Artificial intelligence has moved from an emerging technology to an essential part of modern cloud computing. Businesses are using AI for text analysis, speech recognition, computer vision, information extraction, generative AI, and intelligent applications.

For professionals beginning their AI journey, the Microsoft Azure AI Fundamentals certification provides a practical starting point for understanding AI concepts and implementing foundational AI solutions on Azure.

The AI-901: Microsoft Azure AI Fundamentals exam is the current Microsoft assessment for this certification. The English version was updated on April 15, 2026, so candidates should prepare against the latest Microsoft objectives rather than older AI-900 material. Microsoft currently lists AI-901 with no retirement date.

Practice preparation: Explore CertsVault

What Is the AI-901 Exam?

AI-901: Microsoft Azure AI Fundamentals is a beginner-level Microsoft certification exam for candidates starting their careers in AI solution development.

Microsoft expects candidates to have conceptual knowledge of AI solutions on Azure, foundational technical skills, familiarity with Azure resources, and some knowledge of Python syntax and programming techniques. Familiarity with REST APIs, SDKs, and CLIs is also recommended.

The certification is particularly useful for:

• Aspiring AI engineers
• Cloud professionals
• Developers beginning with Azure AI
• Students learning artificial intelligence
• IT professionals moving into AI
• Professionals who want foundational Azure AI knowledge

You don't need to be an advanced machine learning engineer to begin preparing, but the updated AI-901 exam does have a more implementation-oriented focus than simply memorizing AI terminology.

AI-901 Exam Skills at a Glance

The current Microsoft study guide divides AI-901 into two major domains:

This is an important change in preparation strategy.

More than half of the current exam focuses on implementing AI solutions using Microsoft Foundry, including generative AI applications, agents, model deployment, the Foundry SDK, prompts, and Content Understanding.

1. Understand Responsible AI

Before building AI solutions, you need to understand the principles that help organizations use AI responsibly.

Microsoft's current AI-901 objectives include six major responsible AI principles:

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

Fairness

An AI system should avoid systematically producing unfair outcomes for particular groups.

For example, if an AI-based decision system consistently performs worse for one demographic group, the organization should investigate the cause and address the problem.

Reliability and Safety

AI systems should behave consistently and safely within their intended scenarios.

Testing, monitoring, validation, and appropriate safeguards can help reduce unwanted outcomes.

Privacy and Security

AI solutions may process sensitive information.

Organizations therefore need to consider:

• Data protection
• Access control
• Authentication
• Secure storage
• Appropriate data usage

Inclusiveness

AI solutions should be designed to work effectively for people with different abilities, backgrounds, and circumstances.

Transparency

Users should have appropriate information about how AI systems operate and when AI is involved.

Accountability

Organizations and people remain responsible for AI systems and their outcomes.

These principles aren't simply theoretical. They help determine whether an AI solution is appropriate for a real-world scenario.

2. Understand Generative AI Models

Generative AI models can create new content based on patterns learned during training.

Depending on the model, generated content may include:

• Text
• Images
• Code
• Summaries
• Structured information
• Conversational responses

AI-901 expects candidates to understand how generative AI models work and how to select an appropriate model based on its capabilities.

When selecting a model, consider:

Task → Required capability → Model → Deployment → Configuration

The biggest model isn't automatically the best model.

Instead, select a model that fits the required workload, performance, cost, and deployment requirements.

3. AI Model Deployment and Configuration

AI-901 also covers model deployment options and configuration parameters.

Candidates should understand that deploying an AI model involves more than selecting a model name.

You may need to consider:

• Model capabilities
• Deployment options
• Input requirements
• Output behavior
• Performance
• Resource requirements
• Configuration parameters

A scenario may describe a business requirement and ask which model or deployment approach is most appropriate.

Therefore, learn to connect technical capabilities with workload requirements.

4. Understand Common AI Workloads

One of the most important AI-901 foundations is recognizing common AI workloads.

The current study guide includes:

• Generative AI
• Agentic AI
• Text analysis
• Speech
• Computer vision
• Information extraction

Understanding the difference between these workloads is essential.

Generative AI

Creates new content based on a prompt or other input.

Text Analysis

Analyzes existing text to identify useful information.

Speech

Processes spoken language, including recognition and synthesis.

Computer Vision

Processes and analyzes visual information.

Information Extraction

Extracts structured or meaningful information from unstructured sources such as text, images, audio, or video.

5. Text Analysis

AI-901 includes several common text analysis techniques.

You should understand:

• Keyword extraction
• Entity detection
• Sentiment analysis
• Summarization

Keyword Extraction

Identifies important words or phrases within text.

Entity Detection

Identifies entities such as:

• People
• Organizations
• Locations
• Dates
• Other recognized entities

Sentiment Analysis

Determines the expressed sentiment within text.

For example, customer feedback might be classified as positive, negative, or neutral.

Summarization

Produces a shorter version of a longer piece of content while retaining important information.

Understanding which technique matches a given business requirement is more useful than simply memorizing definitions.

6. Speech AI

AI-901 also includes speech capabilities.

Candidates should understand the difference between:

Speech recognition and speech synthesis.

Speech Recognition

Converts spoken language into text.

Example:

A customer speaks into a support application, and the system converts the speech into written text.

Speech Synthesis

Converts text into spoken language.

Example:

An application generates an audio response from written text.

Microsoft's current AI-901 objectives specifically include identifying features and capabilities of speech recognition and speech synthesis.

7. Computer Vision

Computer vision enables AI systems to process visual information.

AI-901 includes capabilities involving:

• Image analysis
• Computer vision
• Image-generation models

Possible business applications include:

• Analyzing photographs
• Identifying objects
• Extracting information from images
• Processing visual documents
• Generating images

The important exam skill is identifying the appropriate AI capability for a given scenario.

8. Extract Information From Multiple Media Types

Modern AI solutions don't work only with text.

The current AI-901 study guide expects candidates to understand techniques for extracting information from:

• Text
• Images
• Audio
• Video

Consider a company receiving invoices as scanned documents.

A suitable AI solution may need to:

1. Process the document image.
2. Identify relevant fields.
3. Extract values.
4. Convert the information into a structured format.
5. Pass the result into another business system.

This is an example of how multiple AI capabilities can work together.

9. Microsoft Foundry and AI-901

The second major AI-901 domain focuses on implementing AI solutions using Microsoft Foundry.

This domain represents 55–60% of the exam, making it the most heavily weighted area.

Candidates should become familiar with the Foundry experience and understand how it can be used to build and interact with AI solutions.

Key topics include:

• Generative AI applications
• AI agents
• Models
• System prompts
• User prompts
• Model deployment
• Foundry portal
• Foundry SDK
• Content Understanding

This is where hands-on preparation becomes especially valuable.

10. Create Effective System and User Prompts

AI-901 expects candidates to create effective system prompts and user prompts.

A system prompt establishes behavior or instructions for the AI model.

A user prompt communicates the task or request.

For example:

System instruction:

You are an assistant that summarizes technical documentation for beginners.

User request:

Summarize this Azure AI article in five bullet points.

The combination provides both behavioral context and a specific task.

Good prompts should be:

• Clear
• Specific
• Relevant
• Contextual
• Focused on the desired outcome

Avoid unnecessarily complicated instructions when a simple prompt can accomplish the same task.

11. Model Deployment in Microsoft Foundry

AI-901 candidates should understand how to deploy a model and interact with it through the Microsoft Foundry portal.

A typical workflow may involve:

Select model → Deploy model → Configure deployment → Interact with model → Evaluate results

The exact interface can evolve, so preparation should use the current Microsoft Foundry experience and documentation.

The important concept is understanding the relationship between:

• A model
• Its deployment
• Application access
• Prompt input
• Generated output

12. Build a Lightweight Chat Application

The current AI-901 objectives include creating a lightweight chat client application using the Foundry SDK.

This means candidates should have at least foundational familiarity with programmatic interaction with AI models.

A simplified architecture might look like:

User → Chat application → Foundry SDK → Deployed model → Response

You don't need to become a full-stack developer to understand this concept.

However, basic Python syntax and programming techniques are useful because Microsoft explicitly identifies Python knowledge as part of the expected candidate background.

13. Content Understanding

Another updated AI-901 topic is Content Understanding.

Microsoft includes building a lightweight application with information extraction capabilities by using Content Understanding.

This is important because businesses often work with unstructured content.

Examples include:

• Documents
• Images
• Audio
• Video

The objective is to extract meaningful information that applications can use.

For example, an organization might process business documents and extract:

• Names
• Dates
• Invoice numbers
• Addresses
• Amounts
• Other relevant fields

This converts unstructured content into useful structured information.

14. AI-901 vs. Older AI-900 Preparation

Candidates who previously studied AI-900 should pay close attention to the updated AI-901 objectives.

The current AI-901 exam places substantial emphasis on Microsoft Foundry implementation, including model deployment, prompts, Foundry SDK development, and Content Understanding.

That means simply studying older Azure AI Fundamentals material may not provide sufficient preparation for the current exam.

A better strategy is:

AI fundamentals + responsible AI + AI workloads + Microsoft Foundry + hands-on implementation

AI-901 Exam Details

Microsoft currently lists:

• Exam: AI-901
• Certification: Microsoft Certified: Azure AI Fundamentals
• Passing score: 700
• Price: $99 USD, subject to regional pricing and applicable taxes
• Retirement date: None currently listed
• English exam update: April 15, 2026

Microsoft also states that the exam is available in multiple languages, including English, Arabic, Chinese, French, German, Indonesian, Italian, Japanese, Korean, Portuguese, Russian, and Spanish.

How to Prepare for AI-901

A practical preparation strategy can be divided into four stages.

Stage 1: Learn AI Fundamentals

Start with:

• Responsible AI
• AI workloads
• Generative AI
• AI models
• Text analysis
• Speech
• Computer vision
• Information extraction

Make sure you understand when each capability should be used.

Stage 2: Focus on Microsoft Foundry

Spend additional time on:

• Foundry portal
• Model deployment
• Generative AI applications
• AI agents
• System prompts
• User prompts
• Foundry SDK
• Content Understanding

This deserves significant attention because it represents 55–60% of the current exam.

Stage 3: Get Hands-On

Don't study entirely from theory.

Try to:

1. Explore Microsoft Foundry.
2. Deploy an available model.
3. Test different prompts.
4. Build a simple chat experience.
5. Experiment with information extraction.
6. Review the output and improve your prompts.

Stage 4: Practice Scenario Questions

Practice identifying the correct Azure AI capability from a business scenario.

For example:

A company wants to convert recorded customer conversations into text.

Think:

Speech recognition.

Or:

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

Think:

Sentiment analysis.

This style of reasoning is extremely useful for fundamentals-level exams.

Five AI-901 Demo Practice Questions

The following are original practice examples for learning. They are not actual Microsoft exam questions.

Question 1: Sentiment Analysis

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

Which AI capability is most appropriate?

A. Sentiment analysis

B. Speech synthesis

C. Image generation

D. Object detection

Answer: A

Sentiment analysis evaluates the expressed opinion or emotional tone of text.

Question 2: Speech Recognition

A business wants an application to convert a customer's spoken support request into written text.

Which capability should be used?

A. Speech recognition

B. Speech synthesis

C. Image generation

D. Entity detection

Answer: A

Speech recognition converts spoken language into text.

Question 3: Generative AI

A company wants an AI application that can generate a draft email from a user's instructions.

Which workload is most appropriate?

A. Generative AI

B. Computer vision

C. Entity detection

D. Speech recognition

Answer: A

Generative AI can create new content such as emails, summaries, and other text based on instructions.

Question 4: Responsible AI

An organization wants to ensure that an AI solution does not expose sensitive customer information to unauthorized users.

Which responsible AI area is most directly relevant?

A. Privacy and security

B. Image generation

C. Speech synthesis

D. Summarization

Answer: A

Privacy and security address protecting sensitive information and ensuring appropriate access.

Question 5: Microsoft Foundry

A developer wants to build a lightweight Python chat application that communicates with a deployed generative AI model using Microsoft's current AI development platform.

Which capability should the developer investigate?

A. Microsoft Foundry SDK

B. Speech synthesis only

C. Computer vision only

D. Keyword extraction only

Answer: A

The current AI-901 objectives explicitly include creating a lightweight chat client application using the Foundry SDK.

Practice Questions by CertsVault

Additional AI-901 practice questions by CertsVault can help you reinforce the concepts covered throughout the exam.

AI-901 Practice Questions by CertsVault

When practicing, don't focus only on memorizing the correct option. Review the scenario and identify the underlying AI workload, Azure capability, or Microsoft Foundry feature being tested.

Official Microsoft AI-901 Study Resources

For the most accurate preparation, use Microsoft's current documentation alongside hands-on practice.

Microsoft AI-901 Official Exam Page

The official exam page provides current exam information, languages, pricing guidance, preparation options, and certification details.

Microsoft AI-901 Exam

AI-901 Official Study Guide

The study guide is particularly important because it identifies the current skills measured as of April 15, 2026.

Microsoft AI-901 Study Guide

Azure AI Fundamentals Certification

Review the official certification page for Microsoft Certified: Azure AI Fundamentals.

Microsoft Certified: Azure AI Fundamentals

Microsoft Foundry Documentation

Use Microsoft's official Microsoft Foundry documentation to build practical knowledge of the platform.

Microsoft Foundry Documentation

Microsoft AI Learning Resources

Microsoft provides learning paths covering AI concepts and getting started with AI applications and agents on Azure.

Microsoft Learn AI Training

Microsoft Exam Sandbox

Before taking the exam, candidates can familiarize themselves with the exam interface through Microsoft's exam sandbox.

Microsoft Exam Sandbox

Final Thoughts

The AI-901 Azure AI Fundamentals exam is a strong starting point for anyone entering Microsoft's AI ecosystem.

However, the current version of AI-901 is not simply a terminology exam. The updated objectives place significant emphasis on Microsoft Foundry, making practical familiarity with model deployment, prompts, SDK usage, generative AI applications, and Content Understanding increasingly important.

Focus your preparation on:

Responsible AI → AI workloads → Generative AI → AI models → Text analysis → Speech → Computer vision → Microsoft Foundry → Prompts → Model deployment → Foundry SDK → Content Understanding

The most effective preparation combines official Microsoft Learn resources, hands-on Azure AI practice, and scenario-based questions.

If you understand not only what an AI capability does but also when and why you would use it, you'll be in a much stronger position for AI-901 and for your first real-world Azure AI projects.