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AI-200 study guide

All 27 topics as a feed, in exam order. Swipe through a topic, say whether you know it, and answer its question. What you miss comes back later.

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The route

What the guide covers

The topics follow the official skills outline of April 15, 2026, one study card per exam objective. Each links to its card, if you would rather look something up.

1 Develop containerized solutions on Azure

20-25%
  1. ACR images and tags Use unique image tags for deployments, immutable digests for exact versions, and delete images rather than only untagging them.
  2. ACR Tasks Build container images in Azure on demand or from commit, base-image, and timer triggers, without a local Docker engine.
  3. App Service containers Run a custom image in App Service, configure its port and environment, and pull from ACR securely with managed identity.
  4. Container Apps revisions Revision-scope changes create a new revision; application-scope settings do not. Use multiple revisions to split traffic safely.
  5. KEDA scaling Use KEDA scale rules to turn HTTP, TCP, queue, and other event signals into Container Apps replicas, including scale to zero.
  6. AKS manifests A Deployment runs pods and a Service exposes them. Their labels and ports must line up for traffic to reach the app.
  7. Container troubleshooting Troubleshoot from the inside out: platform events, app logs, service endpoints, then ingress, DNS, and network connectivity.

2 Develop AI solutions by using Azure data management services

25-30%
  1. Cosmos SDK queries Use point reads when id and partition key are known; use parameterized queries when you need filtering or multiple items.
  2. Cosmos indexing and RUs Tune RU cost with selective indexing, composite indexes, and the right consistency level, then verify with request charge and index metrics.
  3. Cosmos vector search Store embeddings with items, define a vector policy and index, then rank semantic matches with VectorDistance and TOP N.
  4. Cosmos change feed Process inserts and updates from the change feed, checkpoint progress in leases, and design handlers for at-least-once delivery.
  5. PostgreSQL from Python Connect with psycopg over SSL, use a fresh Microsoft Entra token as the password, and pass SQL values as parameters.
  6. Vector schema design Store chunk text, metadata, and a dimensioned embedding together; choose vector, halfvec, or sparsevec to fit the model and storage needs.
  7. pgvector index tuning Choose exact search, IVFFlat, or HNSW based on recall and latency, then tune query-time settings and verify the plan.
  8. Vector workload sizing Size compute, maintenance memory, and storage together, with special attention to large index builds and vector I/O.
  9. PostgreSQL RAG search Filter metadata, rank chunks by vector distance, apply a relevance threshold, and pass only grounded context into the RAG prompt.
  10. Connection pooling Reuse PostgreSQL connections through an app pool or PgBouncer, and account for transaction-pooling behavior when session state matters.
  11. Redis cache-aside Check Redis first, load and cache on a miss, set a TTL, and invalidate only after the source database has been updated.
  12. Redis vector search Enable RediSearch at cache creation, define a matching vector field, choose FLAT or HNSW, and query nearest neighbors with KNN.

3 Connect to and consume Azure services

20-25%
  1. Service Bus messaging Use queues for competing consumers, topics for fan-out through subscriptions, and dead-letter queues for messages that cannot be completed.
  2. Event Grid routing Publish custom events to a topic, filter them in event subscriptions, and plan retry and dead-letter behavior for failed deliveries.
  3. Functions triggers and bindings One trigger starts each Azure Function; bindings bring data in or send it out, while SDK clients handle interactions that need more control.
  4. Functions hosting and deploy Choose the Functions hosting plan for scaling, networking, and control, then separate app-wide, local, and Azure configuration correctly.

4 Secure, monitor, and troubleshoot Azure solutions

20-25%
  1. Key Vault rotation Use managed identity to read Key Vault secrets, then rotate them by creating new versions from expiry-driven automation.
  2. App Configuration Store labeled configuration and feature flags centrally, refresh them deliberately, and keep secret values in Key Vault behind references.
  3. OpenTelemetry tracing Trace one operation across services with linked spans, identify each service, and send the correlated telemetry to Application Insights.
  4. KQL for logs Filter Azure Monitor data early, aggregate with summarize, rank the result, and project only the columns you need.