Tag: AI-900 Exam Prep Hub

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

Workload Scenarios to Correct AI Approach mappings (AI-900 Exam Prep)

Here is some additional information to help you solidify your knowledge and prepare for the AI-900 exam.

1. Core AI Approaches

AI Approach	What It’s Best At
Traditional (Rule-Based) AI	Fixed logic, deterministic decisions
Predictive Machine Learning	Predicting values or classifying outcomes
Generative AI	Creating new content from prompts

Another way to relay the same information:

If it follows rules, it’s traditional AI.
If it predicts, it’s ML.
If it creates, it’s generative AI

2. Scenario-to-Approach Mapping

Business Rules & Automation

Scenario	Correct AI Approach	Why
Approve a loan if income > threshold	Traditional AI	Rule-based, no learning required
Route support tickets based on keywords	Traditional AI	Deterministic logic
Enforce compliance policies	Traditional AI	Rules must be followed exactly

Predictive & Analytical Scenarios

Scenario	Correct AI Approach	Why
Predict customer churn	Predictive ML (Classification)	Binary outcome
Forecast product demand	Predictive ML (Regression)	Numeric prediction
Detect credit card fraud	Predictive ML (Classification)	Probability-based decision
Predict house prices	Predictive ML (Regression)	Continuous value
Segment customers	Predictive ML (Clustering)	Discover groups

Natural Language Processing (NLP)

Scenario	Correct AI Approach	Why
Analyze customer sentiment	Predictive ML (NLP)	Classification of sentiment
Extract key phrases from text	Predictive ML (NLP)	Pattern recognition
Recognize named entities	Predictive ML (NLP)	Identify structured info
Translate text	Generative AI / NLP	Generates new text
Summarize documents	Generative AI	Content creation

Computer Vision

Scenario	Correct AI Approach	Why
Identify objects in an image	Predictive ML (Vision)	Classification/detection
Detect faces in images	Predictive ML (Vision)	Pattern recognition
Read printed text from images (OCR)	Predictive ML (Vision)	Extraction task
Generate images from text	Generative AI	Creates new images

Speech Workloads

Scenario	Correct AI Approach	Why
Convert speech to text	Predictive ML (Speech)	Recognition task
Convert text to speech	Generative AI	Generates audio
Identify spoken language	Predictive ML	Classification

Generative AI Scenarios

Scenario	Correct AI Approach	Why
Generate an email from a prompt	Generative AI	New content
Write code from a description	Generative AI	Content generation
Answer questions conversationally	Generative AI	Dynamic responses
Create images from text prompts	Generative AI	Creative output

3. Azure Service Mapping

Scenario Type	Azure Service
Predictive ML	Azure Machine Learning
NLP (Sentiment, Entities)	Azure AI Language
Speech workloads	Azure AI Speech
Vision workloads	Azure AI Vision
Generative AI	Azure OpenAI Service
Rule-based workflows	Logic Apps / Power Automate

4. Common AI-900 Exam Traps

Trap	Correct Thinking
“Translation is classification”	❌ Translation generates text
“Chatbots are always rule-based”	❌ Modern chatbots use generative AI
“OCR generates text”	❌ OCR extracts existing text
“Generative AI replaces ML”	❌ Different goals

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 Common Scenarios for Generative AI (AI-900 Exam Prep)

Practice Questions

Question 1

You want to build a solution that can write marketing emails based on a short prompt describing a product.
Which AI approach should you use?

A. Traditional rule-based AI
B. Predictive machine learning
C. Generative AI
D. Computer vision

✅ Correct Answer: C

Explanation:
The solution must create new text content based on prompts. This is a defining characteristic of generative AI, not prediction or rules.

Question 2

A chatbot is required to answer open-ended customer questions using natural, conversational language.
Which scenario does this represent?

A. Sentiment analysis
B. Rule-based automation
C. Generative AI
D. Classification

✅ Correct Answer: C

Explanation:
Conversational assistants that generate dynamic responses are a common generative AI scenario. Rule-based chatbots would rely on predefined responses instead.

Question 3

You need a system that summarizes long legal documents into short executive summaries.
Which type of AI workload is this?

A. Predictive machine learning
B. Generative AI
C. Optical character recognition (OCR)
D. Entity recognition

✅ Correct Answer: B

Explanation:
Summarization involves creating new text that condenses original content, which makes this a generative AI workload.

Question 4

Which task is most appropriate for a generative AI model?

A. Predicting house prices
B. Detecting objects in images
C. Translating text between languages
D. Classifying emails as spam

✅ Correct Answer: C

Explanation:
Translation requires generating new text in another language. The other options are predictive or computer vision tasks.

Question 5

A developer wants to generate Python code from a natural language description of a task.
Which AI capability is being used?

A. Regression
B. Classification
C. Rule-based automation
D. Generative AI

✅ Correct Answer: D

Explanation:
Generating source code from prompts is a classic generative AI use case.

Question 6

Which keyword in an exam question most strongly indicates a generative AI scenario?

A. Predict
B. Classify
C. Detect
D. Generate

✅ Correct Answer: D

Explanation:
Words like generate, create, write, and compose are strong indicators of generative AI scenarios on the AI-900 exam.

Question 7

You need to build a solution that creates images based on a text description.
Which Azure service is most appropriate?

A. Azure AI Vision
B. Azure Machine Learning
C. Azure OpenAI Service
D. Azure AI Language

✅ Correct Answer: C

Explanation:
Azure OpenAI Service is the primary Azure service for generative AI workloads, including text and image generation.

Question 8

Which scenario is NOT a generative AI use case?

A. Writing product descriptions
B. Creating an AI-powered tutor
C. Detecting faces in photos
D. Summarizing meeting notes

✅ Correct Answer: C

Explanation:
Face detection analyzes existing images and does not create new content. It is a computer vision task, not generative AI.

Question 9

A company wants an AI system that rewrites customer emails to sound more polite and professional.
Which AI approach should be used?

A. Predictive machine learning
B. Traditional AI
C. Generative AI
D. Clustering

✅ Correct Answer: C

Explanation:
Rewriting or rephrasing text involves generating new text, which is a generative AI capability.

Question 10

Which statement best describes generative AI?

A. It follows predefined business rules
B. It predicts numeric values
C. It groups similar data points
D. It creates new content based on learned patterns

✅ Correct Answer: D

Explanation:
Generative AI models are designed to create new outputs (text, images, code, audio) based on patterns learned from large datasets.

Quick Exam Tip

If the question asks:

“Will the AI create something new?” → Generative AI
“Will the AI predict or label?” → Predictive ML
“Will the AI follow strict rules?” → Traditional AI

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

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

Identify Common Scenarios for Generative AI (AI-900 Exam Prep)

Overview

In the AI-900: Microsoft Azure AI Fundamentals exam, generative AI represents a significant and growing focus area. This topic assesses your ability to recognize when generative AI is the appropriate solution and how it differs from traditional AI and predictive machine learning.

Generative AI models are designed to create new content—such as text, images, audio, or code—based on patterns learned from large datasets and guided by user prompts.

This article explains common real-world scenarios where generative AI is used, how those scenarios appear on the AI-900 exam, and how they map to Azure services.

What Makes a Scenario “Generative AI”?

A workload is a generative AI scenario when:

The output is newly generated content, not just a prediction or classification
The model responds to natural language prompts or instructions
The output can vary creatively, even for similar inputs

If the task is to predict, classify, or extract, it is not generative AI. If the task is to create, compose, or generate, it is.

Common Generative AI Scenarios (AI-900 Focus)

1. Text Generation

Scenario examples:

Writing emails, reports, or marketing copy
Drafting blog posts or documentation
Generating summaries from bullet points

Why this is generative AI: The model creates original text based on a prompt rather than selecting from predefined responses.

2. Conversational AI and Chatbots

Scenario examples:

AI-powered customer support chatbots
Virtual assistants that answer open-ended questions
Knowledge assistants that explain concepts conversationally

Why this is generative AI: Responses are dynamically generated and context-aware, rather than rule-based or scripted.

3. Text Summarization

Scenario examples:

Summarizing long documents
Creating executive summaries
Condensing meeting transcripts

Why this is generative AI: The model produces a new, concise version of the original content while preserving meaning.

4. Translation and Language Transformation

Scenario examples:

Translating text between languages
Rewriting text to be simpler or more formal
Paraphrasing content

Why this is generative AI: The output text is newly generated rather than extracted or classified.

5. Code Generation and Assistance

Scenario examples:

Generating code from natural language descriptions
Explaining existing code
Refactoring or optimizing code snippets

Why this is generative AI: The model creates original source code based on intent expressed in a prompt.

6. Image Generation

Scenario examples:

Creating images from text prompts
Generating artwork or design concepts
Producing visual content for marketing

Why this is generative AI: The model synthesizes entirely new images rather than identifying objects in existing ones.

7. Audio and Speech Generation

Scenario examples:

Converting text into natural-sounding speech
Generating voiceovers
Creating spoken responses for virtual assistants

Why this is generative AI: The audio output is generated dynamically from text input.

Azure Services Commonly Used for Generative AI

For the AI-900 exam, generative AI scenarios are most commonly associated with:

Azure OpenAI Service
- Large language models (LLMs)
- Text, code, and image generation
- Conversational AI

Other Azure services (such as Azure AI Speech or Language) may support generative capabilities, but Azure OpenAI Service is the primary service to associate with generative AI workloads.

Generative AI vs Other AI Approaches (Quick Contrast)

Task Type	AI Approach
Predict a value or category	Predictive Machine Learning
Follow predefined rules	Traditional AI
Create new text, images, or code	Generative AI

How This Appears on the AI-900 Exam

On the exam, generative AI scenarios are typically described using words such as:

Generate
Create
Write
Summarize
Compose
Respond conversationally

If the question emphasizes creative or open-ended output, generative AI is likely the correct choice.

Key Takeaways for Exam Day

Generative AI is about creation, not prediction
Outputs are flexible and context-aware
Azure OpenAI Service is the primary Azure service for generative AI
If the output did not previously exist, generative AI is likely the answer

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 Responsible AI Considerations for Generative AI (AI-900 Exam Prep)

Practice Exam Questions

Question 1

A company uses a generative AI model to create marketing content. They want to ensure the model does not produce offensive or harmful language.

Which Responsible AI principle is being addressed?

A. Transparency
B. Fairness
C. Reliability and Safety
D. Accountability

Correct Answer: C

Explanation:
Preventing harmful or offensive outputs is a core aspect of reliability and safety, which ensures AI systems behave safely under expected conditions.

Question 2

A chatbot powered by generative AI informs users that responses are created by an AI system and may contain errors.

Which Responsible AI principle does this demonstrate?

A. Privacy and Security
B. Transparency
C. Inclusiveness
D. Fairness

Correct Answer: B

Explanation:
Clearly communicating that content is AI-generated and may be inaccurate supports transparency, helping users understand the system’s limitations.

Question 3

A developer ensures that AI-generated job descriptions do not favor or exclude any gender, ethnicity, or age group.

Which Responsible AI principle is being applied?

A. Accountability
B. Fairness
C. Reliability and Safety
D. Privacy

Correct Answer: B

Explanation:
Avoiding bias and discrimination in generated content aligns with the fairness principle.

Question 4

An organization requires a human reviewer to approve all AI-generated responses before they are published on a public website.

Which Responsible AI principle does this represent?

A. Transparency
B. Reliability and Safety
C. Accountability
D. Inclusiveness

Correct Answer: C

Explanation:
Ensuring humans remain responsible for AI outputs demonstrates accountability.

Question 5

A generative AI system is designed so that user prompts and outputs are not stored or used to retrain the model.

Which Responsible AI principle is primarily addressed?

A. Transparency
B. Privacy and Security
C. Fairness
D. Inclusiveness

Correct Answer: B

Explanation:
Protecting user data and preventing unauthorized use of information supports privacy and security.

Question 6

Which feature in Azure AI services helps prevent generative AI models from producing unsafe or inappropriate content?

A. Model training
B. Content filters
C. Data labeling
D. Feature engineering

Correct Answer: B

Explanation:
Content filters are used to block harmful, unsafe, or inappropriate AI-generated outputs.

Question 7

A generative AI model supports multiple languages and produces accessible text for diverse user groups.

Which Responsible AI principle does this best represent?

A. Fairness
B. Transparency
C. Inclusiveness
D. Accountability

Correct Answer: C

Explanation:
Supporting diverse languages and accessibility aligns with the inclusiveness principle.

Question 8

Which scenario best illustrates a Responsible AI concern specific to generative AI?

A. A model classifies images into categories
B. A model predicts future sales
C. A model generates false but confident answers
D. A model stores structured data in a database

Correct Answer: C

Explanation:
Generative AI can produce hallucinations—incorrect but plausible outputs—which is a key Responsible AI concern.

Question 9

Why is Responsible AI especially important for generative AI workloads?

A. Generative AI requires more computing power
B. Generative AI creates new content that can cause harm if uncontrolled
C. Generative AI only works with unstructured data
D. Generative AI replaces traditional machine learning

Correct Answer: B

Explanation:
Because generative AI creates new content, it can introduce bias, misinformation, or harmful outputs if not properly governed.

Question 10

A company uses Azure OpenAI Service and wants to ensure ethical use of generative AI.

Which action best supports Responsible AI practices?

A. Removing all system prompts
B. Enabling content moderation and human review
C. Increasing model size
D. Disabling user authentication

Correct Answer: B

Explanation:
Combining content moderation with human oversight helps ensure safe, ethical, and responsible use of generative AI.

Final Exam Tips for This Topic

Expect scenario-based questions
Focus on principles, not technical configuration
Watch for keywords: bias, harm, safety, privacy, transparency
If the question mentions risk or trust, think Responsible AI

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

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

Identify Responsible AI Considerations for Generative AI (AI-900 Exam Prep)

Overview

Generative AI systems are powerful because they can create new content, such as text, images, code, and audio. However, this power also introduces ethical, legal, and societal risks. For this reason, Responsible AI is a core concept tested in the AI-900 exam, especially for generative AI workloads on Azure.

Microsoft emphasizes Responsible AI to ensure that AI systems are:

Fair
Reliable
Safe
Transparent
Secure
Inclusive
Accountable

Understanding these principles — and how they apply specifically to generative AI — is essential for passing the exam.

What Is Responsible AI?

Responsible AI refers to designing, developing, and deploying AI systems in ways that:

Minimize harm
Promote fairness and trust
Respect privacy and security
Provide transparency and accountability

Microsoft has formalized this through its Responsible AI Principles, which are directly reflected in Azure AI services and exam questions.

Why Responsible AI Matters for Generative AI

Generative AI introduces unique risks, including:

Producing biased or harmful content
Generating incorrect or misleading information (hallucinations)
Exposing sensitive or copyrighted data
Being misused for impersonation or misinformation

Because generative AI creates content dynamically, guardrails and safeguards are critical.

Microsoft’s Responsible AI Principles (Exam-Relevant)

1. Fairness

Definition:
AI systems should treat all people fairly and avoid bias.

Generative AI Example:
A text-generation model should not produce discriminatory language based on race, gender, age, or religion.

Azure Support:

Bias evaluation
Content filtering
Prompt design best practices

Exam Clue Words: bias, discrimination, fairness

2. Reliability and Safety

Definition:
AI systems should perform consistently and safely under expected conditions.

Generative AI Example:
A chatbot should avoid generating dangerous instructions or harmful advice.

Azure Support:

Content moderation
Safety filters
System message controls

Exam Clue Words: safety, harmful output, reliability

3. Privacy and Security

Definition:
AI systems must protect user data and respect privacy.

Generative AI Example:
A model should not store or reveal personal or confidential information provided in prompts.

Azure Support:

Data isolation
No training on customer prompts (Azure OpenAI)
Enterprise-grade security

Exam Clue Words: privacy, personal data, security

4. Transparency

Definition:
Users should understand how AI systems are being used and their limitations.

Generative AI Example:
Informing users that responses are AI-generated and may contain errors.

Azure Support:

Model documentation
Clear service descriptions
Usage disclosures

Exam Clue Words: explainability, transparency, disclosure

5. Accountability

Definition:
Humans must remain responsible for AI system outcomes.

Generative AI Example:
A human reviews AI-generated content before publishing it externally.

Azure Support:

Human-in-the-loop design
Monitoring and logging
Responsible deployment guidance

Exam Clue Words: human oversight, accountability

6. Inclusiveness

Definition:
AI systems should empower everyone and avoid excluding groups.

Generative AI Example:
Supporting multiple languages or accessibility-friendly outputs.

Azure Support:

Multilingual models
Accessibility-aware services

Exam Clue Words: inclusivity, accessibility

Responsible AI Controls for Generative AI on Azure

Azure provides built-in mechanisms to help organizations use generative AI responsibly.

Key Controls to Know for AI-900

Control	Purpose
Content filters	Prevent harmful, unsafe, or inappropriate outputs
Prompt engineering	Guide model behavior safely
System messages	Set boundaries for AI behavior
Human review	Validate outputs before use
Usage monitoring	Detect misuse or anomalies

Common Responsible AI Scenarios (Exam Focus)

You are very likely to see scenarios like these:

Preventing a chatbot from generating offensive language
Ensuring AI-generated content is reviewed by humans
Avoiding bias in generated job descriptions
Protecting personal data in prompts and outputs
Informing users that AI-generated content may be inaccurate

If the question mentions risk, harm, bias, safety, or trust, it is almost always testing Responsible AI.

Generative AI vs Responsible AI (Exam Framing)

Concept	Purpose
Generative AI	Creates new content
Responsible AI	Ensures that content is safe, fair, and trustworthy

👉 Generative AI answers what AI can do
👉 Responsible AI answers how AI should be used

Key Takeaways for the AI-900 Exam

Responsible AI is not optional — it is a core design principle
Generative AI introduces new ethical risks
Microsoft’s Responsible AI principles guide Azure AI services
Expect scenario-based questions, not deep technical ones
Focus on concepts, not implementation details

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 Features and Capabilities of Azure AI Foundry (AI-900 Exam Prep)

Practice Questions

Question 1

What is the primary purpose of Azure AI Foundry?

A. To provide pre-trained computer vision models only
B. To host virtual machines for AI workloads
C. To provide a unified platform for building, customizing, and managing generative AI solutions
D. To replace Azure Machine Learning

✅ Correct Answer: C

Explanation:
Azure AI Foundry is a unified platform designed to help teams build, customize, deploy, and manage generative AI applications at scale. It does not replace Azure ML but complements it.

Question 2

Which capability of Azure AI Foundry allows organizations to compare and select the most appropriate model for a specific use case?

A. Role-Based Access Control (RBAC)
B. Model catalog and benchmarking
C. Azure Monitor integration
D. Speech synthesis APIs

✅ Correct Answer: B

Explanation:
Azure AI Foundry includes a model catalog with tools to compare and benchmark multiple models, helping teams choose the best model based on performance, cost, or task suitability.

Question 3

A development team wants to create an AI system that can autonomously perform tasks and collaborate with other AI components. Which Azure AI Foundry capability supports this scenario?

A. Image classification
B. Agent orchestration
C. Text analytics
D. Speech recognition

✅ Correct Answer: B

Explanation:
Azure AI Foundry supports AI agents and multi-agent workflows, enabling autonomous task execution and collaboration across agents.

Question 4

Which feature makes Azure AI Foundry suitable for enterprise environments?

A. Open-source licensing
B. Built-in gaming engines
C. Governance, monitoring, and role-based access controls
D. Support for only a single AI model

✅ Correct Answer: C

Explanation:
Enterprise readiness comes from security, governance, RBAC, monitoring, and compliance controls, all of which are core features of Azure AI Foundry.

Question 5

Which task can be performed using Azure AI Foundry?

A. Only training custom neural networks from scratch
B. Managing physical AI hardware
C. Fine-tuning generative AI models for domain-specific use cases
D. Replacing Azure App Service

✅ Correct Answer: C

Explanation:
Azure AI Foundry allows fine-tuning and optimization of generative AI models to adapt them to specific business or domain requirements.

Question 6

What stage of the AI lifecycle is supported by Azure AI Foundry?

A. Only model training
B. Only deployment
C. Only monitoring
D. The full lifecycle from experimentation to production and monitoring

✅ Correct Answer: D

Explanation:
Azure AI Foundry supports the entire AI lifecycle, including experimentation, development, deployment, monitoring, and continuous improvement.

Question 7

Which scenario best matches the use of Azure AI Foundry?

A. Classifying images of animals
B. Translating text between languages
C. Building an enterprise chatbot that uses multiple AI models and enforces governance
D. Running batch SQL queries

✅ Correct Answer: C

Explanation:
Azure AI Foundry is designed for complex generative AI scenarios, such as enterprise chatbots that require multiple models, orchestration, and governance.

Question 8

How does Azure AI Foundry integrate with other Azure services?

A. It operates completely independently
B. It only integrates with Azure OpenAI
C. It integrates with services like Azure App Service, Cosmos DB, and Logic Apps
D. It replaces all other Azure AI services

✅ Correct Answer: C

Explanation:
Azure AI Foundry integrates deeply with the Azure ecosystem, allowing generative AI solutions to be embedded into broader applications and workflows.

Question 9

Which feature helps control access and usage of AI resources in Azure AI Foundry?

A. Prompt engineering
B. Role-Based Access Control (RBAC)
C. Image tagging
D. Speech transcription

✅ Correct Answer: B

Explanation:
RBAC ensures that users and teams only have access to the resources and actions they are authorized to use, supporting secure enterprise deployments.

Question 10

On the AI-900 exam, when should you select Azure AI Foundry as the correct answer?

A. When the question focuses on basic image processing
B. When the question mentions simple sentiment analysis
C. When the scenario describes building, managing, and governing generative AI applications at scale
D. When the question requires only translation services

✅ Correct Answer: C

Explanation:
Azure AI Foundry is the best choice when the scenario involves enterprise-scale generative AI, including model selection, agents, lifecycle management, and governance.

Quick Exam Summary

If the question mentions:

Generative AI
Multiple models
Agents or workflows
Enterprise governance
End-to-end AI lifecycle

👉 Think: Azure AI Foundry

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

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

Describe Features and Capabilities of Azure AI Foundry (AI-900 Exam Prep)

What Is Azure AI Foundry?

Azure AI Foundry — now commonly referred to as Microsoft Foundry — is a unified Azure platform for developing, managing, and scaling enterprise-grade generative AI applications. It brings together models, tools, governance, and infrastructure into a single, interoperable environment, making it easier for teams to build, deploy, and operate AI apps and agents securely and consistently.

For AI-900 purposes, think of Foundry as a comprehensive hub for generative AI development on Azure — far beyond just model hosting — that enables rapid innovation with governance and enterprise readiness built in.

Core Capabilities of Azure AI Foundry

📌 1. Unified AI Development Platform

Foundry provides a single platform for AI teams and developers to:

Explore and compare a broad catalog of foundational models
Build, test, and customize generative AI solutions
Monitor and refine models over time

This reduces complexity and streamlines workflows compared with managing disparate tools.

🧠 2. Vast Model Catalog & Interoperability

Foundry gives access to thousands of models from multiple sources:

Frontier and open models from Microsoft
Models from OpenAI
Third-party models (e.g., Meta, Mistral)
Partner and community models

Teams can benchmark and compare models for specific tasks before selecting one for production.

⚙️ 3. Customization and Optimization

Foundry provides tools to help you:

Fine-tune models for specific domain needs
Distill or upgrade models to improve quality or reduce cost
Route workloads to the best performing model for a given request

Automated routing helps balance performance vs cost in production AI applications.

🤖 4. Build Agents and Intelligent Workflows

With Foundry, developers can build:

AI agents that perform tasks autonomously
Multi-agent systems where agents collaborate to solve complex problems
RPA-like automation and AI-driven business logic

These agents can be integrated into apps, bots, or workflow systems to respond, act, and collaborate with users.

🔐 5. Enterprise-Ready Governance and Security

Foundry includes enterprise-grade tools to manage:

Role-Based Access Control (RBAC)
Monitoring, logging, and audit trails
Secure access and isolation between teams
Compliance with organizational policies

This makes it suitable for large teams and critical use cases.

🛠 6. Integrated Tools and Templates

Foundry includes:

Pre-built solution templates for common AI patterns (e.g., Q&A bots, document assistants)
SDKs and APIs for Python, C#, and other languages
IDE integrations (e.g., Visual Studio Code extensions)

These accelerate development and reduce the learning curve.

🔄 7. End-to-End Lifecycle Support

Foundry supports the full AI project lifecycle:

Experimentation with models
Development of applications or workflows
Testing and evaluation
Deployment to production
Monitoring and refinement for optimization

This means teams can start with prototypes and scale seamlessly.

🧩 8. Integration with Azure Ecosystem

Foundry is not limited to AI models — it integrates with other Azure services, such as:

Azure App Service
Azure Container Apps
Azure Cosmos DB
Azure Logic Apps
Microsoft 365 and Teams

This allows generative AI features to be embedded into broader enterprise systems.

Scenarios Where Azure AI Foundry Is Used

Foundry supports many generative AI workloads, including:

Conversational agents and bots
Knowledge-powered search and assistants
Context-aware automation
Enterprise RAG (Retrieval-Augmented Generation)
AI-powered workflows and multi-agent orchestration

Its focus on flexibility and scale makes it suitable for both prototyping and enterprise production.

How Foundry Relates to Other Azure Generative AI Services

Capability	Azure AI Foundry	Other Azure Services
Model hosting & comparison	✅	Azure OpenAI / Azure AI services
Multi-model catalog	✅	Individual service catalogs
Fine-tuning & optimization	✅	Azure Machine Learning
Build agents & workflows	✅	Azure AI Language / Bots
Governance & enterprise features	✅	Core Azure security services
Rapid prototyping templates	✅	Individual service templates

Foundry’s value is in bringing these capabilities together into a unified platform.

Exam Tips for AI-900

Foundry is the answer when a question describes building, customizing, and governing enterprise generative AI solutions at scale.
It is not just a model API, but a platform for development, deployment, and lifecycle management of generative AI apps.
If a question mentions agents, workflows, integrated governance, or multi-model support for generative workloads, think Azure AI Foundry / Microsoft Foundry.

Key Takeaways

Azure AI Foundry (Microsoft Foundry) is a unified enterprise AI platform for generative AI development on Azure.
It provides model catalogs, customization, development tools, agents, governance, and integrations.
It supports the full AI application lifecycle — from prototype to production.
It integrates deeply with the Azure ecosystem and supports enterprise-grade governance and security.

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), Large Language Models (LLMs), Microsoft Certification January 31, 2026

Practice Questions: Describe Features and Capabilities of Azure OpenAI Service (AI-900 Exam Prep)

Practice Questions

Question 1

You need to build a chatbot that can generate natural, human-like responses and maintain context across multiple user interactions. Which Azure service should you use?

A. Azure AI Language
B. Azure AI Speech
C. Azure OpenAI Service
D. Azure AI Vision

Correct Answer: C

Explanation:
Azure OpenAI Service provides large language models capable of multi-turn conversational AI. Azure AI Language supports traditional NLP tasks but not advanced generative conversations.

Question 2

Which feature of Azure OpenAI Service enables semantic search by representing text as numerical vectors?

A. Prompt engineering
B. Text completion
C. Embeddings
D. Tokenization

Correct Answer: C

Explanation:
Embeddings convert text into vectors that capture semantic meaning, enabling similarity search and retrieval-augmented generation (RAG).

Question 3

An organization wants to generate summaries of long internal documents while ensuring their data is not used to train public models. Which service meets this requirement?

A. Open-source LLM hosted on a VM
B. Azure AI Language
C. Azure OpenAI Service
D. Azure Cognitive Search

Correct Answer: C

Explanation:
Azure OpenAI ensures customer data isolation and does not use customer data to retrain models, making it suitable for enterprise and regulated environments.

Question 4

Which type of workload is Azure OpenAI Service primarily designed to support?

A. Predictive analytics
B. Generative AI
C. Rule-based automation
D. Image preprocessing

Correct Answer: B

Explanation:
Azure OpenAI focuses on generative AI workloads, including text generation, conversational AI, code generation, and embeddings.

Question 5

A developer wants to build an AI assistant that can explain code, generate new code snippets, and translate code between programming languages. Which Azure service should be used?

A. Azure AI Language
B. Azure Machine Learning
C. Azure OpenAI Service
D. Azure AI Vision

Correct Answer: C

Explanation:
Azure OpenAI supports code-capable large language models designed for code generation, explanation, and translation.

Question 6

Which Azure OpenAI capability is MOST useful for building retrieval-augmented generation (RAG) solutions?

A. Chat completion
B. Embeddings
C. Image generation
D. Speech synthesis

Correct Answer: B

Explanation:
RAG solutions rely on embeddings to retrieve relevant content based on semantic similarity before generating responses.

Question 7

Which security feature is a key benefit of using Azure OpenAI Service instead of public OpenAI endpoints?

A. Anonymous access
B. Built-in image labeling
C. Azure Active Directory integration
D. Automatic data labeling

Correct Answer: C

Explanation:
Azure OpenAI integrates with Azure Active Directory and RBAC, providing enterprise-grade authentication and access control.

Question 8

A solution requires generating marketing copy, summarizing customer feedback, and answering user questions in natural language. Which Azure service best supports all these requirements?

A. Azure AI Language
B. Azure OpenAI Service
C. Azure AI Vision
D. Azure AI Search

Correct Answer: B

Explanation:
Azure OpenAI excels at generating and transforming text using large language models, covering all described scenarios.

Question 9

Which statement BEST describes how Azure OpenAI Service handles customer data?

A. Customer data is used to retrain models globally
B. Customer data is publicly accessible
C. Customer data is isolated and not used for model training
D. Customer data is stored permanently without controls

Correct Answer: C

Explanation:
Azure OpenAI ensures data isolation and does not use customer prompts or responses to retrain foundation models.

Question 10

When should you choose Azure OpenAI Service instead of Azure AI Language?

A. When performing key phrase extraction
B. When detecting named entities
C. When generating original text or conversational responses
D. When identifying sentiment polarity

Correct Answer: C

Explanation:
Azure AI Language is designed for traditional NLP tasks, while Azure OpenAI is used for generative AI tasks such as text generation and conversational AI.

Final Exam Tip

If the scenario involves creating new content, chatting naturally, generating code, or semantic understanding at scale, the correct answer is likely related to Azure OpenAI Service.

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

AI, AI-900, Artificial Intelligence (AI), Large Language Models (LLMs), Microsoft Certification January 31, 2026

Describe Features and Capabilities of Azure OpenAI Service (AI-900 Exam Prep)

Overview

The Azure OpenAI Service provides access to powerful OpenAI large language models (LLMs)—such as GPT models—directly within the Microsoft Azure cloud environment. It enables organizations to build generative AI applications while benefiting from Azure’s security, compliance, governance, and enterprise integration capabilities.

For the AI-900 exam, Azure OpenAI is positioned as Microsoft’s primary service for generative AI workloads, especially those involving text, code, and conversational AI.

What Is Azure OpenAI Service?

Azure OpenAI Service allows developers to deploy, customize, and consume OpenAI models using Azure-native tooling, APIs, and security controls.

Key characteristics:

Hosted and managed by Microsoft Azure
Provides enterprise-grade security and compliance
Uses REST APIs and SDKs
Integrates seamlessly with other Azure services

👉 On the exam, Azure OpenAI is the correct answer when a scenario describes generative AI powered by large language models.

Core Capabilities of Azure OpenAI Service

1. Access to Large Language Models (LLMs)

Azure OpenAI provides access to advanced models such as:

GPT models for text generation and understanding
Chat models for conversational AI
Embedding models for semantic search and retrieval
Code-focused models for programming assistance

These models can:

Generate human-like text
Answer questions
Summarize content
Write code
Explain concepts
Generate creative content

2. Text and Content Generation

Azure OpenAI can generate:

Articles, emails, and reports
Chatbot responses
Marketing copy
Knowledge base answers
Product descriptions

Exam tip:
If the question mentions writing, summarizing, or generating text, Azure OpenAI is likely the answer.

3. Conversational AI (Chatbots)

Azure OpenAI supports natural, multi-turn conversations, making it ideal for:

Customer support chatbots
Virtual assistants
Internal helpdesk bots
AI copilots

These chatbots:

Maintain conversation context
Generate natural responses
Can be grounded in enterprise data

4. Code Generation and Assistance

Azure OpenAI can:

Generate code snippets
Explain existing code
Translate code between languages
Assist with debugging

This makes it valuable for developer productivity tools and AI-assisted coding scenarios.

5. Embeddings and Semantic Search

Azure OpenAI can create vector embeddings that represent the meaning of text.

Use cases include:

Semantic search
Document similarity
Recommendation systems
Retrieval-augmented generation (RAG)

Exam tip:
If the scenario mentions searching based on meaning rather than keywords, think embeddings + Azure OpenAI.

6. Enterprise Security and Compliance

One of the most important exam points:

Azure OpenAI provides:

Data isolation
No training on customer data
Azure Active Directory integration
Role-Based Access Control (RBAC)
Compliance with Microsoft standards

This makes it suitable for regulated industries.

7. Integration with Azure Services

Azure OpenAI integrates with:

Azure AI Foundry
Azure AI Search
Azure Machine Learning
Azure App Service
Azure Functions
Azure Logic Apps

This allows organizations to build end-to-end generative AI solutions within Azure.

Common Use Cases Tested on AI-900

You should associate Azure OpenAI with:

Chatbots and conversational agents
Text generation and summarization
AI copilots
Semantic search
Code generation
Enterprise generative AI solutions

Azure OpenAI vs Other Azure AI Services (Exam Perspective)

Service	Primary Focus
Azure OpenAI	Generative AI using large language models
Azure AI Language	Traditional NLP (sentiment, entities, key phrases)
Azure AI Vision	Image analysis and OCR
Azure AI Speech	Speech-to-text and text-to-speech
Azure AI Foundry	End-to-end generative AI app lifecycle

Key Exam Takeaways

For AI-900, remember:

Azure OpenAI = Generative AI
Best for text, chat, code, and embeddings
Enterprise-ready with security and compliance
Uses pre-trained OpenAI models
Integrates with the broader Azure ecosystem

One-Line Exam Rule

If the question describes generating new content using large language models in Azure, the answer is likely related to Azure OpenAI Service.

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), Large Language Models (LLMs), Microsoft Certification January 31, 2026

Practice Questions: Describe features and capabilities of Azure AI Foundry model catalog (AI-900 Exam Prep)

Practice Questions

Question 1

What is the primary purpose of the Azure AI Foundry model catalog?

A. To store training datasets for Azure Machine Learning
B. To centrally discover, compare, and deploy AI models
C. To monitor AI model performance in production
D. To automatically fine-tune all deployed models

✅ Correct Answer: B

Explanation:
The Azure AI Foundry model catalog is a centralized repository that allows users to discover, evaluate, compare, and deploy AI models from Microsoft and partner providers. It is not primarily used for dataset storage or monitoring.

Question 2

Which types of models are available in the Azure AI Foundry model catalog?

A. Only Microsoft-built models
B. Only open-source community models
C. Models from Microsoft and multiple third-party providers
D. Only models trained within Azure Machine Learning

✅ Correct Answer: C

Explanation:
The model catalog includes models from Microsoft, OpenAI, Meta, Anthropic, Cohere, and other partners, giving users access to a diverse range of generative and AI models.

Question 3

Which feature helps users compare models within the Azure AI Foundry model catalog?

A. Azure Cost Management
B. Model leaderboards and benchmarking
C. AutoML pipelines
D. Feature engineering tools

✅ Correct Answer: B

Explanation:
The model catalog includes leaderboards and benchmark metrics, allowing users to compare models based on performance characteristics and suitability for specific tasks.

Question 4

What information is typically included in a model card in the Azure AI Foundry model catalog?

A. Only pricing details
B. Only deployment scripts
C. Metadata such as capabilities, limitations, and licensing
D. Only training dataset information

✅ Correct Answer: C

Explanation:
Model cards provide descriptive metadata, including model purpose, supported tasks, licensing terms, and usage considerations, helping users make informed decisions.

Question 5

Which deployment option allows you to consume a model without managing infrastructure?

A. Managed compute
B. Dedicated virtual machines
C. Serverless API deployment
D. On-premises deployment

✅ Correct Answer: C

Explanation:
Serverless API deployment (Models-as-a-Service) allows users to call models via APIs without managing underlying infrastructure, making it ideal for rapid development and scalability.

Question 6

What is a key benefit of having search and filtering in the model catalog?

A. It automatically selects the best model
B. It restricts models to one provider
C. It helps users quickly find models that match specific needs
D. It enforces Responsible AI policies

✅ Correct Answer: C

Explanation:
Search and filtering features allow users to narrow down models based on capabilities, provider, task type, and deployment options, speeding up model selection.

Question 7

Which AI workload is the Azure AI Foundry model catalog most closely associated with?

A. Traditional rule-based automation
B. Predictive analytics dashboards
C. Generative AI solutions
D. Network security monitoring

✅ Correct Answer: C

Explanation:
The model catalog is a core capability supporting generative AI workloads, such as text generation, chat, summarization, and multimodal applications.

Question 8

Why might an organization choose managed compute instead of a serverless API deployment?

A. To avoid version control
B. To reduce accuracy
C. To gain more control over performance and resources
D. To eliminate licensing requirements

✅ Correct Answer: C

Explanation:
Managed compute provides greater control over performance, scaling, and resource allocation, which can be important for predictable workloads or specialized use cases.

Question 9

Which scenario best illustrates the use of the Azure AI Foundry model catalog?

A. Writing SQL queries for data analysis
B. Comparing multiple large language models before deployment
C. Creating Power BI dashboards
D. Training image classification models from scratch

✅ Correct Answer: B

Explanation:
The model catalog is designed to help users evaluate and compare models before deploying them into generative AI applications.

Question 10

For the AI-900 exam, which statement best describes the Azure AI Foundry model catalog?

A. A low-level training engine for custom neural networks
B. A centralized hub for discovering and deploying AI models
C. A compliance auditing tool
D. A replacement for Azure Machine Learning

✅ Correct Answer: B

Explanation:
For AI-900, the key takeaway is that the model catalog acts as a central hub that simplifies model discovery, comparison, and deployment within Azure’s generative AI ecosystem.