Microsoft Azure AI Fundamentals (AI-901)
Microsoft Certified: Azure AI Fundamentals is Microsoft's entry-level AI credential. Its exam, AI-900, retired June 30, 2026; the certification is now earned via AI-901, a substantially more implementation-heavy exam requiring familiarity with Python and Azure resources. Two domains: Identify AI concepts and capabilities (40-45%) and Implement AI solutions by using Microsoft Foundry (55-60%, the majority of the exam). Passing score 700, $99 fee.
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Owner: Microsoft · Read: · Source: Exam AI-901: Microsoft Azure AI Fundamentals
Exam fee at last check: not confirmed (the owner's page could not be read for the price on 2026-09-26; we hold $99 from an earlier check), per the vendor. The official objectives are at learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ai-901. Verified 2026-09-26.
Practice is free. The full analysis adds the right answer, the explanation and the source for every question, readiness by domain and a study plan, for one payment.
- Identify AI Concepts and Capabilities40-45%
- Implement AI Solutions by Using Microsoft Foundry55-60%
Source: Vendor exam objectives. Checked 2026-09-26. Bar length is relative to the heaviest domain; the figure beside each name is the published weight.
What's on the exam
Identify AI Concepts and Capabilities 40-45%
Responsible AI principles, AI model components, and identifying AI workload types from a scenario.
- The six Microsoft Responsible AI principles: fairness, reliability and safety, privacy and security, inclusiveness, transparency, accountability
- How generative AI models work, and identifying an appropriate model based on capabilities
- AI workload types: generative and agentic AI, text analysis, speech, computer vision, information extraction
- Text analysis techniques: keyword extraction, entity detection, sentiment analysis, summarization
- ML task types: regression, classification, clustering, and core concepts like overfitting/underfitting
Primary sources: Microsoft Learn AI-901 study guide, Microsoft Responsible AI principles
Implement AI Solutions by Using Microsoft Foundry 55-60%
The majority of the exam: hands-on implementation using the Foundry portal and SDK, covering generative AI apps/agents, text/speech, vision, and information extraction.
- Creating effective system/user prompts, deploying a model, and building a lightweight chat client with the Foundry SDK
- Creating and testing a single-agent solution in the Foundry portal
- Text analysis and responding to spoken prompts using a deployed multimodal model, including Azure Speech in Foundry Tools
- Interpreting visual input and generating new visual output with a deployed multimodal or image-generation model
- Extracting information from documents, images, audio, and video using Azure Content Understanding in Foundry Tools
Primary sources: Microsoft Learn AI-901 study guide
Exam-day strategy
Implement AI Solutions by Using Microsoft Foundry is 55-60% of the exam, the majority. Do not treat this as a purely conceptual, no-coding exam the way the retired AI-900 was; you need working familiarity with the Foundry portal and SDK.
Read each conceptual question for the workload type (classification, regression, clustering, entity detection, generation, agentic). The right answer almost always maps to one specific workload.
For implementation questions, match the scenario to the specific Foundry capability (system prompts, agent creation, multimodal model calls, Content Understanding for document/image/audio/video extraction).
Memorize the six Microsoft Responsible AI principles. Distractors swap them.
Watch for Content Understanding vs a generic text-generation prompt as distractors on extraction questions; the exam tests whether you know the purpose-built tool.
A passing score is 700. Pace evenly; the exam mixes quick concept-recall questions with implementation questions that need a moment more thought.
How to prepare for Microsoft Azure AI Fundamentals (AI-901)
- EVIDENCE-BACKEDPractice in the same format as the exam. How well practice matches the final test shapes how much it helps. (Yang et al., 2021)
- EVIDENCE-BACKEDStart practice questions in week one, even on topics you have not studied. Failed attempts before seeing the answer still helped learning. (Kornell et al., 2009 and others)
- EVIDENCE-BACKEDSpace your reviews over weeks instead of cramming the week before. (Cepeda et al., 2006 and others)
- PLAUSIBLEDivide the minutes by the questions before you start, and keep a margin for a second pass if the exam allows one. (Sound arithmetic; no study compared time budgets on a licensing exam.)
Why this works
EVIDENCE-BACKED At least one peer-reviewed study or official source found the effect or states the fact. A study can be small.
PLAUSIBLE Reasonable and partly studied, but not tested on exams, or the results are mixed.
Practice that matches the final test format works better than practice that does not.
Failed attempts before seeing the answer still improved later learning of the material asked about; the benefit for material not asked about was virtually nil.
Spacing beat massing across hundreds of experiments and beat cramming with flashcards; combined with retrieval it raised scores on a real high-stakes exam.
Sound arithmetic; no study compared time budgets on a licensing exam.
Sources
- Yang, C., Luo, L., Vadillo, M. A., Yu, R., & Shanks, D. R. (2021). Testing (quizzing) boosts classroom learning: A systematic and meta-analytic review. Psychological Bulletin, 147(4), 399-435. doi.org/10.1037/bul0000309
- Kornell, N., Hays, M. J., & Bjork, R. A. (2009). Unsuccessful retrieval attempts enhance subsequent learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35(4), 989-998. doi.org/10.1037/a0015729
- Richland, L. E., Kornell, N., & Kao, L. S. (2009). The pretesting effect: Do unsuccessful retrieval attempts enhance learning? Journal of Experimental Psychology: Applied, 15(3), 243-257. doi.org/10.1037/a0016496
- St. Hilaire, K. J., Chan, J. C. K., & Ahn, D. (2024). Guessing as a learning intervention: A meta-analytic review of the prequestion effect. Psychonomic Bulletin & Review, 31(2), 411-441. doi.org/10.3758/s13423-023-02353-8
- Cepeda, N. J., Pashler, H., Vul, E., Wixted, J. T., & Rohrer, D. (2006). Distributed practice in verbal recall tasks: A review and quantitative synthesis. Psychological Bulletin, 132(3), 354-380. doi.org/10.1037/0033-2909.132.3.354
- Kornell, N. (2009). Optimising learning using flashcards: Spacing is more effective than cramming. Applied Cognitive Psychology, 23(9), 1297-1317. doi.org/10.1002/acp.1537
- Kang, S. H. K. (2016). Spaced repetition promotes efficient and effective learning: Policy implications for instruction. Policy Insights from the Behavioral and Brain Sciences, 3(1), 12-19. doi.org/10.1177/2372732215624708
- Janes, J. L., Dunlosky, J., Rawson, K. A., & Jasnow, A. (2020). Successive relearning improves performance on a high-stakes exam in a difficult biopsychology course. Applied Cognitive Psychology, 34(5), 1118-1132. doi.org/10.1002/acp.3699
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Editorial and assessment-design review: Dr. Julian Calvo, instructional and assessment design (Ed.D. in Learning Sciences, University of Miami). He reviews question sets for key defensibility, distractor quality and fit to the cited source, and logs corrections at /corrections. This is not a review by a licensed subject-matter professional.
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