The AI-102 exam, Designing and Implementing a Microsoft Azure AI Solution, validates that you can build, manage and deploy AI solutions on Azure. As an Azure AI engineer you work across the whole lifecycle — requirements, development, deployment, integration, maintenance, performance tuning and monitoring — usually in Python or C# via REST APIs and SDKs.

The skills-measured outline is grouped into six areas, each weighted: Plan and manage an Azure AI solution (20–25%), Implement generative AI solutions (15–20%), Implement an agentic solution (5–10%), Implement computer vision solutions (10–15%), Implement natural language processing solutions (15–20%) and Implement knowledge mining and information extraction solutions (15–20%).

A score of 700 out of 1000 is required to pass. Knowing the weightings tells you where to spend your study time — the largest blocks are planning, NLP, knowledge mining and generative AI.

Map your study time to the exam weightings: planning, NLP, knowledge mining and generative AI together make up the majority of the exam.

📋 6 skill areas🎯 Pass = 700/1000🐍 Python & C#

🧠 Microsoft Azure AI Engineer (AI-102)