AI-103 Certification Guide: Skills, Cost, Prep

A stack of chatbot prototypes no longer proves you can ship. Employers want developers who ground models, call tools, and run agents safely in production on Azure. AI-103 is the exam for Microsoft Certified: Azure AI Apps and Agents Developer Associate, the associate credential built around Microsoft Foundry, generative AI, and agentic workflows.

In this guide, you’ll find what the exam covers, who it’s for, how it differs from AI-102, and a practical prep path, from learning resources to exam-like questions.

What is the AI-103 certification?

AI-103 is the exam you need to pass to get Microsoft Certified: Azure AI Apps and Agent Developer Associate, an intermediate credential for AI engineers and developers.¹

Exam format, cost, and scoring¹

• Duration: 120 minutes
• Passing score: 700 or greater (on Microsoft’s standard scale)
• Price: $165 USD in the US. Price changes based on the country or region where the exam is proctored
• Delivery: proctored (Pearson VUE); interactive components may appear
• Languages: English, Chinese (Simplified and Traditional), French, German, Japanese, Korean, Italian, Portuguese (Brazil), Spanish
• Renewal: associate certifications expire annually; renew with a free online assessment on Microsoft Learn2²
• Account tip: register with a personal Microsoft account so exam records stay with you

Always confirm price and language options at scheduling time, as they can vary by region.

AI-103 vs AI-102: what changed for developers

Microsoft’s AI-102 study guide states that the exam was retired on June 30, 2026.³

• AI-102 (Azure AI Engineer Associate) measured designing and implementing Azure AI solutions across planning, generative AI, a smaller agentic slice, vision, NLP, and knowledge mining.
• AI-103 puts agent development and Microsoft Foundry at the center, with heavier weight on generative and agentic solutions plus multimodal and information-extraction work for grounded apps.

If you have an existing AI-102 credential, you’ll keep the certification in your transcript, but it doesn’t automatically convert to AI-103. If you want the Azure AI Apps and Agents Developer Associate certification, you need to take the AI-103 exam separately.

Check out the Microsoft Azure Certification Path and see which certification is the right one for you.

Who should take AI-103 (and what you need first)

AI-103 fits software developers, Azure AI engineers, and cloud developers moving into generative AI and agents.

As a candidate, you’re expected to build, manage, and deploy agents and AI solutions that use Microsoft Foundry.

Microsoft lists these as the core responsibilities of the role:¹

• Plan and manage Azure AI solutions.
• Implement generative AI and agentic solutions.
• Implement computer vision solutions.
• Implement text analysis solutions.
• Implement information extraction solutions.

There aren’t any specific prerequisites or certifications you need to have first, but you should have experience building apps with Python and familiarity with general AI, generative AI, and Azure services.¹

Use this quick fit check to know if you’re ready for AI-103:

• You write production-minded Python and consume APIs or SDKs.
• You want to prove agent, RAG, and multimodal skills, not only single-service demos.
• You’re ready to practice security, monitoring, and responsible AI alongside prompts.

Skills measured on the AI-103 exam⁴

Microsoft groups skills into five domains. Generative AI and agentic solutions carry the largest share.

Plan and manage an Azure AI solution — 25–30%

• Choose Foundry services and models, retrieval and indexing approaches, and memory, tools, and knowledge for agents
• Deployment options, CI/CD for Foundry projects, quotas, cost, monitoring, and security (identity, networking, roles)
• Responsible AI: safety filters, evaluators, audit and trace, agent oversight

Implement generative AI and agentic solutions — 30–35%

• Build RAG apps, multistep reasoning, and Foundry SDK integrations
• Define agent roles, tools, function calling, and memory
• Orchestrate multi-agent flows with safeguards and approvals
• Optimize with prompting, parameters, and observability (traces, tokens, safety, latency)

Implement computer vision solutions — 10–15%

• Image and video generation
• Multimodal understanding
• Content Understanding

Implement text analysis solutions — 10–15%

• Core language tasks (classification, extraction, summarization)
• Speech (speech-to-text and text-to-speech) that agents use

Implement information extraction solutions — 10–15%

• Ingest and index patterns
• Semantic, hybrid, and vector search
• Document OCR and layout work that grounds agents and RAG

That’s a lot of ground to cover in 120 minutes. Here’s how to study for it without wasting time on passive review.

How to prepare for AI-103 without drowning in content

Skip endless passive watching. Tie every study hour to a domain skill you can demo.

1. Read the official study guide skills measured and mark gaps.⁴

2. Work the Microsoft Learn Course AI-103T00-A: Develop AI apps and agents on Azure)⁵

3. Build two or three small projects mapped to domains: a RAG app, a tool-calling agent, and a document-grounded flow.

4. For structured, hands-on video training, check out the following course on Learning Platform. This one pairs lectures with labs across all five domains. It’s a large course (over 30 hours), so treat it as a reference to work through domain-by-domain alongside your own project builds, not a single weekend binge.

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AI-103 (Microsoft’s Azure AI Apps and Agents Developer Associate) exam guide: skills, cost, AI-102 changes, and prep tips to plan your path. This guide provides structured insights, proven methodologies, and actionable execution steps for modern professionals.

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