Microsoft AI-901 Exam Dumps – [May 2026] Azure AI Fundamentals

The AI-901 (Microsoft Azure AI Fundamentals) entered beta on April 21, 2026, and becomes the only path to the Microsoft Certified: Azure AI Fundamentals credential after AI-900 retires on June 30, 2026. The exam covers two domains: Identify AI Concepts and Responsibilities (40–45%) and Implement AI Solutions by Using Microsoft Foundry (55–60%). The passing score is 700 out of 1000. Duration is 40–60 questions in 45 minutes. The certification has lifetime validity and never expires. AI-901 is a substantial redesign from AI-900 — it shifts from recognizing what Azure AI services do to building real lightweight applications inside the Microsoft Foundry platform. CertEmpire’s AI-901 dumps are built directly from the official Microsoft skills blueprint updated April 15, 2026.

The Number That Defines This Exam: 55–60%

Before you look at any topic list, understand the weight distribution. It changes everything about how you should prepare.

More than half the exam — potentially three questions in every five — tests your ability to implement AI solutions inside Microsoft Foundry. Domain 1 carries the conceptual and responsible AI knowledge from AI-900’s tradition. Domain 2 is where AI-901 is genuinely new: prompts, model deployment, agent creation, multimodal applications, vision capabilities, and information extraction, all implemented through the Foundry portal and SDK.

Candidates who prepare equally across both domains will be underweighted in Domain 2. Candidates who understand Foundry implementation well enough to answer application-building questions will be aligned with where the exam actually places its marks.

What Changed from AI-900 to AI-901: The Full Picture

AI-901 is not an update — it is a redesign. The conceptual foundation carries over, but the implementation layer is entirely new.

The clearest way to describe the shift: AI-900 asked “what is this AI service?” AI-901 asks “build something with this AI capability in Foundry.”

What Is Microsoft Foundry?

Most prep materials describe AI-901’s topics without explaining what Microsoft Foundry actually is — which makes the Foundry implementation questions harder to reason about.

Microsoft Foundry (formerly Azure AI Studio) is Microsoft’s unified platform for building, deploying, and managing AI applications on Azure. It provides a single portal where developers can:

• Browse and deploy AI models from the Foundry model catalog (including OpenAI models, Meta Llama, Mistral, and others)
• Create and test generative AI applications through the Foundry portal’s chat playground
• Build and deploy AI agents that can use tools, reason through tasks, and take actions
• Access built-in tools for Azure Speech, Azure AI Language text analysis, and Azure Content Understanding
• Use the Foundry SDK (available in Python and other languages) to build lightweight client applications that call deployed models and services

The Foundry SDK is the programmatic counterpart to the Foundry portal. Where the portal is a visual interface for deployment and testing, the SDK enables developers to call Foundry-deployed models and services from application code. The AI-901 exam tests basic Foundry SDK usage — reading and understanding Python client code that calls a deployed model — not writing complex applications from scratch.

Key Takeaway: Microsoft Foundry is not a single AI service — it is the platform that unifies access to AI models, development tools, and deployment infrastructure. The AI-901 exam assumes you can navigate the Foundry portal, deploy a model, test it in the playground, and understand what a basic Foundry SDK client application does. Candidates who treat “Foundry” as just another Azure service name will struggle with 55–60% of the exam.

What Is the AI-901 Exam?

The AI-901 is the Microsoft Azure AI Fundamentals certification exam, rebuilt for the Foundry era. It validates your understanding of AI concepts, responsible AI principles, and practical ability to implement generative AI applications, agents, and AI-powered solutions using the Microsoft Foundry platform.

Beta exam note: Beta exam results are not immediate. Microsoft holds beta results while the exam scoring is finalized, then releases results approximately 10 days after the exam goes live (expected June 2026). If you need certification quickly, wait for general availability.

Domain 1: Identify AI Concepts and Responsibilities (40–45%)

Domain 1 covers the foundational knowledge that is expected of every AI-901 candidate — principles, capabilities, workloads, and model concepts. This domain builds on AI-900 tradition but with updated framing for 2026.

Describe Principles of Responsible AI

The six Microsoft Responsible AI principles remain central to the exam and appear in scenario-based questions across both domains, not only in Domain 1.

Scenario-based responsible AI questions describe a real situation — an AI hiring tool producing systematically different outcomes for candidates from different demographic groups — and ask which principle is being violated (Fairness) or which remediation step addresses which principle. Reading the scenario carefully for signal language is the exam skill.

Identify AI Model Components and Configurations

This section is new to AI-901 and reflects the generative AI model landscape. Topics include:

How generative AI models work — large language models (LLMs) as statistical models trained on text corpora, the transformer architecture at a conceptual level, tokenization as how text is converted into numerical representations the model processes, context windows as the amount of text a model can process in a single interaction, and how models generate output by predicting likely next tokens.

Identifying an appropriate AI model based on capabilities — different models have different strengths: text generation, code generation, image generation, speech processing, and multimodal understanding (processing both text and images simultaneously). The exam tests which model type is appropriate for a described capability requirement, not which specific model version.

Model deployment options and configuration parameters — deploying a model in Foundry involves selecting a deployment type (standard, global-standard, or serverless API depending on throughput and latency requirements) and configuring parameters such as temperature (controls randomness/creativity of output), maximum tokens (limits response length), and top-p (controls diversity of token selection). The exam tests which parameters affect which output characteristics.

Identify AI Workloads

AI workload identification covers the full range of AI capabilities implemented through Foundry, each with specific techniques and features.

Generative and agentic AI — generative AI produces new content (text, images, code, audio) based on learned patterns. Agentic AI goes further: AI agents can reason through goals, select tools to use, execute actions, and iterate toward completing complex multi-step tasks without explicit step-by-step instructions. The distinction between generative AI (producing output from a prompt) and agentic AI (taking goal-directed action sequences) is specifically testable.

Text analysis — keyword extraction identifies significant terms in text, entity detection identifies named entities such as people, places, organizations, dates, and phone numbers, sentiment analysis determines positive/negative/neutral tone, and summarization condenses long text into key points. The exam tests which technique is appropriate for a described text processing requirement.

Speech recognition and synthesis — speech recognition (speech-to-text) converts spoken audio to text, and speech synthesis (text-to-speech) converts text to spoken audio. Specific features tested include real-time transcription, batch transcription for pre-recorded audio, speaker diarization for identifying different speakers, custom neural voice for creating branded speech synthesis voices, and speech translation for real-time multilingual spoken communication.

Computer vision and image generation — computer vision capabilities include image classification (categorizing images), object detection (identifying and locating objects within images), optical character recognition (OCR for reading text in images), and facial analysis. Image generation creates new images from text prompts using generative models. The exam tests both understanding these capabilities conceptually and understanding how multimodal models in Foundry can interpret visual inputs alongside text.

Information extraction — extracting structured information from unstructured documents, images, audio, and video. Azure Content Understanding is the Foundry-native service for this capability, replacing the older Azure AI Document Intelligence concept at the AI-901 level. Content Understanding can extract key-value pairs from forms, tables from documents, transcriptions from audio, and structured data from video. This is a new capability area with no direct AI-900 equivalent.

Domain 2: Implement AI Solutions by Using Microsoft Foundry (55–60%)

Domain 2 is the defining domain of AI-901 — it is why this exam is meaningfully different from AI-900 and why it demands a different preparation approach.

Implement Generative AI Apps and Agents by Using Foundry

This section covers the core Foundry implementation workflow for generative AI.

Effective system and user prompts — prompt engineering is the practice of crafting inputs to AI models to produce better outputs. System prompts define the model’s role, constraints, and behavior for all interactions in a session. User prompts are the specific inputs provided at interaction time. The exam tests which prompt elements achieve which output behaviors: adding persona instructions to the system prompt makes the model respond as a specific character; adding constraints prevents the model from discussing topics outside its defined scope; using few-shot examples in the prompt demonstrates the format expected in responses.

Deploying a model and interacting with it in the Foundry portal — the Foundry portal provides a visual interface for deploying models from the model catalog, configuring deployment parameters, and testing the deployed model in the chat playground. The exam tests the deployment workflow: selecting a model, choosing a deployment name and type, setting configuration parameters, and testing the deployment. Candidates should understand what the Foundry portal looks like and what operations are available in its main navigation areas.

Creating a lightweight chat client application using the Foundry SDK — the Foundry SDK allows developers to call a deployed model from application code. A basic Foundry SDK Python client creates an AzureOpenAI or AzureChatClient object, passes an endpoint URL and API key (or uses managed identity for authentication), and sends a message in the format the deployed model expects. The exam tests reading and understanding this code pattern — which line establishes the connection, which line sends the prompt, which line reads the response — not writing the code from scratch.

Creating and testing a single-agent solution in the Foundry portal — an AI agent in Foundry is a system that uses an AI model as its reasoning engine and can call tools (defined functions, Azure services, or external APIs) to take actions. The Foundry portal provides a visual interface for defining an agent’s instructions, specifying which tools it has access to, and testing the agent through conversation. The exam tests the conceptual model: what makes an agent different from a simple prompt-response interaction, what tools are and how agents select which tool to use, and what the agent loop looks like (receive goal → reason → select action → execute → observe result → iterate).

Creating a lightweight client application for an agent — analogous to the chat client, an agent client application connects to a deployed Foundry agent through the SDK and sends user requests that the agent resolves by reasoning and using tools. The exam tests understanding the structure of this client code.

Implement AI Solutions for Text and Speech by Using Foundry

Text analysis — building a lightweight application that uses Azure AI Language capabilities through Foundry Tools to analyze text. The exam tests recognizing which Foundry capability to use for a described text processing requirement and understanding what the application code does when it calls a text analysis function.

Speech from a deployed multimodal model — modern multimodal models can process spoken audio input and respond with spoken audio output through Foundry. The exam tests how to use a deployed multimodal model to respond to spoken prompts — sending an audio input to the model and receiving a response.

Azure Speech in Foundry Tools — Azure Speech is accessible through Foundry Tools for speech recognition and synthesis. Building a lightweight application using Azure Speech in Foundry means creating a client that connects to Azure Speech through the Foundry Tools integration and calls speech-to-text or text-to-speech capabilities.

Implement AI Solutions with Computer Vision and Image Generation by Using Foundry

Interpreting visual input using a multimodal model — multimodal models can accept both text and image inputs in the same prompt. The exam tests using a deployed multimodal model to interpret an image — providing an image URL or base64-encoded image along with a text instruction and receiving a text description or analysis of the image.

Creating visual outputs using generative models — image generation models create new images from text descriptions (prompts). The exam tests how to call an image generation model through Foundry, how to structure the prompt for effective image generation, and what the API response contains (typically an image URL or base64-encoded image data).

Building a lightweight application with vision capabilities — creating a Python client application that sends image data to a Foundry-deployed multimodal or vision model and processes the response. The exam tests understanding the structure of this application code.

Implement AI Solutions for Information Extraction by Using Foundry

Azure Content Understanding is the most distinctive new topic in AI-901 with no direct AI-900 equivalent. It is a Foundry-native service for extracting structured information from unstructured documents, images, audio, and video.

Extracting information from documents and forms — Content Understanding can analyze scanned or digital documents and extract key-value pairs (field name and value from a form), tables, and text content. The exam tests what Content Understanding does for document analysis and what the application code looks like when it calls Content Understanding to process a document.

Extracting information from images — Content Understanding can analyze images and extract text, structured data, and described visual content. The exam tests how Content Understanding differs from basic OCR (it extracts structured information aligned with a defined schema, not just raw text) and when it is the appropriate tool versus simpler OCR approaches.

Extracting information from audio and video — Content Understanding can transcribe audio content and extract information from video including transcription, visual content description, and structured data extraction. The exam tests which Content Understanding capability applies to which media type.

Building a lightweight application with information extraction — creating a Python client that submits a document, image, audio file, or video to Content Understanding and processes the structured response. The exam tests understanding the code structure for this type of application.

What Python Knowledge Does AI-901 Actually Require?

The official Microsoft skills profile for AI-901 states: “You also need knowledge of Python coding syntax and programming techniques.” This raises an immediate question for non-developer candidates.

In practice, the AI-901 exam does not require writing Python code from scratch or debugging complex applications. The exam presents short Python code snippets — typically 10–25 lines — and asks you to identify what a specific line does, which variable holds the model response, or which SDK object establishes the connection. Reading comprehension of code, not code writing, is the practical skill required.

Specifically, you should be comfortable reading:

• Import statements (from azure.ai.inference import ChatCompletionsClient)
• Object creation with named parameters (client = ChatCompletionsClient(endpoint=endpoint, credential=api_key))
• Method calls (response = client.complete(messages=[…]))
• Response access patterns (print(response.choices[0].message.content))

Non-developers who spend 2–3 hours studying Foundry SDK Python examples through the Microsoft Learn learning path will have sufficient exposure to answer these questions confidently.

What CertEmpire’s AI-901 Exam Dumps Include

PDF Dumps — Instant Download. Built directly from the official Microsoft AI-901 skills blueprint updated April 15, 2026. Domain 2 (Foundry implementation) receives proportional question density matching the 55–60% exam weight — the same emphasis that the real exam applies. Coverage spans Foundry portal deployment, Foundry SDK client code reading, single-agent creation, multimodal model usage, Azure Content Understanding for all four media types, and prompt engineering for system and user prompts. Domain 1 covers all six Responsible AI principles in scenario format, model selection by capability, and the full range of AI workload types including the new agentic AI distinction. Preview a free demo.

Timed Exam Simulator. 40–60 questions in 45 minutes. Domain-level performance tracking for both Domain 1 and Domain 2, with Foundry implementation sub-topic tracking to identify which implementation areas need more preparation. Full practice test library.

Explanation-Backed Answers. Every answer identifies the specific Foundry capability, responsible AI principle, or AI workload type being tested and explains why each wrong option fails. For Foundry SDK code questions, explanations walk through what each code element does. For agent questions, explanations trace the reasoning-action loop.

90-Day Free Updates. Money-Back Guarantee. Content reflects the AI-901 beta exam as launched April 21, 2026, with the skills measured document updated April 15, 2026. All purchases include 90 days of free updates as the exam progresses from beta to general availability and beyond.

AI-901 Preparation at a Glance

The Azure AI Certification Path After AI-901

The AI-901 is the starting point — the entry credential that establishes AI fundamentals before advancing to role-specific certifications.

AI-901 is not a prerequisite for these advanced certifications — you can sit any of them directly. But the conceptual and practical foundation AI-901 builds makes the transition to AI-103 or AI-200 significantly more manageable.

Related Microsoft Certifications at CertEmpire

• Microsoft AZ-900 exam dumps — Azure Fundamentals, the cloud infrastructure credential that complements AI-901 for a complete Azure foundations profile

Browse our full Microsoft certification catalog.

Frequently Asked Questions About AI-901

What is the AI-901 exam? 

AI-901 is the Microsoft Azure AI Fundamentals (Refreshed) exam, currently in beta since April 21, 2026, with general availability expected June 2026. It validates foundational AI concepts and the ability to implement AI solutions using Microsoft Foundry. The exam has two domains: AI Concepts and Responsibilities (40–45%) and Implement AI Solutions by Using Microsoft Foundry (55–60%). Passing score is 700 out of 1000. Duration is 45 minutes with 40–60 questions. The certification has lifetime validity.

What is the difference between AI-900 and AI-901? 

AI-901 is a substantial redesign of AI-900, not just an update. AI-900 tested recognizing Azure AI services and understanding what they do. AI-901 tests implementing AI solutions inside the Microsoft Foundry platform — deploying models, building lightweight applications, creating agents, using multimodal models, and extracting information with Azure Content Understanding. AI-901 also expects basic Python code reading familiarity. The Responsible AI principles carry over between both exams.

What is Microsoft Foundry? 

Microsoft Foundry (formerly Azure AI Studio) is Microsoft’s unified platform for building, deploying, and managing AI applications on Azure. It provides a portal for deploying models from the Foundry catalog, a chat playground for testing, built-in tools for Azure AI capabilities, and the Foundry SDK for building Python client applications. The AI-901 exam assumes you can navigate Foundry, deploy a model, test it, and understand Foundry SDK client application code.

What is Azure Content Understanding?

Azure Content Understanding is a Foundry-native service for extracting structured information from unstructured documents, images, audio, and video. It is new in AI-901 with no direct AI-900 equivalent. It can extract key-value pairs from forms, tables from documents, transcriptions from audio, and structured data from video. The AI-901 exam tests Content Understanding for all four media types.

What is an AI agent in the AI-901 context?

An AI agent in Foundry is a system where an AI model acts as the reasoning engine and can call tools — defined functions, Azure services, or external APIs — to take goal-directed actions. Unlike a simple generative AI that responds to a single prompt, an agent reasons through a task, selects which tool to use, executes the tool, observes the result, and iterates until the goal is achieved. The AI-901 exam tests creating and testing a single-agent solution in the Foundry portal and understanding the structure of an agent client application.

Does AI-901 require coding skills? 

Basic Python familiarity is helpful. The exam presents short Python code snippets for Foundry SDK client applications and expects you to identify what specific lines do — which line establishes the connection, which line sends the prompt, which line reads the response. The exam does not require writing code from scratch. Non-developers who study Foundry SDK examples through the Microsoft Learn AI-901 learning path gain sufficient code reading exposure.

Does the AI-901 certification expire? 

No. Like all Microsoft Fundamentals certifications, the Azure AI Fundamentals credential earned by passing AI-901 has lifetime validity. It does not expire and does not require a renewal assessment.

Is there a free demo available? 

Yes. Visit our free demo files page and free practice test library.

Get Your AI-901 Exam Dumps Now

CertEmpire’s AI-901 dumps are built directly from the official Microsoft skills blueprint updated April 15, 2026. Every question reflects the implementation-focused approach the AI-901 exam takes — Foundry portal workflows, Foundry SDK code reading, agent creation, Content Understanding extraction, and responsible AI scenario application — across the full 55–60% Foundry domain that most candidates underestimate.