AI-901 Study Plan: Concepts and Foundry Practice
Build an AI-901 study routine around current Foundry skills, short code reviews, targeted practice, and explanation-led revision.
Use IT Mastery for a mixed opening practice session, then build your schedule around the decisions you cannot yet explain. A missed implementation question and an unfamiliar term need different follow-up work.
Start with a useful baseline
Review the current blueprint and make two lists: concepts you need to explain and application steps you need to interpret. The implementation domain carries more than half of Microsoft’s weighting, so include short code and configuration reviews throughout your preparation.
For each uncertain answer, record the task, the fact you missed, and the competing choice you considered. Treat a correct guess as a review item. Avoid recording only an answer letter or product name.
Keep a short review log
Use one entry per missed decision. This example records what to look for next time, so the review remains useful when the wording or answer order changes.
| Record | Example entry |
|---|---|
| Task | Interpret an image supplied to an application. |
| Fact I missed | The prompt names a file, but the request contains no supported image input. |
| Why my choice failed | Increasing the output limit would not supply the missing image. |
| Next check | Trace image input in a Microsoft example, then try fresh visual-input questions in a later session. |
Keep a question number or link alongside the entry so you can revisit its explanation. Group entries by the underlying gap—such as prompt roles, conversation context, or result handling—to choose a focused practice session. Retire an entry when you can explain the distinction and apply it with different facts.
A flexible two-week plan
Use this as a sequence of study sessions. If the material is new, spread each block over more days; if you already build small Azure applications, spend more time on the gaps revealed by practice.
| Sessions | Review focus | Evidence that you can move on |
|---|---|---|
| 1–2 | Responsible AI, model behavior, and workload differences | Explain which risk or input/output requirement decides a scenario. |
| 3–4 | Model selection, prompts, deployments, and playground testing | Compare constraints and carry a tested prompt and supported settings into a small client. |
| 5–6 | Chat clients and single agents | Trace endpoint, authentication, request, and response handling; distinguish agent behavior from conversation history. |
| 7–8 | Text analysis, speech recognition, synthesis, and voice interaction | Select the required output and timing, then explain how the application receives it. |
| 9–10 | Image interpretation, image creation or editing, and video | Check source input, required preservation, and whether results arrive immediately or after job completion. |
| 11–12 | Content Understanding across documents, images, audio, and video | Match an analyzer and output schema to the requested information; validate fields, relationships, and missing values. |
| 13–14 | Mixed practice and targeted repair | Explain misses across both domains and apply the same concepts when the facts change. |
For a month-long schedule, use the first two weeks for these blocks and the remaining weeks for small hands-on exercises, targeted practice, and spaced revisits. Do not fill the extra time by repeatedly memorizing one static question set.
Use a repeatable daily session
- Recall: explain yesterday’s difficult distinction without notes.
- Inspect: read one short Microsoft example or a small practice exhibit. Identify input, configuration, operation, and output.
- Apply: complete a focused set, including questions with more than one required answer where available.
- Repair: write the requirement that rules out your preferred distractor, then revisit it in a later mixed set.
For the extraction block, compare four small tasks: invoice line items, fields from a product image, follow-up details from a recorded conversation, and events from a video. Explain the required output before choosing how to extract it.
Keep hands-on work small: test a prompt, inspect a response, follow an agent tool call, or map a few extracted fields. Microsoft’s AI-901 course provides the official learning starting point. Follow each exercise’s resource and cleanup instructions.
Use timed practice after broad coverage
Start mixed timed sessions once every major area has had a first pass. Review slow correct answers as well as errors. A useful review separates conceptual confusion, a missed constraint, and a mistake following the request or result flow.
The free practice page is an optional one-pass check. A higher score on an immediate repeat can reflect remembered answers. Readiness is better supported by varied attempts and the ability to explain decisions; a practice score does not guarantee an exam result.