AI-901 Exam Study Plan

The AI-901 Microsoft Azure AI Fundamentals (beta) training course is a structured preparation path for candidates who need to connect Azure AI concepts with practical Microsoft Foundry implementation. It is designed for early-career AI solution developers, junior developers, technical students, support engineers, and IT professionals who need scenario-based readiness rather than vocabulary-only review.

This 6-week first-attempt preparation plan covers the current AI-901 skill boundary updated by Microsoft on April 15, 2026. It organizes the two assessed domains into daily learning actions, practical outputs, Pomodoro study blocks, forgetting-curve review checkpoints, and evidence-based scenario practice without promising a guaranteed result.

What This Plan Offers

• Comprehensive coverage of the two AI-901 exam domains:

• Identify AI concepts and responsibilities (3 operational focus areas)

• Implement AI solutions by using Microsoft Foundry (4 operational focus areas)

• Daily goals and tasks that convert each operational focus area into reading, mapping, recall, comparison, and scenario drills.

• Pomodoro Technique structure: 25 minutes of focused study, 5 minutes of reset, and a short active-recall output after each session.

• Forgetting Curve review checkpoints: same-day recall, next-day review, 3-day review, weekly consolidation, and final cumulative review.

• Practice-oriented outputs: domain maps, capability comparison sheets, responsible AI control cards, Foundry workflow diagrams, lightweight Python-reading notes, mock scenarios, troubleshooting notes, and mistake logs.

Who This Plan Is For

This plan is for candidates who are beginning AI solution development on Azure and need a practical route through AI concepts, Microsoft Foundry model deployment, Foundry Tools, single-agent behavior, multimodal workloads, and Content Understanding scenarios. It also fits experienced IT or support professionals who want a structured training course to translate existing Azure knowledge into AI-901 answer-selection skill.

Final Outcome

After completing the plan, learners should be able to classify AI workloads, explain responsible AI controls, choose model capabilities and deployment parameters, interpret Foundry implementation clues, distinguish text, speech, vision, image generation, and information extraction workflows, and validate answers through observable evidence such as portal state, SDK response shape, tool output, confidence signals, or governance review artifacts.

Week 1 Study Plan - Responsible AI, Model Capability, and Workload Classification

Primary Objective for the Week:

Build the foundation for the first AI-901 domain by learning how responsible AI requirements, model capability selection, deployment parameters, and workload categories appear in scenario questions.

Learning Methodology for the Week:

Study 60 to 90 minutes per day. Use two 25-minute Pomodoro blocks for focused reading and mapping, then one 15-minute active-recall block. Review each day's notes later the same day, again the next day, and during Day 7 consolidation.

Day 1 - Responsible AI Scenario Controls

Goal: Understand how fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability act as decision controls in Azure AI scenarios.

Tasks: Study Apply Responsible AI Requirements to an Azure AI Scenario. Create a six-principle control card that maps each principle to one scenario clue, one required evidence type, and one distractor pattern.

Learning Method: Use Pomodoro Block 1 for concept grounding, Block 2 for evidence mapping, and the recall block to answer from memory: "Which principle owns the risk, and what evidence proves the control?"

Verification: You can explain why a responsible AI answer must address the scenario risk before choosing a tool, model, or tuning option.

Day 2 - Model Capabilities and Deployment Parameters

Goal: Match model choice, input modality, output shape, deployment option, and configuration parameter to the scenario requirement.

Tasks: Study Select AI Model Capabilities and Deployment Parameters. Build a comparison sheet for model capability, deployment name, endpoint access, token or generation parameters, and expected response shape.

Learning Method: Use one Pomodoro for reading, one for comparison-sheet construction, and one short recall pass that hides the answers and reconstructs the capability decision path.

Verification: You can reject distractors that tune prompts or parameters before the correct model capability and deployment boundary are established.

Day 3 - AI Workload Pattern Identification

Goal: Recognize generative AI, agent, text analytics, speech, computer vision, image generation, multimodal prompting, and information extraction scenarios.

Tasks: Study Identify Azure AI Workload Patterns. Draw a workload routing chart from input type to output requirement to likely Azure AI or Foundry capability.

Learning Method: Use a diagram-first Pomodoro, then a flashcard Pomodoro covering one input, one output, and one wrong adjacent workload per card.

Verification: You can classify a scenario from its required output even when the option list contains several plausible Azure AI services.

Day 4 - Mixed Domain 1 Scenario Drill

Goal: Convert the first-domain topics into exam-style answer selection.

Tasks: Write 12 short scenarios: four responsible AI, four model/deployment, and four workload classification. For each scenario, identify the controlling clue, the first dependency, the best answer, and the tempting distractor.

Learning Method: Use 25 minutes for scenario writing, 25 minutes for answer review, and 15 minutes for an error-log update.

Verification: Every answer explanation names why the correct option resolves the stated constraint and why the distractor solves a different problem.

Day 5 - Python and Client Clue Reading

Goal: Read lightweight AI client examples for meaning without overfitting to exact code syntax.

Tasks: Extract client-facing clues from the first-domain topics: endpoint, deployment name, input payload, modality, output object, and evidence returned by a response.

Learning Method: Use one Pomodoro to annotate examples and one Pomodoro to rewrite each clue as a plain-English dependency.

Verification: You can distinguish a conceptual client clue from an official command or product-plane boundary.

Day 6 - Forgetting-Curve Review and Comparison Tables

Goal: Strengthen retention across all first-domain focus areas.

Tasks: Rebuild the responsible AI control card, model capability sheet, and workload routing chart without looking. Add at least five missed or slow items to the mistake log.

Learning Method: Use active recall first, source checking second, and spaced repetition at the end of the day.

Verification: You can recover the correct map from memory and identify the specific clue that triggered each topic.

Day 7 - Weekly Consolidation and Mock Set

Goal: Validate Week 1 readiness through mixed practice.

Tasks: Answer 20 mixed questions or self-written scenarios. Review misses by category: governance evidence gap, modality confusion, output mismatch, deployment-boundary error, or workload misclassification.

Learning Method: Use timed Pomodoro rounds for practice and a final review block for mistake-pattern analysis.

Verification: You can explain all missed questions in two sentences: the controlling clue and the dependency that made the correct option better.

Week 2 Study Plan - Microsoft Foundry Chat, Model Deployment, and Single-Agent Solutions

Primary Objective for the Week:

Learn how Microsoft Foundry implementation scenarios move from deployed model capability to chat testing, lightweight client use, and single-agent behavior.

Learning Methodology for the Week:

Study 75 to 90 minutes per day. Pair each concept block with an observable-output task: a workflow diagram, a client clue table, a portal evidence checklist, or an agent-boundary note. Review Week 1 material briefly on Days 2, 4, and 7.

Day 1 - Foundry Project and Deployment Boundary

Goal: Understand why a scenario must establish the project, model deployment, endpoint, and deployment name before client calls can work.

Tasks: Study Build and Test a Foundry Chat and Single-Agent Solution. Diagram the control path from Foundry project to model deployment to chat test to client invocation.

Learning Method: Use one Pomodoro for the Foundry object chain and one for a dependency diagram.

Verification: You can describe what evidence proves that the model is callable before troubleshooting prompt quality.

Day 2 - Chat Testing and Response Evidence

Goal: Interpret chat test behavior as evidence of model capability, deployment routing, and prompt-response contract.

Tasks: Create a table of scenario clues: no response, wrong model behavior, missing deployment identifier, weak prompt instruction, and expected output mismatch.

Learning Method: Use active recall after each table row by covering the "first evidence to inspect" column.

Verification: You can choose the option that checks deployment or response evidence before choosing a downstream tuning action.

Day 3 - Single-Agent Instruction and Tool Boundary

Goal: Understand single-agent behavior at a fundamentals level: instructions, available tools, grounding constraints, and expected task output.

Tasks: Build a one-page agent boundary note that separates model deployment, agent instruction, tool availability, and client application responsibility.

Learning Method: Use one Pomodoro for concept reading and one for a "wrong option clinic" where each distractor changes the wrong boundary.

Verification: You can identify when a scenario asks for an agent configuration choice rather than a general model or workload category.

Day 4 - Lightweight Client Application Reading

Goal: Read Python-oriented or SDK-style examples for the object being called, the input sent, and the evidence returned.

Tasks: Annotate a conceptual client flow: credential or access boundary, endpoint, deployment or agent identifier, request payload, response object, and error clue.

Learning Method: Use two Pomodoro blocks to translate code-shaped details into scenario clues, then one recall block to reconstruct the flow.

Verification: You can state what a 401/403-style access clue, wrong endpoint clue, or malformed response clue would mean without inventing unsupported commands.

Day 5 - Foundry Scenario Practice

Goal: Apply Foundry chat, deployment, agent, and client clues to answer selection.

Tasks: Write eight scenario cards that ask for the first action, best capability, validation evidence, or likely cause of a failure.

Learning Method: Use a mixed practice Pomodoro and immediately update the error log after each miss.

Verification: Each card has one best answer and three plausible distractors tied to adjacent Foundry decisions.

Day 6 - Cross-Domain Review

Goal: Connect Foundry implementation with responsible AI and workload classification.

Tasks: For each Foundry scenario, add a responsible AI or workload classification note: what risk, modality, or output contract must be considered before implementation?

Learning Method: Use interleaved recall: one Week 2 item, one Week 1 item, then one combined scenario.

Verification: You can avoid treating every AI-901 question as a deployment question when the stem is actually testing governance or workload identification.

Day 7 - Weekly Consolidation and Troubleshooting Tree

Goal: Consolidate Foundry implementation logic into a reusable troubleshooting tree.

Tasks: Build a tree with first checks: scenario objective, model capability, deployment evidence, agent/tool boundary, client call, response evidence, and responsible AI constraint.

Learning Method: Use active recall to redraw the tree, then compare against notes and revise.

Verification: You can explain why the first check changes depending on whether the symptom is selection, deployment, agent behavior, or client consumption.

Week 3 Study Plan - Text, Speech, Vision, Image Generation, and Multimodal Workflows

Primary Objective for the Week:

Separate Foundry Tool scenarios by input modality, output shape, and validation evidence so text, speech, vision, and image generation questions do not blur together.

Learning Methodology for the Week:

Use daily 75-minute sessions: one Pomodoro for the specific tool family, one for comparison with adjacent tools, and one recall block for scenario elimination. Revisit Week 1 and Week 2 cards on Days 3 and 6.

Day 1 - Text Analysis Workflows

Goal: Recognize key phrase extraction, entity detection, sentiment analysis, summarization, and text-to-output workflows.

Tasks: Study Implement Text and Speech Solutions with Foundry Tools, focusing first on text analysis. Create a table that maps each text requirement to output evidence.

Learning Method: Use a comparison-sheet Pomodoro and a recall block that converts each row into a scenario stem.

Verification: You can choose a text tool when the output is linguistic structure or meaning rather than audio, image, or extracted document fields.

Day 2 - Speech Recognition and Synthesis

Goal: Distinguish speech-to-text, text-to-speech, spoken interaction, voice/language configuration, and transcript evidence.

Tasks: Extend the table with speech scenarios, including input audio, output transcript, synthesized audio, language or voice settings, and validation artifacts.

Learning Method: Use one Pomodoro for speech directionality and one for distractor analysis against text-only workflows.

Verification: You can reject options that analyze text when the first dependency is converting spoken input or producing spoken output.

Day 3 - Vision Understanding

Goal: Interpret image input, visual prompt context, computer vision features, and response evidence from a multimodal model.

Tasks: Study Implement Vision and Image Generation Solutions with Foundry. Build a vision workflow map from image input to visual interpretation to response evidence.

Learning Method: Use diagramming first, then active recall from image scenario clues.

Verification: You can separate existing-image interpretation from generating a new image artifact.

Day 4 - Image Generation

Goal: Understand prompt-to-image workflows, generation parameters at a fundamentals level, output artifact validation, and safety constraints.

Tasks: Add image generation to the comparison sheet with prompt input, generated image output, style or size constraints, and validation evidence.

Learning Method: Use one Pomodoro to study the capability and one to write distractors that confuse vision analysis with image generation.

Verification: You can choose image generation only when the required output is new visual content.

Day 5 - Lightweight Applications with Vision or Speech

Goal: Connect Foundry Tool capabilities to simple application behavior.

Tasks: Annotate conceptual app flows for text, speech, and vision: user input, capability call, response handling, and evidence of success.

Learning Method: Use mixed modality flashcards and a next-day review of Week 3 Days 1-4.

Verification: You can identify the application layer clue without replacing it with infrastructure or governance-only answers.

Day 6 - Modality Elimination Drill

Goal: Build speed in eliminating wrong modalities and wrong output contracts.

Tasks: Answer or write 16 mixed modality scenarios. Label each miss as input confusion, output confusion, later-step tuning, or wrong Foundry boundary.

Learning Method: Use timed 25-minute practice rounds followed by error-log updates.

Verification: You can classify a scenario in under a minute and explain the best evidence to check.

Day 7 - Weekly Consolidation and Visual Map Review

Goal: Consolidate multimodal workflows into a single map.

Tasks: Redraw the map for text, speech, vision, image generation, chat, and single-agent workflows. Add one validation evidence item and one distractor for each path.

Learning Method: Use active recall first, source checking second, and spaced repetition of all Week 1-3 notes.

Verification: You can explain how each workflow changes when input modality or required output changes.

Week 4 Study Plan - Azure Content Understanding and Information Extraction

Primary Objective for the Week:

Master information extraction scenarios that use Azure Content Understanding in Foundry Tools for documents, forms, images, audio, and video.

Learning Methodology for the Week:

Study 75 to 90 minutes daily. Use one Pomodoro for extraction concepts, one for analyzer and output evidence, and one for scenario practice. Review multimodal comparison cards every other day.

Day 1 - Content Understanding Purpose and Analyzer Output

Goal: Understand why Content Understanding is used when the scenario needs repeatable structured fields from unstructured or semi-structured content.

Tasks: Study Extract Information with Azure Content Understanding in Foundry Tools. Create an analyzer-output note covering input content, field schema, extracted value, confidence, and review evidence.

Learning Method: Use one Pomodoro for concept grounding and one for table construction.

Verification: You can identify structured extraction as different from open-ended summarization or general chat.

Day 2 - Document and Form Extraction

Goal: Recognize document and form scenarios that require field extraction, layout-aware content, and repeatable outputs.

Tasks: Build sample scenario cards for invoices, forms, contracts, receipts, or records. For each card, define required fields and evidence of extraction success.

Learning Method: Use a scenario-writing Pomodoro and a recall block that hides the field schema.

Verification: You can choose Content Understanding when the answer depends on reliable field output, not a generic text response.

Day 3 - Image Extraction

Goal: Separate image understanding from image-based information extraction.

Tasks: Compare a vision question that asks "what is in this image" with an extraction question that asks for specific fields or facts from an image.

Learning Method: Use a two-column comparison sheet and next-day review of Day 2 cards.

Verification: You can explain why structured output requirements change the best capability.

Day 4 - Audio and Video Extraction

Goal: Understand how audio or video inputs can require extracted information, not only transcription or visual interpretation.

Tasks: Create a workflow note for audio/video: media input, analyzer processing, extracted field or event, confidence signal, and application consumption.

Learning Method: Use a diagram Pomodoro and a distractor-analysis Pomodoro against speech transcription or vision-only answers.

Verification: You can identify when speech or vision is an intermediate capability but Content Understanding owns the extraction outcome.

Day 5 - Lightweight Extraction Application

Goal: Connect Content Understanding output to simple application behavior.

Tasks: Annotate a conceptual app flow: upload content, call analyzer, receive structured fields, validate confidence, handle missing fields, and display or store results.

Learning Method: Use active recall after each flow step by naming the dependency it satisfies.

Verification: You can choose validation actions that inspect analyzer status, fields, confidence, and output shape before changing unrelated services.

Day 6 - Extraction Scenario Drill

Goal: Practice selecting Content Understanding under realistic constraints.

Tasks: Answer or write 14 scenarios across documents, images, audio, and video. Include distractors for chat summarization, speech transcription, computer vision description, and manual field mapping.

Learning Method: Use timed practice and update the error log by distractor category.

Verification: Each answer explanation identifies the structured field requirement and the evidence that proves extraction.

Day 7 - Weekly Consolidation and Cross-Modal Review

Goal: Consolidate all information extraction patterns and connect them to prior weeks.

Tasks: Rebuild the full comparison sheet for Foundry chat, single-agent, text, speech, vision, image generation, and Content Understanding. Add one scenario where the wrong answer is tempting for each adjacent workflow.

Learning Method: Use one active-recall Pomodoro, one source-check Pomodoro, and one mistake-log review block.

Verification: You can explain why Content Understanding is the best option when the scenario asks for repeatable structured extraction.

Week 5 Study Plan - Integrated Scenario Practice and Exam Strategy

Primary Objective for the Week:

Turn the individual domains into integrated exam behavior: read the scenario, identify intent, locate the controlling constraint, eliminate adjacent tools, and choose the first valid action.

Learning Methodology for the Week:

Use 90-minute sessions with alternating practice and review. Start each day with 10 minutes of spaced recall from previous weeks, then complete two Pomodoro practice rounds and a final error-log analysis.

Day 1 - Keyword Extraction and Intent Classification

Goal: Read scenario wording for intent, constraint, modality, output contract, and evidence requirement.

Tasks: Build a keyword bank for responsible AI, model deployment, Foundry chat, agent workflow, text, speech, vision, image generation, and Content Understanding.

Learning Method: Use active recall to write keywords first, then verify and refine.

Verification: You can map each keyword to a domain, focus area, and likely distractor.

Day 2 - Four-Step Elimination Practice

Goal: Apply a stable elimination routine to mixed questions.

Tasks: For 20 mixed scenarios, eliminate options by wrong input modality, wrong output shape, premature later-step tuning, and missing evidence.

Learning Method: Use timed Pomodoro sets and write one sentence for each eliminated option.

Verification: You can show why rejected options are useful in another scenario but not in the current one.

Day 3 - Responsible AI in Implementation Scenarios

Goal: Keep governance and responsible AI controls visible inside implementation questions.

Tasks: Rewrite five Foundry or Content Understanding scenarios to include fairness, privacy, transparency, or accountability constraints.

Learning Method: Use scenario transformation and same-day review.

Verification: You can choose an answer that satisfies both the technical workflow and the responsible AI control.

Day 4 - Foundry Boundary Review

Goal: Avoid confusing Microsoft Foundry deployment, Foundry Tools, Azure Content Understanding, and general Azure AI workload labels.

Tasks: Produce a boundary table with object, scenario clue, first evidence, common distractor, and review action.

Learning Method: Use one table-building Pomodoro and one oral recall Pomodoro.

Verification: You can explain each boundary without using internal document language or unsupported command claims.

Day 5 - Mock Set and Error Log Rewrite

Goal: Identify persistent weak areas.

Tasks: Complete a mixed mock set. Rewrite every missed question as a new scenario with the correct dependency made explicit.

Learning Method: Use timed practice followed by slow review.

Verification: The error log shows a corrected rule for every miss, not just the right letter.

Day 6 - Practical Evidence Rehearsal

Goal: Connect answer choices to evidence categories.

Tasks: For each topic, name the evidence type: portal state, deployment details, endpoint response, transcript, generated artifact, extracted field, confidence score, analyzer status, or governance review artifact.

Learning Method: Use flashcards and next-day spaced repetition.

Verification: You can explain what evidence would confirm the correct option in a lab or portal setting.

Day 7 - Weekly Consolidation and Readiness Check

Goal: Decide what to review in the final week.

Tasks: Score the mistake log by category, select the top three weak areas, and create a final-week daily review order.

Learning Method: Use cumulative recall before looking at notes, then confirm and correct.

Verification: You have a measurable final-week plan tied to weak areas and high-frequency scenario types.

Week 6 Study Plan - Final Review, Mock Practice, and Readiness Validation

Primary Objective for the Week:

Consolidate all AI-901 domains into exam-ready scenario reasoning and practical job-task readiness.

Learning Methodology for the Week:

Use shorter, higher-frequency review sessions. Each day includes a recall block, mixed practice block, and mistake-log correction block. Avoid learning new unrelated services; focus on the official AI-901 scope and the operational focus areas already studied.

Day 1 - Full Domain Map Recall

Goal: Reconstruct the full AI-901 domain and focus-area map from memory.

Tasks: Write both domains and all operational focus areas, then add one scenario clue and evidence type for each.

Learning Method: Use active recall first, source comparison second, and 3-day review scheduling for missed items.

Verification: You can reproduce the map accurately and explain the role of each topic.

Day 2 - Responsible AI and Model Selection Review

Goal: Stabilize first-domain decision rules.

Tasks: Review responsible AI controls, model capability, deployment parameters, and workload classification. Answer mixed scenarios that include governance and technical clues together.

Learning Method: Use two Pomodoro rounds with immediate mistake-log correction.

Verification: You can identify the first dependency before reading the options.

Day 3 - Foundry Chat and Agent Review

Goal: Stabilize Foundry implementation reasoning.

Tasks: Review deployment, endpoint, chat testing, single-agent instruction, tool boundary, and lightweight client clues.

Learning Method: Use scenario-first reading and four-step elimination.

Verification: You can separate deployment evidence from prompt quality, agent configuration, and client application logic.

Day 4 - Text, Speech, Vision, and Image Generation Review

Goal: Stabilize modality-based selection.

Tasks: Review text, speech, vision, and image generation comparison sheets. Write one scenario per modality with a plausible distractor.

Learning Method: Use interleaved recall across modalities.

Verification: You can reject wrong-modality answers quickly and explain the output contract.

Day 5 - Content Understanding Review

Goal: Stabilize structured extraction reasoning.

Tasks: Review documents, forms, images, audio, video, analyzer output, extracted fields, confidence signals, and application consumption.

Learning Method: Use extraction scenario cards and mistake-log review.

Verification: You can distinguish structured extraction from summarization, transcription, and visual description.

Day 6 - Final Mixed Mock and Weak-Area Repair

Goal: Test readiness under mixed conditions.

Tasks: Complete a final mixed mock set. Review every missed or guessed item. Rewrite weak rules into concise flashcards.

Learning Method: Use timed practice, slow review, and a final spaced-repetition pass at the end of the day.

Verification: Misses are explained by category and corrected with a specific decision rule.

Day 7 - Exam Readiness Rehearsal

Goal: Enter the exam with a stable decision process.

Tasks: Rehearse the sequence: identify objective, mark constraints, classify modality or governance requirement, choose capability boundary, validate evidence, eliminate distractors, and select the best answer.

Learning Method: Use short recall rounds rather than long new-study sessions. Review only weak cards, comparison sheets, and the final mistake log.

Verification: You can explain a mixed scenario aloud in under two minutes and defend the selected option against all three distractors.