Developing AI Apps and Agents on Azure
AI-103
AI-103 is an associate-level exam for developing generative AI and agent solutions on Azure, and the successor to AI-102, which retires on 2026-06-30. It validates the practical skills to design, deploy, operate, and secure AI solutions using Python and Microsoft Foundry. The exam covers five areas: planning and managing Azure AI solutions, implementing generative AI and agents, computer vision, text analysis, and information extraction. It emphasizes real implementation and design decisions — such as RAG-based retrieval and grounding, multi-agent orchestration, model and service selection, responsible AI, and document content extraction — rather than concepts alone. It targets AI engineers comfortable with Python and Azure services.
50
120 min
700/ 1000
224+
Target Audience
Azure AI engineers and developers who build, deploy, and operate generative AI and agent solutions on Microsoft Foundry using Python. Suitable for those advancing from AI-900/AI-901 fundamentals or transitioning from the retiring AI-102, and for developers collaborating with solution architects, data scientists, and security engineers on production AI systems.
Prerequisites
Hands-on experience developing applications in Python and familiarity with Azure services, generative AI, and agentic concepts. Prior fundamentals knowledge (AI-900 or AI-901) is helpful but not required. This associate/developer-level exam tests implementation and design decisions rather than only concepts, so practical experience with Foundry SDKs, RAG, and agents is recommended.
Question Types
Language & Recognition
Official Exam Languages
PasslyExam Languages
Recognition Scope
Globally recognized Microsoft role-based associate certification; the successor to AI-102, broadly applicable across countries and industries.
Exam Domains
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
Recommended Study Plan
- Week 1: Map Microsoft Foundry concepts, model/service selection, and core RAG/agent architecture.
- Week 2: Build RAG, function-calling, and agent flows with the Foundry Python SDK, learning the configuration options and trade-offs.
- Week 3: Strengthen computer vision, text analysis, information extraction, security (managed identity), and responsible AI.
- Final 5–7 days: Run timed mocks to adapt to scenario and multi-response items, and eliminate repeated mistakes.
Question Validation Process
Try the practice set directly and judge real-exam similarity yourself.
Frequently Asked Questions
Q. Do I need to code (Python) for AI-103?
A. AI-103 is an associate/developer-level exam and assumes hands-on experience building apps in Python. Expect implementation and design decisions — which model, service, or SDK to use and how to configure it — rather than pure recall. The most effective prep is to actually build RAG and agent flows with the Microsoft Foundry Python SDK so you learn the options and the reasons behind each choice.
Q. How is AI-103 different from AI-102, and which should I take now?
A. AI-103 is the successor to AI-102 (Azure AI Engineer Associate), which retires on 2026-06-30. AI-103 is rebuilt around Microsoft Foundry, agents and multi-agent orchestration, RAG, evaluation, and responsible AI. If you are starting fresh, choose AI-103 rather than the soon-to-retire AI-102.
Q. Do I need AI-900 or AI-901 first?
A. Not required. Fundamentals (AI-900/AI-901) help, but AI-103 tests real implementation and design decisions, not basic concept checks. If your fundamentals are thin, first firm up AI workload types and service-role boundaries, then spend most of your study time on Foundry implementation — RAG, agents, and security.
Q. How should I prepare, and what score is safe?
A. The passing score is 700 out of 1000. A good benchmark is ~75–80%+ across your last three mocks with fewer repeated mistakes. Practice building your reasoning as requirement → constraints (cost, latency, security) → best service/configuration, especially for scenario and multi-response items. Common mistakes include choosing fine-tuning where RAG is required, or hardcoding API keys instead of using a managed identity.
Q. How do practice questions and the timed mock exam differ?
A. Practice questions are solved by topic with answer explanations to shore up weak areas; the timed mock exam is taken like the real thing. Both use original questions with AI tutor explanations.
Q. Is this similar to exam dumps?
A. PasslyExam does not provide leaked dumps. Our content is built from official guides and public exam objectives, then tuned to reflect realistic question patterns and difficulty distribution. The focus is practical readiness with explanation-based learning, not memorization of leaked items.
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