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Microsoft Azure AI Fundamentals (AI-901) Fact Sheet

Exam Overview

Exam: AI-901, Microsoft Azure AI Fundamentals Certification earned: Microsoft Certified: Azure AI Fundamentals (AI-901 is the only exam required) Level: Fundamentals Status: Live. English version updated 2026-04-15; skills measured as of 2026-04-15 Replaces: AI-900, retired 2026-06-30 Passing score: 700 Cost: Based on the country or region where the exam is proctored (shown at scheduling) Duration: 45 minutes of exam time, 65 minutes of seat time (Microsoft's figures for all Fundamentals exams; the AI-901 page itself gives none, checked 2026-10-22) Number of questions: Not published. Microsoft says most of its exams have 40-60 questions and the count varies by exam Languages: 13 Renewal: None needed; Microsoft states that Fundamentals certifications do not expire Practice assessment: Free, on Microsoft's AI Skills Navigator

Verify before booking. Microsoft revises study guides regularly. Read the "skills measured" change log on the study guide page before you start.

📖 Exam AI-901 📖 Azure AI Fundamentals certification 📖 Exam duration and exam experience - Fundamentals exams: 45 minutes exam time, 65 minutes seat time; "most" exams have 40-60 questions 📖 AI-901 study guide 📖 AI-900 retirement notice - "retired on June 30, 2026, and has been replaced by AI-901"

Who it is for

Microsoft describes the candidate as someone at the start of a career building AI solutions, with conceptual knowledge of AI in Azure, knowledge of Python syntax and programming techniques, and familiarity with Azure resources. That last part is the real change from AI-900, which was pitched equally at non-technical candidates. You do not need to be a developer, but you should be comfortable reading a 20-line Python script and knowing what an endpoint, a key and a deployment are.

Skills measured (as of 2026-04-15)

1. Identify AI concepts and capabilities (40-45%)

Describe principles of responsible AI - Considerations for fairness - Considerations for reliability and safety - Considerations for privacy and security - Considerations for inclusiveness - Considerations for transparency - Considerations for accountability

Identify AI model components and configurations - Describe how generative AI models work - Identify an appropriate AI model, based on capabilities - Identify appropriate model deployment options and configuration parameters

Identify AI workloads - Scenarios for common AI workloads, including generative and agentic AI, text analysis, speech, computer vision, and information extraction - Common text analysis techniques: keyword extraction, entity detection, sentiment analysis, summarization - Features and capabilities of speech recognition and speech synthesis - Features and capabilities of computer vision and image-generation models - Techniques to extract information from text, images, audio and videos

2. Implement AI solutions by using Microsoft Foundry (55-60%)

Implement generative AI apps and agents by using Foundry - Create effective system and user prompts for generative AI models - Deploy a model and interact with it in the Foundry portal - Create a lightweight chat client application by using the Foundry SDK - Create and test a single-agent solution in the Foundry portal - Create a lightweight client application for an agent

Implement AI solutions for text and speech by using Foundry - Build a lightweight application that includes text analysis - Respond to spoken prompts by using a deployed multimodal model - Build a lightweight application by using Azure Speech in Foundry Tools

Implement AI solutions with computer vision and image-generation capabilities by using Foundry - Interpret visual input in prompts by using a deployed multimodal model - Create new visual outputs by using generative models - Build a lightweight application that includes vision capabilities

Implement AI solutions for information extraction by using Foundry - Extract information from documents and forms by using Azure Content Understanding in Foundry Tools - Extract information from images by using Content Understanding - Extract information from audio and video by using Content Understanding - Build a lightweight application with information extraction capabilities by using Content Understanding

Names you must know (as of 2026-10-21)

Current name Formerly What it is
Microsoft Foundry Azure AI Studio, then Azure AI Foundry The platform: projects, model catalog, deployments, playgrounds, agents, evaluation
Foundry Tools Azure AI Services (Cognitive Services) The prebuilt capabilities: Azure Speech, Azure Language, Azure Vision, Content Understanding and others
Azure OpenAI in Foundry Azure OpenAI Service OpenAI models sold directly by Azure
Foundry Agent Service Azure AI Agent Service Building and running agents
Azure Content Understanding (new; overlaps Document Intelligence) Extracts structured fields from documents, images, audio and video
Foundry IQ - Managed knowledge layer, built on Azure AI Search
Responses API Assistants API The API agents and chat clients use

Source: What is Microsoft Foundry? ("Evolution of Foundry" table).

Official training

How it compares

AI-901 AWS AI Practitioner (AIF-C01) Google Generative AI Leader
Hands-on content Yes: Foundry portal and short Python No No
Classic ML Light Moderate Light
Generative AI and agents Heavy Heavy Heavy
Business strategy Light Moderate Heavy

Exam-day tips

  • Read the last sentence of each question first: is it asking for a workload, a principle, a service or a step?
  • For "which service" questions, the current answer is usually a Foundry capability; distractors use retired names (LUIS, QnA Maker, Form Recognizer) or the wrong workload.
  • For code questions, find the endpoint, the credential, the deployment or model name, and the call. Those four lines are what gets tested.