AI-200 Beta Study Guide: Azure AI Developer

What the AI-200 exam is

AI-200 targets the Microsoft Azure AI Cloud Developer Associate role: developers who build and ship AI-enabled applications on Azure. It goes beyond calling a model API. You are expected to package solutions in containers, wire them to Azure data services for vector search and retrieval, connect them through messaging and events, and make the whole thing secure and observable in production.

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Who it is for

This is a developer exam, not a concepts exam. It suits software engineers who already write code, understand HTTP APIs, and want to prove they can build AI features that run reliably in the cloud. Comfort with a language such as C#, Python, or JavaScript, and prior exposure to Azure at the AZ-900 level, will help the material make sense quickly.

The domains you must know

• Develop containerized solutions on Azure: build and store images in Azure Container Registry, and deploy to App Service, Azure Container Apps, or Azure Kubernetes Service. Know when a simple managed platform beats a full orchestrator, plus scaling, revisions, and health probes.
• Develop AI solutions with Azure data services: store and query embeddings for vector search, and implement retrieval augmented generation (RAG). Know the data-side options: Azure Cosmos DB, Azure Database for PostgreSQL with the pgvector extension, and Azure Cache for Redis for fast retrieval and caching.
• Connect to and consume Azure services: use Azure Service Bus and Event Grid for messaging and events, and Azure Functions for event-driven and glue code, so components stay decoupled and resilient.
• Secure, monitor, and troubleshoot: keep secrets in Azure Key Vault, manage settings with Azure App Configuration, prefer managed identities over keys, and instrument apps with OpenTelemetry, then query telemetry with KQL in Azure Monitor and Application Insights.

How to prepare

Build a small end-to-end project rather than reading alone. Containerize a simple API, push it to Azure Container Registry, and run it on Container Apps. Add a vector store using pgvector or Cosmos DB, implement a basic RAG endpoint, and put a Service Bus queue in front of a background worker. Then move every secret into Key Vault, switch to a managed identity, and add OpenTelemetry so you can query your own traces with KQL. Doing this once teaches more than any summary.

How long it takes

Experienced Azure developers often need three to six weeks, roughly 40 to 60 hours, given the breadth from containers to vector search to observability. If containers or Azure are new to you, plan for more and prioritize a working RAG project. Beta scoring can also take longer to arrive, so plan your timeline accordingly.

How to know you are ready

You are ready when you can containerize and deploy a service without notes, explain and debug a RAG pipeline end to end, choose the right messaging service for a scenario, and describe how you would secure and observe the app in production. If you have built that loop once with your own hands, the beta will feel like familiar ground. A quick check of where you stand across all four domains will point you to the last gaps worth closing.