AI-200 study notes

9 learning paths grouped by the official skill areas. Your reading progress stays in this browser.

Develop containerized solutions on Azure

20–25% of the exam

Deploy and operate containers on Azure App Service
Deploy custom containers to Azure App Service from ACR (managed identity or admin credentials), Docker Hub, and other registries using the portal and CLI. Configure app settings, connection strings, slot settings, Key Vault references, and runtime behavior (ports, startup, Always On, health checks, persistent storage). Cover day-two operations—image updates, continuous deployment, and pull behavior—plus troubleshooting with log stream, filesystem logs, Kudu, and common failure patterns. Includes a hands-on Quote API project that builds with ACR Tasks and runs on Azure Container Apps.
0 of 30 read
Deploy and manage apps on Azure Container Apps
Learn how to deploy, manage, and scale containerized AI applications on Azure Container Apps. Configure secrets and environment variables, pull images from private registries, verify deployments, manage the day-two lifecycle, and implement automatic scaling with KEDA.
0 of 14 read
Deploy and monitor applications on Azure Kubernetes Service
This learning path guides you through the complete lifecycle of running applications on Azure Kubernetes Service: creating deployment manifests, exposing applications with Kubernetes Services, externalizing configuration with ConfigMaps, securing sensitive settings with Secrets, attaching persistent storage, and monitoring/troubleshooting application health and connectivity.
0 of 11 read

30–35% of the exam

Develop AI solutions with Azure Cosmos DB for NoSQL
This learning path guides you through developing AI solutions using Azure Cosmos DB for NoSQL: building a data foundation with the resource model, SDK integration, CRUD, and SQL queries; implementing vector search for embeddings, similarity queries, hybrid search, and change-feed sync; and optimizing query performance via indexing and consistency levels.
0 of 16 read
Develop AI solutions with Azure Database for PostgreSQL
This learning path guides you through developing AI solutions using Azure Database for PostgreSQL: building a data foundation with schema design, SQL queries, and secure Python integration via Microsoft Entra authentication; implementing vector search with pgvector for embeddings and RAG retrieval patterns; and optimizing vector search performance through tuning, indexing, data layout, scaling, and connection pooling.
0 of 19 read
Enhance AI solutions with Azure Managed Redis
Learn how to use Azure Managed Redis to enhance your AI solutions, including caching strategies, data operations, event messaging, and vector storage.
0 of 12 read

20–25% of the exam

Integrate backend services for AI solutions
This learning path teaches you how to build and integrate backend services that support AI solutions on Azure: using Azure Service Bus to decouple components and queue inference requests; building event-driven workflows with Azure Event Grid; and creating serverless AI backends with Azure Functions.
0 of 12 read

20–25% of the exam

Manage application secrets and configuration for AI solutions
This learning path teaches you how to securely manage secrets and centralize configuration for AI solutions on Azure using Azure Key Vault (managed identity auth, secret versioning/rotation, caching) and Azure App Configuration (centralized settings, labels, feature flags, Key Vault references).
0 of 11 read
Observe and troubleshoot apps on Azure
This learning path teaches you how to gain end-to-end observability into distributed AI applications on Azure: instrumenting applications with OpenTelemetry to capture distributed traces and export telemetry to Azure Monitor Application Insights, and analyzing telemetry with KQL queries, dashboards, workbooks, and alerts.
0 of 12 read