The Microsoft AI-103 exam measures an engineer’s ability to build and run AI applications and agents on Azure with Microsoft Foundry. Its scope is broader than chatbots: candidates must make dependable choices about models, retrieval, agent tools, deployment, safety, multimodal processing, text and speech, and document extraction. This learning center organizes existing practical explanations and original scenario practice by the five domains in Microsoft’s current blueprint, rather than adding another general exam overview.
Microsoft’s April 16, 2026 skills outline leads to Microsoft Certified: Azure AI Apps and Agents Developer Associate. Candidates should have Python development experience and be familiar with generative AI, general AI and Azure services. The retired AI-102 exam is not the current booking target. If you studied an older Azure AI Engineer course, review which AI-102 skills still transfer before revising against the newer requirements.
What the current exam actually measures
The official AI-103 study guide lists five groups of measured skills. Its published weighting ranges help prioritize preparation but are not a promise of fixed question counts or specific scenarios:
The two largest groups—planning/management and generative/agentic engineering—should receive significant attention. The other three must not disappear from the study plan, however. A retrieval application can fail because OCR lost a table; a multilingual agent can act on an incorrect transcript; a vision model can misinterpret text in an image as instructions. Integrating domains is more valuable than learning each service as an isolated product.
Plan and manage an Azure AI solution — 25–30%
Use Azure AI solution planning and Foundry service and deployment architecture for model choice, resource boundaries, CI/CD, identity, budgets and operational release controls. Compare concrete requirements rather than defaulting to the largest model or broadest role. Safety is part of the design: responsible-AI controls covers guardrails and accountable tool access.
Original practice questions
Implement generative AI and agentic solutions — 30–35%
Build one useful application instead of collecting disconnected prompting tricks. Generative AI and agents supplies the broad engineering model; RAG and grounding and agent orchestration and tool use cover the distinct retrieval and action boundaries. Use GenAIOps to understand traces and release regression evidence.
Original practice questions
- Generative applications, RAG and Foundry integration
- Agent tools and multi-agent workflows
- Generation tuning, reflection and observability
Implement computer vision solutions — 10–15%
The vision domain includes multimodal understanding, generated and edited images or videos, and safety controls. The computer vision guide and multimodal engineering guide help distinguish understanding an existing image from generating or editing new media.
Original practice questions
Implement text analysis solutions — 10–15%
Start with the text, speech and translation engineering guide. It follows multilingual inputs through recognition, entity or intent extraction, translation, voice output and authorization. A fluent transcript is not enough if the application changes an order code or acts on an ambiguous command.
Original practice questions
Implement information extraction solutions — 10–15%
Use Azure AI Search and retrieval for indexing, hybrid/vector search and grounding. The separate document extraction and Content Understanding guide follows OCR, layout, structured fields, provenance and reviewer decisions. Search relevance and exact financial-field extraction are different engineering problems.
Original practice questions
An integrated readiness exercise
Build one nonproduction application in stages. Imagine an internal support assistant that answers policy questions, accepts a photo of a delivery document, and can propose—but not unilaterally execute—a ticket update. Each stage tests a separate part of the blueprint. Keep resources and data within an authorized test subscription, observe costs, and delete unused lab assets.
- Plan. Choose the Foundry model and region, deployment and identity configuration, retrieval method, monitored signals and budget. Explain why a smaller or larger model fits the user task.
- Ground. Ingest authorized knowledge documents, preserve source and access metadata, build searchable chunks and evaluate relevance on known questions.
- Act safely. Add a narrowly scoped ticket-lookup tool. Require confirmation before state-changing actions. Test a retrieved document containing malicious instructions and verify that it cannot change tool permissions.
- Use multiple modalities. Accept one document image, validate extracted identifiers and add a multilingual text or speech option. Record when the system requests clarification instead of inventing a field.
- Observe. Capture latency, errors, retrieval quality, groundedness, token usage, tool results and safety exceptions. Make a controlled change to the prompt or index and determine whether the existing test set catches a regression.
A strong completion record is more than a screenshot showing a successful deployment. Keep the requirement, architecture, a real authorized user test, a negative or failure test, and the evidence that the application stayed within its intended permissions. For unfamiliar scenarios, use the individual practice sets above to test reasoning, then return to the implementation that exposed the underlying weakness.
Use the appropriate next resource
Readers beginning with the syllabus can use the AI-103 objective breakdown; those scheduling preparation may prefer the existing AI-103 study plan. If you are confident with service names but consistently miss scenario questions, the error-analysis method can help isolate the specific decision you do not yet understand. These pages serve different reader tasks and do not need to become another broad summary of every Azure AI feature.
Always reconcile API and product behavior with current Microsoft documentation. Foundry services, region support, preview features and pricing can change faster than a study article. The best preparation is not remembering a portal interface; it is being able to explain, test and repair the whole Azure AI solution.
