Microsoft Azure AI Fundamentals
Microsoft's entry-level exam for building AI solutions on Azure. Practice what it tests: responsible AI principles, how generative models work and how to choose and deploy one, prompts and agents in Microsoft Foundry, and text, speech, vision and information-extraction workloads. Every question is original and maps to Microsoft's current skills outline.
What you'll study
Every section follows the official AI-901 exam outline. Click a domain to see its sections.
The responsible AI principles of fairness, inclusiveness, reliability, safety, privacy, security, transparency and accountability, then how generative models work, choosing and deploying a model and setting its parameters, and spotting common workloads in text analysis, speech, computer vision, image generation and information extraction.
Hands-on work in Microsoft Foundry: writing system and user prompts, deploying and testing models in the portal, chat and agent client apps, text analysis and Azure Speech apps, vision input and image generation, and pulling information from documents, forms, images, audio and video with Content Understanding.
Study, Quiz, Exam
Read the concepts, practice in short rounds, then test yourself on a mock exam with targeted feedback on where you need to focus more. Every question is original and maps to the official AI-901 outline.
Learn the concept first
Short study cards cover each section before you answer a single question.
Microsoft groups responsible AI into six principles, and many questions ask which one a problem touches. Match the problem to its focus: who gets worse decisions, who cannot use the system at all, what users are told, or who answers for it.
| Principle | Core question |
|---|---|
| Fairness | Do similar people get similar outcomes? |
| Inclusiveness | Can everyone who should benefit actually use it? |
| Reliability and safety | Does it behave consistently and safely, even in odd conditions? |
Ten-question rounds
Every answer explained. Score 80% and the next section unlocks.
Which responsible AI principle is MOST directly at risk in this screening model?
Fairness means similar people should get similar outcomes, and a model that rejects similarly qualified applicants more often because of their group is unfair. Inclusiveness is not the issue here because every applicant can use the system and get a decision. Privacy and security concerns protecting personal data, and accountability concerns who answers for the system and how it is governed, not the bias in its decisions.
Know exactly where to focus
Timed practice exams any time, with a study plan built from your mistakes.
What to expect on the AI-901
Made for anyone starting to build AI solutions on Azure, even if AI is new to you. A little Python and some familiarity with Azure help, but you don't need to be an expert.
Source: Microsoft Learn: Exam AI-901: Microsoft Azure AI Fundamentals, checked October 16, 2026
Where this cert takes you
Real job titles where AI-901 skills are part of the day-to-day work.
- Junior Systems Analyst
- IT Business Analyst
- Technical Support Analyst
- Junior AI Developer
- Cloud Support Associate
- Automation Analyst
- Systems Analyst
- AI Solutions Analyst
- Business Systems Analyst
- Azure Developer
- Integration Analyst
- Solutions Consultant
- Senior Systems Analyst
- AI Solutions Architect
- Lead Business Systems Analyst
- AI Product Owner
- Senior Solutions Consultant
- Technical Program Manager
Ranges based on BLS Occupational Employment & Wage Statistics (May 2025) for Computer Systems Analysts: 10th percentile $67,340, median $105,850, 90th percentile $167,710. Adjusted by metro. AI-901 tests choosing and applying AI services for business needs, which is the work of computer systems analysts; specialist AI engineer and data scientist roles are counted under other occupations. The first role in each column is the most common entry point.
Recommended ProCourse
A good course to pair with your cert study for learning hard tech skills while you study for your cert
AI Engineering & Automation
Go beyond prompting. Call APIs, build tools that classify and generate, wire up RAG pipelines, create autonomous agents with memory and tool use, then train and deploy your own models. AI engineering, not AI tourism.
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