AI-901 Microsoft Azure AI Fundamentals (Beta) Exam Guide 2026

The AI-901 Microsoft Azure AI Fundamentals (Beta) exam is designed for candidates looking to demonstrate foundational knowledge of machine learning, artificial intelligence concepts, responsible AI, generative AI, Microsoft Foundry, and related Microsoft Azure AI services. This guide details the essential technical elements, core exam domains, and preparation strategies required to successfully navigate the updated blueprint.

Quick Facts

Recent Certification Changes

Microsoft introduced the AI-901 exam as an updated foundational assessment reflecting modern advancements in artificial intelligence while aligning with the latest Azure AI ecosystem and Microsoft Foundry platform.

Compared to earlier Azure AI fundamentals exams, the AI-901 blueprint now places greater emphasis on:

• Microsoft Foundry
• Generative AI applications
• AI agents and agentic AI concepts
• Prompt engineering
• AI application development
• Text analysis, speech, computer vision, image generation, and information extraction workloads
• Responsible AI implementation

Candidates should understand not only core AI concepts, but also how Microsoft Foundry is used to build, deploy, test, and interact with AI models and agents. The exam expects foundational awareness of SDKs, REST APIs, CLIs, Azure resources, prompts, and AI workloads.

AI-900 vs AI-901

Blueprint & Strategy

Key Takeaways

• Focus heavily on responsible AI principles, machine learning fundamentals, model capabilities, deployment options, and common AI workloads.
• Understand the role of Microsoft Foundry in building AI applications and AI agents.
• Learn how generative AI models use prompts, grounding, embeddings, tokenization, and contextual information to produce useful responses.
• Understand how AI agents can use tools, retrieve information, reason through tasks, and perform multi-step workflows.
• Learn to distinguish between computer vision, natural language processing, speech, image generation, and information extraction workloads.
• Maintain familiarity with traditional machine learning concepts, including classification, regression, clustering, and model evaluation metrics.

Certification Path

This is a foundational certification. There are no prerequisites required before sitting for the AI-901 exam.

Achieving this certification establishes a baseline of knowledge useful for advanced certifications such as:

• Microsoft Certified: Azure AI Apps and Agents Developer Associate (AI-103)
• Microsoft Certified: Azure AI Engineer Associate (AI-102)
• Other Azure AI and data-focused certifications

Skills Measured

The exam is structured around key technical domains, each carrying specific weight and focusing on testable, objective skills:

Identify AI concepts and responsibilities (40–45%)

• Describe responsible AI principles including fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability.
• Identify AI model components, capabilities, deployment options, and configuration parameters.
• Recognize common AI workloads including generative AI, agentic AI, text analysis, speech, computer vision, image generation, and information extraction.

Implement AI solutions using Microsoft Foundry (55–60%)

• Create effective system and user prompts for generative AI models.
• Deploy models and interact with them in Microsoft Foundry.
• Build lightweight AI applications using Foundry SDKs, REST APIs, CLIs, and Azure resources.
• Create and test AI agent solutions.
• Implement text analysis, speech, computer vision, image generation, and information extraction solutions, including scenarios that use Azure Content Understanding in Foundry Tools.

High-Yield Topics Table

Knowledge Explanations

Responsible AI Principles

Responsible AI refers to designing, developing, and using AI systems in ways that are ethical, safe, reliable, and aligned with human values.

Microsoft organizes responsible AI into six major principles:

• Fairness
• Reliability and Safety
• Privacy and Security
• Inclusiveness
• Transparency
• Accountability

These principles help organizations design systems that minimize bias, protect sensitive information, improve user trust, and ensure appropriate oversight throughout the AI lifecycle.

• Exam Relevance: Questions may present scenarios involving bias, unsafe outputs, privacy concerns, lack of transparency, or inadequate governance.
• Practical Consideration: A hiring model that consistently favors one demographic group may violate fairness principles, while exposing customer data may violate privacy and security principles.
• Common Pitfall: Treating responsible AI as a purely ethical discussion instead of understanding how it influences practical engineering and deployment decisions.

Microsoft Foundry Fundamentals

Microsoft Foundry is Microsoft's platform for building, testing, deploying, and managing AI applications and agents.

Candidates should understand how Foundry supports:

• Model deployment
• Prompt management
• AI application development
• Agent creation
• Tool integration
• Evaluation and testing workflows

The exam focuses on understanding when and why these capabilities are used rather than requiring advanced implementation skills.

• Exam Relevance: Questions may ask candidates to identify the appropriate Foundry capability for a specific AI development scenario.
• Practical Consideration: Organizations can use Foundry to deploy models, build AI assistants, create agents, and integrate enterprise knowledge sources.
• Common Pitfall: Assuming AI-901 only covers theoretical AI concepts without practical awareness of Microsoft's AI development platform.

Model Evaluation Metrics

Model evaluation metrics are quantitative measures used to assess the performance of a trained machine learning model.

In supervised learning, different metrics apply to regression and classification tasks.

For regression models, Mean Squared Error (MSE) measures the average squared difference between predicted values and actual values.

For classification models:

• Accuracy measures overall prediction correctness.
• Precision measures the percentage of predicted positives that are actually positive.
• Recall measures the percentage of actual positives that are successfully identified.
• F1-Score provides a balanced measure of Precision and Recall.

Although the newest AI-901 blueprint places less emphasis on machine learning theory than previous versions, these concepts remain foundational and may still appear in conceptual questions.

• Exam Relevance: Questions may present scenarios where a specific type of error (False Positive or False Negative) must be minimized.
• Practical Consideration: Medical diagnostic systems often prioritize Recall because missing a true positive could have serious consequences.
• Common Pitfall: Relying solely on Accuracy when evaluating highly imbalanced datasets.

Embeddings and Tokenization

Tokenization is the process of breaking text into smaller units called tokens. These tokens may represent words, sub-words, or individual characters depending on the tokenization strategy.

Embeddings are numerical vector representations of text that allow AI systems to capture semantic meaning. Words, phrases, and documents with similar meanings tend to occupy nearby positions in vector space.

Embeddings are fundamental to:

• Semantic search
• Retrieval-Augmented Generation (RAG)
• Knowledge retrieval
• Similarity matching
• AI grounding techniques
• Exam Relevance: Candidates may be tested on how language models process text and how semantic similarity is determined.
• Practical Consideration: Internal company documents can be converted into embeddings to support enterprise AI assistants.
• Common Pitfall: Assuming token count is equivalent to word count. A single word may be represented by multiple tokens.

Prompt Design and Generative AI Behavior

Prompt design is the process of creating instructions that guide the behavior of generative AI models.

Prompts can define:

• The role of the AI assistant
• Expected output format
• Behavioral constraints
• Task objectives
• Tone and style requirements

Effective prompts often improve response quality without requiring retraining or fine-tuning.

Microsoft Foundry uses prompts extensively when building AI applications and agents.

• Exam Relevance: Candidates may be required to identify how prompts influence model behavior or choose the most appropriate prompt strategy.
• Practical Consideration: Customer service assistants often use system prompts to ensure consistent responses that comply with organizational policies.
• Common Pitfall: Assuming that AI models automatically understand business requirements without explicit instructions.

AI Agents

AI agents extend traditional generative AI systems by combining models with memory, tools, planning capabilities, and external knowledge sources.

Unlike a simple chatbot, an AI agent can:

• Retrieve information
• Call external tools
• Execute actions
• Complete multi-step workflows
• Reason through complex tasks

Agent-based systems are becoming increasingly important within Microsoft's AI ecosystem and are a significant area of focus within Microsoft Foundry.

• Exam Relevance: Questions may ask candidates to identify when an agent is more appropriate than a standalone generative AI model.
• Practical Consideration: An IT support agent can answer questions, retrieve documentation, create tickets, and escalate issues automatically.
• Common Pitfall: Confusing a language model with an AI agent. Models generate responses, while agents can perform actions and interact with external systems.

Computer Vision: Classification vs. Object Detection

Computer vision enables AI systems to understand and interpret visual information.

Image classification assigns a label to an entire image.

Object detection goes further by identifying individual objects within an image and determining their locations using bounding boxes.

Other common computer vision workloads include:

• Optical Character Recognition (OCR)
• Image analysis
• Facial analysis
• Image generation
• Multimodal AI
• Exam Relevance: Candidates must select the appropriate computer vision capability based on business requirements.
• Practical Consideration: Manufacturing quality-control systems often use object detection to locate defects on products.
• Common Pitfall: Selecting image classification when object localization is required.

Information Extraction

Information extraction uses AI to identify and structure information from unstructured or semi-structured content.

Common examples include:

• Invoice processing
• Contract analysis
• Form extraction
• Receipt processing
• Document intelligence

Extracted information may include:

• Names
• Dates
• Invoice numbers
• Key-value pairs
• Tables
• Financial figures
• Exam Relevance: Questions may require candidates to identify when information extraction is the correct AI workload.
• Practical Consideration: Finance departments commonly use document intelligence systems to automate invoice processing.
• Common Pitfall: Confusing information extraction with text generation.

Grounding and Hallucination Mitigation

Grounding is the process of providing verified, contextually relevant information to a generative AI model before it produces a response.

Grounding plays a critical role in Retrieval-Augmented Generation (RAG) architectures.

Its primary purpose is to reduce hallucinations.

Hallucinations occur when a model generates information that sounds plausible but is factually incorrect or entirely fabricated.

Grounding can be implemented using:

• Enterprise knowledge bases
• Internal documentation
• Databases
• Search systems
• Vector search indexes
• Exam Relevance: Candidates may need to identify techniques for improving response accuracy without retraining a model.
• Practical Consideration: Organizations frequently use vector search and RAG to ensure AI assistants answer questions using approved internal content.
• Common Pitfall: Assuming that fine-tuning is always the best solution for hallucination reduction when grounding often provides a simpler and more reliable approach.

Sample Questions

Question 1

A company wants to build an AI assistant that answers questions from internal company documents and can also create support tickets when users need further assistance.

Which Microsoft Foundry solution type is most appropriate?

A. Agent

B. Clustering

C. Regression

D. Reinforcement Learning

Correct Answer: A

Explanation: An AI agent can retrieve information from internal knowledge sources and perform actions such as creating support tickets or interacting with external systems. This makes an agent more appropriate than a standalone model for multi-step business workflows. Clustering groups similar data, regression predicts numeric values, and reinforcement learning focuses on learning through rewards and actions.

Question 2

You are designing a customer support chat application using a generative AI model. During testing, the model occasionally provides plausible-sounding but completely fabricated answers regarding company return policies.

Which technique should you implement to resolve this issue without altering the model's core weights?

A. Increase the training dataset size

B. Implement grounding using Retrieval-Augmented Generation

C. Use unsupervised clustering on customer queries

D. Apply image classification to user avatars

Correct Answer: B

Explanation: Providing fabricated information is known as hallucination. Grounding the model using Retrieval-Augmented Generation provides verified external policy documents during the query process, helping it generate responses based on approved information. Increasing the training dataset size would require retraining or fine-tuning. Clustering groups similar items but does not improve factual accuracy. Image classification is unrelated to the problem.

Question 3

An automated security system needs to scan images of a facility perimeter. The system must identify if an intruder is present and also determine the exact location of the intruder within the frame to direct a security camera.

Which computer vision capability fits this requirement?

A. Image Classification

B. Optical Character Recognition

C. Object Detection

D. Named Entity Recognition

Correct Answer: C

Explanation: Object detection identifies individual objects within an image and determines their location using bounding boxes. Image classification only determines whether an object exists in an image and does not provide location information. OCR extracts text, and Named Entity Recognition is a natural language processing task.

Question 4

A developer wants to create a lightweight AI application that sends user prompts to a deployed model and receives generated responses.

Which tool type should the developer understand at a foundational level for this scenario?

A. Foundry SDK

B. Disk Defragmentation Utility

C. Spreadsheet Macro

D. Firewall Rule Analyzer

Correct Answer: A

Explanation: Foundry SDKs allow developers to build lightweight AI applications that interact with deployed models and AI services. AI-901 does not require advanced programming skills, but candidates should understand the purpose of SDKs, APIs, CLIs, and Azure resources.

Question 5

A healthcare organization is evaluating an AI model used to detect rare diseases. Missing a positive case could result in delayed treatment.

Which metric should the organization prioritize?

A. Accuracy

B. Precision

C. Recall

D. Mean Squared Error

Correct Answer: C

Explanation: Recall measures how many actual positive cases are correctly identified. In medical scenarios, minimizing false negatives is often more important than minimizing false positives because missed cases can have severe consequences.

Prep & Close

Exam Difficulty Analysis

The AI-901 exam is considered a foundational certification but requires strong conceptual understanding across multiple AI domains. Candidates should be comfortable with machine learning basics, responsible AI principles, Microsoft Foundry concepts, AI agents, prompt engineering, embeddings, grounding, and common Azure AI workloads.

The exam does not require production-level coding, advanced mathematics, or enterprise-scale architecture design. However, candidates should understand how AI technologies are applied in real-world business scenarios and be able to select the most appropriate solution based on requirements.

Candidates who are completely new to AI should spend additional time learning:

• Core AI terminology
• Machine learning concepts
• Responsible AI principles
• Microsoft Foundry fundamentals
• Generative AI workflows
• Common Azure AI services

Career Opportunities

Earning the Microsoft Certified: Azure AI Fundamentals certification validates your understanding of modern AI concepts and Azure AI technologies.

Potential career paths include:

• AI Solution Specialist
• Technical Sales Consultant
• Cloud Support Engineer
• Junior AI Engineer
• AI Product Specialist
• Business Technology Consultant
• Technical Account Manager
• Entry-Level Data and AI Professional

Although AI-901 is an entry-level certification, it provides a strong foundation for more advanced Azure AI certifications and real-world AI projects.

How AAAdemy Helps You Prepare

AAAdemy provides structured resources designed to guide you through the complete certification preparation process:

Objectives → Learning → High-Yield Topics → Practice → Assessment

• Phase 1: Review the official objectives to establish your baseline knowledge.
• Phase 2: Study detailed concept explanations covering machine learning, responsible AI, Microsoft Foundry, generative AI, AI agents, and Azure AI workloads.
• Phase 3: Focus on high-yield topics and common exam scenarios.
• Phase 4: Practice with realistic exam-style questions and explanations.
• Phase 5: Complete full-length assessments to evaluate readiness before scheduling your exam.

Related Certifications

FAQ

• Is AI-901 replacing AI-900?

  Yes. AI-900 is scheduled to retire, and AI-901 is the updated exam for the Microsoft Certified: Azure AI Fundamentals certification path.

• Does the AI-901 exam require writing Python or C# code?

  AI-901 does not require advanced software engineering experience or production-level coding. However, candidates should understand basic Python coding syntax, common programming techniques, Azure resources, and the role of REST APIs, SDKs, and CLIs when working with Microsoft Foundry solutions.

• How does this exam address generative AI compared to older Azure fundamentals exams?

  The AI-901 blueprint places greater emphasis on generative AI applications, Microsoft Foundry, prompt engineering, AI agents, grounding, multimodal AI workloads, and practical AI solution development concepts.

• What is Microsoft Foundry in the context of AI-901?

  Microsoft Foundry is Microsoft's platform for building, testing, deploying, and managing AI applications and agents. The exam focuses on understanding its purpose and capabilities rather than advanced implementation.

• What is the retake policy if I do not pass the AI-901 exam on my first attempt?

  Microsoft generally requires a waiting period before retaking exams. Candidates should always verify the latest retake policy through Microsoft's certification website because policies may change over time.

• Is there a lab component where I have to configure services in the Azure Portal?

  No. Fundamentals exams generally focus on conceptual understanding through multiple-choice, drag-and-drop, and scenario-based questions.

• Are Responsible AI principles tested heavily on this exam?

  Yes. Responsible AI is one of the most important conceptual domains in the AI-901 blueprint. Candidates should understand how each principle applies to real-world AI scenarios.

• How long is the Azure AI Fundamentals certification valid?

  Fundamentals certifications currently do not require periodic renewal. However, candidates should verify Microsoft's latest certification policies.

• Does this exam cover third-party AI frameworks like PyTorch or TensorFlow?

  Only at a conceptual level. The focus remains on Azure AI concepts, Microsoft Foundry, AI workloads, and foundational AI knowledge rather than third-party framework implementation.

Start Practicing AI-901 for Free with Detailed Knowledge Explanations on AAAdemy

• Define AI Concepts and Responsibilities – AI-901 Certification Knowledge Guide
• Implement AI Solutions using Microsoft Foundry – AI-901 Certification Knowledge Guide

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Explore the Complete AI-901 Training Course:

Microsoft AI-901 Microsoft Azure AI Fundamentals Certification Training Course - AAAdemy