AI-103 preparation should feel like designing real AI applications, not memorizing isolated service names. The exam direction is centered on planning and managing Azure AI solutions, building generative AI and agentic experiences, and applying computer vision, text analysis, and information extraction where they fit.
Start with Microsoft Foundry and the application lifecycle: choose a model, ground responses with data, design prompts and agents, evaluate outputs, monitor quality, and protect the solution. Then study each workload as a product problem. What does the user ask for? What data is available? What risk needs to be controlled?
What to study first
Begin with AI-103 exam objectives. The largest areas are planning/managing solutions and generative AI with agents, so they deserve more review time. Computer vision, text analysis, and information extraction are smaller, but they are easier to miss if you cannot recognize the scenario.
Use the AI-103 Practice Test after each workload. For wrong answers, write whether the miss came from architecture, Foundry workflow, retrieval and grounding, multimodal AI, monitoring, security, or extraction logic.
Verified against the official vendor reference: learn.microsoft.com.