Final Week Review Plan for AI-901
Key Takeaways
- Begin the final week by confirming the current Microsoft Learn exam page, beta status, study guide date, sandbox, and any available practice assessment updates.
- Use the official two-domain weighting to allocate time: responsible AI and AI concepts matter, but Foundry implementation scenarios are the larger share.
- Build a one-page service picker for generative AI, agents, Language, Speech, Vision, Content Understanding, Search, and Content Safety.
- Practice with scenario prompts by identifying input, required output, constraints, service, model type, and responsible AI control before looking at answer choices.
- Keep the final 24 hours light: review misses, use the sandbox, verify logistics, and avoid adding unfamiliar services at the last minute.
A Seven-Day AI-901 Review Plan
The final week is for convergence, not collecting every possible Azure AI detail. AI-901 rewards the learner who can read a short scenario, identify the workload, choose a Foundry model or service, and add the right responsible AI control. Use Microsoft Learn as the source of truth, especially because AI-901 is new and listed as beta as of June 6, 2026. Confirm the current exam page before scheduling or making claims about practice assessment availability, price, language rollout, or timing.
| Day | Main target | Concrete output |
|---|---|---|
| 7 days out | Blueprint reset | Write the two official objective groups and weights from the current Microsoft Learn page |
| 6 days out | Responsible AI | Create six cards: fairness, reliability and safety, privacy and security, inclusiveness, transparency, accountability |
| 5 days out | Foundry models | Compare catalog, deployment, endpoint, playground, prompt, model parameter, and lightweight client call |
| 4 days out | Foundry apps and agents | Trace a single-agent example and label instructions, tool use, boundary, review, and logging |
| 3 days out | Foundry Tools | Drill Language, Speech, Vision, image generation, Content Understanding, Search, and Content Safety scenarios |
| 2 days out | Mixed scenarios | Do timed original practice and explain why each wrong option is wrong |
| 1 day out | Light review | Revisit misses, open the exam sandbox, verify account and appointment details, and stop adding new topics |
On day 7, make sure you are studying AI-901, not only older AI-900 material. The current objectives emphasize identifying AI concepts and responsibilities, then implementing AI solutions with Microsoft Foundry. Older responsible AI, machine learning, computer vision, NLP, and generative AI ideas still help, but the final review should use Foundry language: resources, projects, model catalog, deployments, endpoints, tools, agents, and lightweight client apps.
On day 6, turn the responsible AI principles into action verbs. Fairness means compare quality across groups and reduce unequal outcomes. Reliability and safety means test expected behavior, plan fallback paths, and control harmful failure modes. Privacy and security means limit data exposure, control access, avoid hardcoded secrets, and protect personal data. Inclusiveness means design for varied abilities, languages, and contexts. Transparency means disclose AI use, limits, confidence, or evidence. Accountability means people own the system, the review process, and the consequences.
On day 5, reduce model selection to a fast checklist. Ask whether the scenario needs chat, embeddings, multimodal interpretation, image generation, lower latency, lower cost, a specific region, or a particular deployment option. Remember that a model must be deployed before an app can call it. Prompt design can improve instructions and context, but it does not replace retrieval for private facts. If the scenario asks for grounded answers from enterprise content, reach for Azure AI Search and RAG.
On day 4, focus on agents without overcomplicating them. A single-agent solution in Foundry has instructions and can use approved tools to complete a task. The review questions to ask are simple: what is the goal, what tool may the agent use, what data can it access, what should it refuse, what should require user confirmation, and how will behavior be traced or evaluated? If the answer choice gives the agent unlimited authority over sensitive actions, be suspicious.
On day 3, build a service picker and say it out loud. Text analytics such as NER, key phrases, sentiment, summarization, PII, and language detection point to Azure AI Language. Spoken input or output points to Azure AI Speech unless the scenario specifically says a multimodal model handles the spoken prompt. Existing image analysis points to Vision or a multimodal model; new visual output points to image generation. Structured extraction from documents, images, audio, or video points to Content Understanding. Grounded search over enterprise content points to Azure AI Search.
Harmful content, prompt attacks, or moderation point to Content Safety and model content filters.
On day 2, practice a consistent answer routine. First identify the input type: text, audio, image, document, video, or enterprise data. Second identify the required output: label, extracted field, generated answer, translation, audio, image, or ranked result. Third identify constraints: cost, latency, privacy, confidence, human review, citation, or safety. Fourth choose the simplest service or model type that satisfies those facts. Finally, name a responsible AI control. This routine prevents attractive but irrelevant answers from pulling you away from the scenario.
On day 1, stop trying to learn every product update. Review your wrong-answer log, especially traps involving RAG versus fine-tuning, catalog versus deployment, OCR versus extraction, chat versus agent, and fairness versus accuracy. Visit the exam sandbox so navigation, review flags, case-study style screens, and controls are familiar. Verify the appointment, account, identification requirements, and accommodation status through official channels. During the exam, do not fight a hard question early. Mark it, answer the easier service-selection items, and return with a calmer view of the wording.
During the final week, a learner keeps missing questions that ask whether to use fine-tuning, RAG, embeddings, or a chat model. What is the best next review activity?
A candidate sees a scenario asking for structured extraction from support-call audio and uploaded documents, with low-confidence results routed to human review. Which final-week service picker row should they use?
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