How to Prepare for AI-200 with Hands-On Azure Labs

The best AI-200 prep plan includes hands-on labs. Reading the study guide tells you what Microsoft may ask. Labs teach you how the services behave. Practice questions then test whether you can make the right decision under scenario pressure.

Lab 1: Containerize a simple AI API

Build a small Python or .NET API that returns a simulated AI response. Package it as a container, push it to Azure Container Registry, and deploy it to App Service or Azure Container Apps. Configure endpoint settings through app settings or secrets, not in the image. Then rebuild the image using an ACR Task.

Lab 2: Add asynchronous processing

Create a workflow where an HTTP endpoint accepts a job and places a message on Service Bus. Process the message with an Azure Function or containerized worker. Add retry handling and inspect what happens when processing fails. Dead-letter behavior is easier to understand after you have created a failure yourself.

Lab 3: Build a small retrieval store

Store document metadata and fake embeddings in Cosmos DB or PostgreSQL. Run queries that combine semantic retrieval with metadata filters. If you use PostgreSQL, explore pgvector indexing. If you use Cosmos DB, review request charges and indexing policy effects. The goal is to connect data design to AI application behavior.

Lab 4: Secure and monitor the workflow

Move secrets into Key Vault and use managed identity where possible. Put non-secret configuration in App Configuration. Add structured logs and OpenTelemetry traces across the API, worker, and data call. Write KQL queries to find failures, latency spikes, and top dependency errors.

After each lab, take a short drill in the AI-200 practice exam on ExamStudyApp. That rhythm matters: build, practice, review misses, and rebuild the weak area. It is much more effective than doing all labs first and all questions later.