Full exam simulation
45 questions · 45 minutes, 0 unscored and not identified. One question per screen, flag and strike-out, a review screen and a score report per domain — no answers until you finish.
Responsible AI, models and AI workloads, and building generative AI, agent, speech, vision and extraction apps with Microsoft Foundry.
Independent practice — not affiliated with or endorsed by Microsoft. AI-901 is used only to identify the exam. Trademark notices
AI-901 replaced AI-900, which retired on June 30, 2026; AI-901 has no announced retirement date.
Share of scored content per domain. Every sitting draws questions by these weights.
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45 questions · 45 minutes, 0 unscored and not identified. One question per screen, flag and strike-out, a review screen and a score report per domain — no answers until you finish.
The full sitting with 30 extra minutes (75 min) — the accommodation non-native English speakers can request for exams in English.
20 questions · 20 min, same weights, all scored. A daily check.
20 questions aimed at your weakest domains, missed concepts and due reviews.
Pick domains, topics, how many questions and which pool (new, wrong, due, low confidence). Spaced repetition with confidence ratings, flashcards and a mistake book.
Open practiceOne domain, up to 20 questions.
Responsible AI principles, how generative models work, choosing models and deployment parameters, and AI workloads: text analysis, speech, computer vision, image generation and information extraction. Official range 40–45 %.
Practice domain 1 →Prompts, model deployment and chat clients with the Foundry SDK, single-agent solutions, text and speech apps, multimodal vision and image generation, and Azure Content Understanding for documents, images, audio and video. Official range 55–60 %.
Practice domain 2 →Domain names and weights come from the official AI-901 exam objectives (checked 2026-10-26). Always check it for the authoritative scope.
The real exam also has drag-and-drop and hot-area questions; the simulator uses multiple-choice only.
Two of this exam's original questions, with the explanation you get after every answer.
A bank's loan-approval model approves applicants from one postal area far less often than equally qualified applicants elsewhere. Which responsible AI principle is MOST at risk?
Correct: C. Fairness means AI systems should treat all people equitably and not allocate opportunities differently to similar groups. Unequal approval rates for equally qualified applicants indicate a fairness problem.
Which practices support the accountability principle? (Select TWO.)
Correct: C, E. Accountability is supported by governance frameworks, clear ownership and human oversight, especially where decisions significantly affect people.