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Reference guide

Microsoft AI-200 Course Notes

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Section 1Develop containerized solutions on Azure (20%-25%)Preview
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Summary

Containers are a full deployment lifecycle: ACR images/tasks, App Service custom containers, Container Apps revisions/KEDA, AKS manifests, and end-to-end troubleshooting.

Key Points

  • ACR repository groups images; tag is a friendly label; digest is immutable content identity.

Common Mistakes

  • Treating tags as immutable.

Exam Tips

  • Build once; configure at runtime.
Section 2Develop AI solutions by using Azure data management services (25%-30%)Preview
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Summary

AI-200 data is now operational vector storage: Cosmos DB NoSQL, PostgreSQL/pgvector, and Azure Managed Redis—not Azure AI Search as a standalone measured skill.

Key Points

  • Cosmos: partition key + query shape + indexing drive RU cost.

Common Mistakes

  • Treating Azure AI Search as the current data-domain centerpiece.

Exam Tips

  • Cosmos = operational NoSQL + vectors + change feed.
Section 3Connect to and consume Azure services (20%-25%)Preview
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Summary

Connect Azure AI back ends with durable messaging/event routing and serverless compute: Service Bus, Event Grid, and Azure Functions.

Key Points

  • Service Bus queue = point-to-point work queue.

Common Mistakes

  • Using a topic when only one worker should process each message.

Exam Tips

  • Queue = work; topic = fan-out; Event Grid = notify.
Section 4Secure, monitor, and troubleshoot Azure solutions (20%-25%)Preview
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Summary

Security and operations now center on Key Vault, App Configuration, OpenTelemetry, and KQL.

Key Points

  • Key Vault = secrets, keys, certificates; AI-200 focuses on secret retrieval and rotation.

Common Mistakes

  • Using App Configuration as a secret vault.

Exam Tips

  • Secret -> Key Vault; setting -> App Configuration.