Microsoft Azure AI Cloud Developer AssociateStudy guide
The associate developer certification for building, integrating, and monitoring AI cloud solutions on Azure (AI-200).
About Microsoft Azure AI Cloud Developer Associate (AI-200)
Microsoft Azure AI Cloud Developer Associate (AI-200) is a Associate-level certification from Microsoft. This page organizes the exam scope into a 4-chapter, 8-section study guide and lets you check your understanding with exam-style practice questions. A good flow is to read the chapters below in order, then test yourself via "Practice questions."
Exam domains (approximate weighting)
- Azure data management services for AI (vector DBs)~28%
- Containerized and serverless compute~24%
- Messaging and event-driven AI pipelines~24%
- Security and observability~24%
Weights are approximate guidance for the live exam. Each domain is covered in detail in the chapters and sections below.
Official exam information: https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ai-200
1Azure Data Management Services for AI
- 1.1Vector Data and Similarity Search
Understand the "vector" concepts behind the data layer of AI apps (especially RAG)—vectorization via embeddings, semantic search by similarity (distance), and approximate nearest neighbor (ANN) indexes—from a developer’s view.
- 1.2Azure Vector-Enabled Data Services
Understand Azure options for storing/searching AI app vectors—Azure AI Search, Cosmos DB for NoSQL, Azure Database for PostgreSQL (pgvector), and Azure Managed Redis—and how to choose by use case, from a developer’s view.
2Containerized and Serverless Compute
- 2.1Running AI Workloads in Containers
Understand running/distributing AI back-ends in containers—container basics, image management in Azure Container Registry (ACR), and choosing between Azure Container Apps and Azure Kubernetes Service (AKS)—from a developer’s view.
- 2.2Scaling and Serverless Functions
Understand event-driven autoscaling with KEDA, serverless Azure Functions (triggers and bindings), and compute selection—in the context of AI back-ends.
3Messaging and Event-Driven AI Pipelines
- 3.1Asynchronous, Loosely Coupled Messaging
Understand messaging that connects AI back-ends in a loosely coupled, resilient way—Azure Service Bus (queues/topics) and Azure Event Grid—and how to choose between them, from a developer’s view.
- 3.2Event-Driven AI Pipelines and AI Service Integration
Understand building event-driven AI pipelines by combining messaging, functions, and containers, integrating Azure OpenAI / Azure AI services in a loosely coupled way, and designing for resilience (retries, idempotency).
4Security and Observability
- 4.1Secret Management and Identity
Understand securing an AI back-end’s secrets and configuration—Azure Key Vault (secrets/keys/certificates), Azure App Configuration (settings and feature flags), and keyless auth via managed identity with RBAC—from a developer’s view.
- 4.2Observability (Monitoring, Tracing, Logs)
Understand observability for a distributed AI back-end—instrumentation with OpenTelemetry, distributed tracing, Application Insights / Azure Monitor, log analysis with KQL, and monitoring tokens/cost/quality.