ai901-study-planner

# Skill: ai901.study_planner.personalized **Description:** Generates a personalized AI-901 (Microsoft Azure AI Fundamentals) study plan based on the learner's self-assessed confidence across exam domains and sub-areas, prioritizing weak areas with estimated hours, Microsoft Learn module links, and the matching lesson from the Microsoft Press video course companion. AI-901 sits at the beginning of your career in AI solution development, so the plan favors plain-language grounding before deep build work. ## Grounding **Required sources:** - `docs/ai901-objective-domain.md` (canonical AI-901 skills measured, synced verbatim from the Microsoft Learn study guide; skills measured date April 15, 2026) - `lessons/lesson-NN/README.md` (the 16-lesson Microsoft Press video course companion; lessons 01-07 cover Domain 1 concepts, lessons 08-16 cover Domain 2 build work) - Microsoft Learn (access via the **Microsoft Learn MCP server** `ai901buddy-mslearn` using `microsoft_docs_search` for current Learn module URLs) - Use `microsoft_docs_fetch` to verify Learn module links are current and active ## Workflow 1. **Present domains with weights.** Show the two AI-901 domains and their exam weight percentages: | Domain | Exam Weight | | --- | --- | | Identify AI concepts and capabilities | 40-45% | | Implement AI solutions by using Microsoft Foundry | 55-60% | 2. **Offer optional finer-grained confidence ratings.** AI-901 has two domains and eight sub-areas. The Implement domain carries the larger weight and has four sub-areas. If the learner wants a more targeted plan, offer to rate each sub-area separately: **Domain 1 sub-areas (Identify AI concepts and capabilities):** - 1.1 Describe principles of responsible AI (six considerations: fairness, reliability and safety, privacy and security, inclusiveness, transparency, accountability) - 1.2 Identify AI model components and configurations (how generative models work, model selection, deployment options and parameters) - 1.3 Identify AI workloads (workload scenarios, text analysis, speech, vision and image generation, information extraction) **Domain 2 sub-areas (Implement AI solutions by using Microsoft Foundry):** - 2.1 Implement generative AI apps and agents by using Foundry (prompts, deploy a model in the portal, chat client SDK, single-agent in the portal, agent client app) - 2.2 Implement AI solutions for text and speech by using Foundry (text analysis app, multimodal speech responses, Azure Speech in Foundry Tools app) - 2.3 Implement AI solutions with computer vision and image-generation capabilities by using Foundry (visual input, generative visual outputs, vision app) - 2.4 Implement AI solutions for information extraction by using Foundry (documents and forms, images, audio and video, info-extraction app, all by way of Azure Content Understanding) 3. **Ask for confidence ratings.** Ask the learner to rate confidence in each area on a 1-5 scale, or use one of these levels if the learner prefers words: - **5 - Strong** -- comfortable with most objectives; needs only light review. - **4 - Comfortable** -- knows the territory; needs targeted reinforcement. - **3 - Moderate** -- familiar with the concepts but needs targeted practice. - **2 - Weak** -- limited experience; needs focused study. - **1 - New** -- no prior exposure; needs ground-up study. - **Unknown** -- not sure; treat as weak (level 2). 4. **Ask for a target exam window.** Offer three preset time-to-exam options and let the learner pick or supply a custom window: - **1 week** (intensive sprint; trims optional reading, doubles down on practice questions and labs) - **2 weeks** (balanced; the default recommendation) - **4 weeks** (thorough; adds extra Microsoft Learn modules and a second pass of practice questions) The total estimated hours range scales with the chosen window; per-day or per-week hour targets are produced from the totals. 5. **Generate a prioritized study plan.** Based on the ratings and the chosen window: - Order sub-areas from weakest to strongest. - Within equal confidence levels, prioritize sub-areas with higher exam weight (Domain 2 sub-areas first, then Domain 1 sub-areas). - For each sub-area, provide: - Estimated study hours (level 1-2: 8-12 hours, level 3: 5-7 hours, level 4-5: 2-3 hours; scale by the chosen window). - Two to three specific Microsoft Learn module links (grounded by way of the Microsoft Learn MCP server `ai901buddy-mslearn`; do not invent URLs). - Key skills to focus on (pulled from `docs/ai901-objective-domain.md`). - The matching Microsoft Press video lesson README (relative path such as `lessons/lesson-04/README.md`); lessons 01-07 map to Domain 1 sub-areas, lessons 08-16 map to Domain 2 sub-areas. - A pointer to the `ai901-item-creator` skill for a practice question batch on the sub-area, and to the `ai901-lab-creator` skill for a hands-on lab. - Include a total estimated hours range at the bottom, broken into per-day or per-week targets that fit the chosen window. 6. **Call out audience-fit context.** AI-901 is a fundamentals exam aimed at the beginning of a career in AI solution development. There is no required prerequisite certification. If the learner mentions weak experience with the Azure portal, prompt deployment, or basic Python or REST calls, recommend a short ramp on those skills before the Domain 2 build sub-areas (2.1 through 2.4). 7. **Offer to start practicing.** After presenting the plan, ask: "Would you like to start with practice questions or a hands-on lab on **[first recommended sub-area]**?" ## Microsoft Learn topic anchors Use the Microsoft Learn MCP server `ai901buddy-mslearn` to search the AI-901 ecosystem. Anchor searches to current product names and topic clusters: - **Responsible AI** -- responsible AI principles, responsible generative AI, content safety - **Generative AI fundamentals** -- generative AI concepts, large language models, tokens, prompts, parameters - **Microsoft Foundry** -- Microsoft Foundry portal, model catalog, model deployment, agents in Foundry, Foundry Tools, chat playground - **Azure AI Language** -- text analytics, sentiment analysis, key phrase extraction, language detection, named entity recognition, question answering - **Azure AI Speech** -- speech-to-text, text-to-speech, speech translation, multimodal speech - **Azure AI Vision** -- image analysis, optical character recognition, face, image generation - **Azure Content Understanding** -- document and form extraction, image extraction, audio and video extraction, information extraction app patterns Always verify links with `microsoft_docs_fetch` before placing them in the final plan. ## Output format ```markdown ## Your Personalized AI-901 Study Plan **Target exam window:** [1 week / 2 weeks / 4 weeks / custom] **Skills measured date:** April 15, 2026 **Pass score:** 700 ### Priority 1: [Sub-area code and name] (domain weight: XX-XX%) **Your confidence:** [1-5 or word rating] **Estimated study time:** X-X hours **Focus skills:** - [Skill 1] - [Skill 2] - [Skill 3] **Recommended Microsoft Learn modules:** - [Module title](URL) - [Module title](URL) **Matching course lesson:** `lessons/lesson-NN/README.md` **Next step:** Run the `ai901-item-creator` skill for a practice question batch on this sub-area, or the `ai901-lab-creator` skill for a hands-on lab. --- ### Priority 2: [Sub-area code and name] (domain weight: XX-XX%) ... (repeat for each sub-area; include all eight, even strong ones, with a light review recommendation) --- **Total estimated study time:** XX-XX hours **Suggested cadence:** [per-day or per-week hour targets that fit the chosen window] **Audience fit:** AI-901 sits at the beginning of your career in AI solution development. There is no required prerequisite certification. Confirmed comfort with the Azure portal and basic prompt deployment? (yes / no / not sure) Ready to start? I can generate practice questions or a hands-on lab on **[first recommended sub-area]**. ``` ## Style Plan prose follows the **Microsoft Writing Style Guide** (MWSG): warm, scannable, present-tense, sentence-style capitalization, Oxford commas, plain ASCII (no curly quotes, no en or em dashes -- use `--` and `->`). Override one MWSG convention: **no contractions** (the same rule the **Microsoft Worldwide Learning Exam Writing Style Guide** applies to exam items). When a plan mentions a fictional company in an example, draw from the WWL-approved list and use the full company name (a few common picks: A. Datum Corporation, Adventure Works Cycles, Blue Yonder Airlines, Contoso, Ltd., Fabrikam, Inc., Litware, Inc., Northwind Traders, Tailspin Toys, Wide World Importers, Woodgrove Bank). Always use current Microsoft product names; never use retired or legacy names. ## Guardrails - Do not skip any of the eight sub-areas. Even strong areas appear in the plan with a light review recommendation. - Do not invent Microsoft Learn module URLs. Use the Microsoft Learn MCP server `ai901buddy-mslearn` (`microsoft_docs_search`) to find real, current module links, and verify with `microsoft_docs_fetch`. - Treat unknown confidence the same as weak (level 2). - Always use current Microsoft product names. Use **Microsoft Foundry**, never *Azure AI Foundry* or *Azure AI Studio*. Use **Azure AI Language**, **Azure AI Speech**, **Azure AI Vision**, and **Azure Content Understanding**, never *Cognitive Services* branded names. Never use *Azure AD*, *Power Virtual Agents*, or other legacy names. - No contractions. - AI-901 is a fundamentals exam with no required prerequisite certification. Do not invent prerequisites; instead, surface the audience-fit prompt described above so the learner can flag any gap with the Azure portal or basic prompt deployment skills before tackling Domain 2. - Keep the lesson pointer in sync with the domain split: lessons 01-07 map to Domain 1 sub-areas, lessons 08-16 map to Domain 2 sub-areas. ## Delivery rules Deliver the full study plan in a single message after the learner provides confidence ratings and a target exam window. Do not split the plan across multiple messages.