

Describe considerations for privacy and security in an AI solution (AI-900 Exam Prep)

Overview

Privacy and security are foundational principles of Responsible AI and a key topic on the AI-900: Microsoft Azure AI Fundamentals exam. These considerations focus on protecting personal data, maintaining user trust, and safeguarding AI systems from unauthorized access or misuse.

For AI-900, candidates are expected to understand why privacy and security matter, recognize scenarios where they apply, and identify how they relate to the responsible use of AI — not to implement technical security controls.

What do privacy and security mean in AI?

Privacy refers to protecting personal and sensitive data used by or generated from AI systems.
Security refers to protecting AI systems, data, and models from unauthorized access, attacks, or misuse.

AI solutions often rely on large volumes of data, which makes safeguarding that data critical.

Why privacy and security are important

AI systems frequently process sensitive information such as:

Personal identifiers (names, addresses, IDs)
Images or videos of people
Voice recordings
Text containing confidential or proprietary information

If privacy and security are not properly considered, AI solutions can expose personal data, violate regulations, and lose user trust.

Examples of privacy and security concerns

Common real-world scenarios include:

Facial recognition systems collecting biometric data without consent
Chatbots storing or exposing personal information shared by users
Document processing systems handling confidential financial or legal documents
Generative AI systems unintentionally revealing sensitive training data
Unauthorized access to AI models or datasets

In each case, the concern is how data is collected, stored, protected, and used.

Privacy and security across AI workloads

Privacy and security considerations apply to all AI workloads, including:

Machine learning models trained on personal or sensitive data
Computer vision systems analyzing images or video of people
Natural language processing systems processing user text or conversations
Speech AI systems handling voice recordings
Generative AI systems creating or using content based on user input

Any AI system that uses personal or sensitive data must prioritize privacy and security.

Key privacy considerations

High-level privacy concepts tested on AI-900 include:

Collecting only the data that is necessary
Using data responsibly and for intended purposes
Protecting user consent and expectations
Preventing unintended data exposure

These considerations help ensure ethical and lawful use of data.

Key security considerations

Security-related concepts include:

Preventing unauthorized access to AI systems and data
Protecting AI models from tampering or misuse
Ensuring secure storage and transmission of data

While AI-900 does not test technical security mechanisms, you should recognize when security is a concern in AI scenarios.

Microsoft’s approach to privacy and security

Privacy and security are core components of Microsoft’s Responsible AI principles. Azure AI services are designed to meet enterprise-grade security and compliance standards, helping organizations build AI solutions that protect data and users.