AI-103 Study Plan: A 30-Day Readiness Path
Allocate time by objective weight, build one small system end to end, and let scenario practice decide what you revise next.
Three rules for the month
- Use the current blueprint. Keep the official AI-103 study guide open and map every study task to one of its five domains.
- Build as well as read. Configuration, identity, RAG, agents, evaluation, and troubleshooting become clearer when you operate them.
- Review decisions, not letters. For every wrong answer, explain why the best option satisfies the scenario and why each distractor fails.
The 30-day sequence
Plan and manage the AI solution
Cover Foundry resources and projects, model deployments, connections, quotas, managed identity, RBAC, networking, safety, monitoring, and cost controls. Build a project and draw its trust boundaries.
Readiness gate: you can choose an identity, scope its permissions, explain private access, and diagnose a deployment or quota constraint.
Generative AI, RAG, and agents
Give the largest domain the largest block of time. Practise model selection, prompting, structured output, retrieval design, AI Search, grounding, tools, agent state, orchestration, multi-agent choices, evaluation, tracing, and safety.
Readiness gate: you can design a grounded agent, choose its tools and identity, and explain how you would evaluate quality and harmful behaviour.
Vision, language, speech, and extraction
Work through the three 10–15% domains. Focus on service selection, supported inputs and outputs, SDK/REST patterns, confidence and error handling, batch versus real-time work, and when information extraction is a better fit than open-ended generation.
Readiness gate: given a scenario, you can select the simplest suitable service and reject plausible alternatives.
Timed scenarios and targeted repair
Alternate timed sets with review sessions. Track misses by objective, not just total score. Revisit documentation for repeated gaps and rebuild one failing component rather than rereading everything.
Readiness gate: two separate timed attempts show stable performance across all five domains, with no domain depending on guesses.
Suggested time allocation
| Objective domain | Official weight | Study emphasis |
|---|---|---|
| Plan and manage an Azure AI solution | 25–30% | High |
| Implement generative AI and agentic solutions | 30–35% | Highest |
| Implement computer vision solutions | 10–15% | Focused |
| Implement text analysis solutions | 10–15% | Focused |
| Implement information extraction solutions | 10–15% | Focused |
Final readiness checklist
- I can explain every component in a Foundry solution and its permission boundary.
- I can choose between retrieval, tools, workflows, and multiple agents from scenario constraints.
- I can identify how to evaluate groundedness, safety, quality, latency, and operational health.
- I can choose between vision, language, speech, search, document, and content-understanding capabilities.
- I can read Python, REST, JSON, and CLI fragments without relying on memorised wording.
- I have reviewed every repeated error against current Microsoft documentation.
Start with a measured baseline
Ten scenarios are enough to expose the first gaps and decide where day one should begin.
Start the free AI-103 diagnosticComing from AI-102?
Read what changed from AI-102 to AI-103 before reusing an older course or question bank.