The Microsoft Certified: Azure AI Apps and Agents Developer Associate certification is Microsoft's newer Azure AI developer track for building modern generative AI applications and agentic systems on Azure and Microsoft Foundry. It targets engineers who can plan, build, deploy, and maintain production AI apps rather than simply experiment with prompts in notebooks.
This exam is currently in beta as of May 28, 2026, and Microsoft labels it AI-103. The public exam page focuses on Azure AI planning, generative AI, agents, computer vision, text analysis, and information extraction. Microsoft also notes that a public practice assessment is not yet available while the exam is still in beta.
AI-103 sits between Azure AI fundamentals knowledge and production AI delivery. Microsoft is testing whether you can choose the right Azure AI building blocks, implement them safely, and connect them into real applications and agent workflows. That makes this a service-selection and implementation exam, not a pure machine-learning theory exam.
Exam At a Glance
| Attribute | Value |
|---|---|
| Certification | Microsoft Certified: Azure AI Apps and Agents Developer Associate |
| Exam code | AI-103 |
| Status | Beta as of 2026-05-28 |
| Level | Intermediate / Associate |
| Duration | 120 minutes |
| Cost | $165 USD |
| Languages | English |
| Prerequisites | No formal prerequisite, but Microsoft expects Python development experience plus familiarity with general AI, generative AI, and Azure services |
| Target candidate | Azure AI engineers and developers building apps and agents on Azure and Microsoft Foundry |
| Primary focus | Planning AI solutions, implementing generative AI and agents, computer vision, language, and information extraction |
- Official certification page: Microsoft Certified: Azure AI Apps and Agents Developer Associate
- Official exam page: Exam AI-103: Azure AI Apps and Agents Developer Associate
- Official study guide: AI-103 study guide
- Official course: Develop AI apps and agents on Azure
- Official learning paths: Develop generative AI apps in Azure, Develop AI agents on Azure, Develop natural language solutions in Azure, Extract insights from visual data on Azure
Official Assessed Areas
- Plan and manage an Azure AI solution
- Implement generative AI and agentic solutions
- Implement computer vision solutions
- Implement text analysis solutions
- Implement information extraction solutions
Because AI-103 is still in beta, Microsoft can refine the exam before general availability. Use the official study guide and course structure as your source of truth, and expect the domain boundaries to stay broadly stable even if subtopics evolve.
1. Plan and Manage an Azure AI Solution
This first domain is about architecture and service fit. You need to know how the Azure AI portfolio and Microsoft Foundry pieces fit together before you start implementing features.
- Azure AI portfolio and Microsoft Foundry positioning - Understand what each service family is for and how Azure AI services, Azure AI Search, Azure OpenAI, and Microsoft Foundry fit into end-to-end delivery. Official resources: What is Azure AI Foundry?, AI-103 course.
- Planning for secure and maintainable AI delivery - Microsoft expects you to think about deployment, monitoring, integration, and lifecycle concerns from the start, not as afterthoughts. Official resources: AI-103 exam page, Well-Architected Framework for AI workloads.
- Responsible AI and service selection - Planning questions often reward the answer that matches the right capability to the scenario while respecting safety and operational constraints. Official resources: Responsible AI concepts in Azure AI Foundry, AI-103 course.
Exam tip: If the question asks how to structure an Azure AI solution, classify the workload first: generation, agent orchestration, language understanding, vision, or information extraction.
2. Implement Generative AI and Agentic Solutions
This is the center of AI-103. It tests whether you can build modern AI apps that use models, prompts, retrieval, and agent workflows appropriately.
- Generative AI application patterns - Study prompt-based applications, grounding, evaluation, and how Azure-hosted generative capabilities fit into application code. Official resources: Develop generative AI apps in Azure, Azure OpenAI in Azure AI Foundry Models overview.
- Agent design and orchestration - Microsoft explicitly tests agentic solutions, so understand where agents fit relative to simpler prompt flows and application logic. Official resources: Develop AI agents on Azure, Azure AI Foundry Agent Service overview.
- Grounding, retrieval, and tool use - The exam is likely to test patterns that connect models and agents to enterprise context instead of treating generation as an isolated chat prompt. Official resources: Azure AI Search overview, Generative AI apps learning path.
- This domain is implementation-focused - Microsoft is asking whether you can build AI application behavior with the right Azure components, not whether you can train foundation models. Official resource: AI-103 course.
Exam tip: If the scenario includes prompts, retrieval, tools, conversation flow, or multi-step reasoning, think generative and agentic architecture before you think classic prediction models.
3. Implement Computer Vision Solutions
This section covers image and visual-data understanding inside Azure AI applications.
- Visual insight workloads - Be comfortable identifying image analysis, OCR, visual captioning, object-level understanding, and related vision scenarios. Official resources: Extract insights from visual data on Azure, Azure AI Vision overview.
- Vision service selection - Questions usually test which Azure AI service or feature family best fits the visual scenario, not low-level model mathematics. Official resources: Visual data learning path, AI-103 exam page.
- Vision often overlaps with extraction workflows - Keep in mind that visual data questions can connect to OCR and downstream structured extraction tasks. Official resources: OCR overview, Vision path.
Exam tip: If the prompt is about pixels, images, screenshots, or extracting visible information from a document or scene, start with the vision capability family.
4. Implement Text Analysis Solutions
This domain is about language-centered AI features that interpret, classify, or analyze text.
- Natural language solutions on Azure - Study sentiment, classification, entity recognition, summarization, translation, conversational understanding, and related language scenarios at the application level. Official resources: Develop natural language solutions in Azure, Azure AI Language overview.
- Language capability recognition - Microsoft typically tests whether you can identify the right text-analysis feature for the problem rather than invent a custom NLP pipeline from scratch. Official resources: Language solutions learning path, AI-103 exam page.
- Language questions stay close to application outcomes - Think in terms of the user-facing or business-facing capability needed: classify, extract, summarize, translate, or understand. Official resource: AI-103 course.
Exam tip: If the scenario is primarily about text meaning rather than content generation, suspect Azure AI Language before you reach for generative patterns.
5. Implement Information Extraction Solutions
This final domain covers extracting structure and usable data from documents and broader information sources.
- Document intelligence and structured extraction - Review how Azure extracts fields, tables, and structured content from business documents. Official resources: Azure AI Document Intelligence overview, AI-103 exam page.
- Search and retrieval support for extraction workflows - Information extraction questions can connect to indexing, retrieval, and surfacing extracted content inside broader AI applications. Official resources: Azure AI Search overview, AI-103 course.
- Extraction is about usable structure - Microsoft is usually testing whether you understand how to turn unstructured input into data your application can search, route, or reason over. Official resources: Document Intelligence overview, Azure AI Search overview.
Exam tip: If the goal is to pull fields, records, or searchable information out of documents and other unstructured sources, think extraction workflow rather than plain text analytics.
Recommended 4-Week Study Plan
| Week | Focus | Primary resources |
|---|---|---|
| 1 | Azure AI portfolio, Foundry basics, AI planning, responsible AI, architecture choices | AI-103 course, Azure AI Foundry overview, responsible AI concepts |
| 2 | Generative AI apps, grounding, agents, retrieval, Azure OpenAI, agent workflows | Develop generative AI apps path, develop AI agents path, Azure OpenAI overview, Agent Service overview, Azure AI Search overview |
| 3 | Computer vision and language solutions | Visual data path, Azure AI Vision overview, language solutions path, Azure AI Language overview |
| 4 | Information extraction, document workflows, mixed review, beta study guide review | Document Intelligence overview, Azure AI Search overview, AI-103 study guide, exam sandbox |
Last-Mile Exam Strategy
- Study AI-103 as an application-composition exam. The core skill is choosing and combining the right Azure AI services for the product behavior you need.
- Spend extra time on generative AI and agents. Those topics are central to the exam identity and the most likely place for beta-era question density.
- Use the official learning paths as your structure, then reinforce with Azure AI Foundry, Azure OpenAI, Language, Vision, Search, and Document Intelligence overview docs.
- Remember that the exam is in beta. Anchor to the official study guide and avoid over-trusting outdated AI-102-era prep material.
- When a question feels broad, classify the capability first: generation, agent orchestration, language understanding, visual understanding, or information extraction. That usually narrows the right service family quickly.
If you want exam-style reinforcement after the official docs, use our AI-103 practice questions if the repo route is active in your environment. For foundational context, pair this with our Azure AI Fundamentals study guide. If you also need the operational side of AI delivery, the internal roadmap already pairs this track with AI-300 for MLOps and GenAIOps depth.
The fastest way to pass AI-103 is to think like an Azure AI application developer: plan with the right Foundry and Azure service boundaries, build generative and agentic flows deliberately, and stay sharp on the classic AI service categories that still power real solutions. Because the exam is beta, staying close to the current Microsoft Learn material matters more than ever.