AI-103 study roadmap with practical projects

Begin with the current exam blueprint

Microsoft updates role-based exams, so the official AI-103 study guide must remain your source of truth. As of the verification date for this guide, the outline covers planning and managing Azure AI solutions, generative and agentic solutions, computer vision, text analysis, and information extraction.

• Plan and manage an Azure AI solution: 25-30%
• Implement generative AI and agentic solutions: 30-35%
• Implement computer vision solutions: 10-15%
• Implement text analysis solutions: 10-15%
• Implement information extraction solutions: 10-15%

Use one project as the learning spine

Build a support agent for a fictional company and improve it every week. A single evolving system forces you to connect model selection, retrieval, tools, identity, evaluation, and operations instead of memorizing isolated portal steps.

• Ground answers in an approved support knowledge base
• Extract information from an uploaded service document
• Use one read-only support-status tool
• Evaluate answers and tool use against a small test set
• Record architecture and security decisions

Week 1: establish the Foundry foundation

Create the project structure, understand available services, deploy a suitable model, and call it from Python. Learn resource scopes, endpoints, authentication choices, quotas, and deployment constraints.

• Map resource, project, deployment, and application responsibilities
• Use the SDK rather than relying only on portal experimentation
• Capture model name, version, region, quota, and fallback assumptions

Week 2: design prompts and model behavior

Practice system instructions, few-shot examples, structured outputs, and parameter choices. Add tests for incomplete input, conflicting instructions, and answers outside the available evidence.

• Define an output contract
• Separate trusted instructions from user and retrieved data
• Measure quality, latency, and token use
• Document refusal and escalation behavior

Weeks 3-4: retrieval, knowledge, and tools

Add a controlled knowledge source and measure retrieval before judging generated answers. Then expose a narrowly scoped tool and verify selection, arguments, output handling, and approval behavior.

• Compare keyword, vector, and hybrid retrieval
• Preserve source identifiers for citations
• Apply permission-aware filtering where required
• Constrain tools with explicit schemas and allowlists

Weeks 5-6: agents, evaluation, and safeguards

Define the agent role, available tools, conversation behavior, and stopping conditions. Build a representative dataset and evaluate system outcomes as well as step-by-step tool behavior.

• Task completion and intent resolution
• Groundedness and answer completeness
• Tool selection, input accuracy, and call success
• Safety tests, human approval, and recovery paths

Week 7: vision, language, and extraction

Cover the non-generative domains with small working labs. Explain when a specialized Azure service is more appropriate than asking a general-purpose model to perform the task.

• Analyze or classify visual content
• Extract and analyze text
• Process structured information from documents
• Compare accuracy, cost, latency, and operational fit

Week 8: scenario review and exam readiness

Use scenario questions to practice service selection and trade-offs. Revisit weak blueprint areas, complete the official practice assessment, and explain your project without the portal in front of you.

• State requirements before choosing a service
• Identify identity, network, data, and safety boundaries
• Explain how the solution is tested and monitored
• Review the current official study guide again before scheduling

Avoid memorization traps

Do not memorize a screenshot, a preview feature, or a single SDK sample as if it were the platform. Learn the responsibility of each component and verify names, availability, and exam objectives in current Microsoft documentation.