AI-200 Study Notes & Learning Paths
Free Microsoft AI-200 study notes and learning paths for the Azure AI Cloud Developer Associate exam. Select a topic to open its study page with modules and sub-topics.
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Showing 9 learning paths
5 modules
AI Solution Development & IntegrationDeploy 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.
containersacrapp-servicecontainer-apps
Intermediate·2 hr 35 min·3 modules
AI Solution Development & IntegrationDeploy 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.
containerscontainer-appskedaacr
Intermediate·3 modules
AI Solution Development & IntegrationDeploy 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.
containersakskubernetes
Intermediate·3 modules
Vector Database ManagementDevelop 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.
cosmos-dbvectorragembeddings
Intermediate·3 modules
Vector Database ManagementDevelop 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.
postgresqlpgvectorvectorrag
Intermediate·3 modules
Vector Database ManagementEnhance 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.
rediscachevectorembeddings
Intermediate·3 modules
AI Solution Development & IntegrationIntegrate 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.
service-busevent-gridazure-functionsbackend
Intermediate·2 modules
AI Security & Secret ManagementManage 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).
key-vaultapp-configurationsecurity
Intermediate·2 modules
Distributed Observability & MonitoringObserve 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.
monitoringopentelemetrykqlobservability
Quick reference guides
Condensed notes with Python, Azure CLI samples, and exam tips.
Container Apps & ACR for AI Workloads
Host and deploy containerized AI applications with Azure Container Registry and Container Apps.
Cosmos DB for AI & Vector Search
Store embeddings and metadata in Cosmos DB for NoSQL with vector similarity search.
PostgreSQL with pgvector for RAG
Use Azure Database for PostgreSQL with pgvector for semantic search and RAG.
Azure Managed Redis for AI Caching
Cache embeddings, session data, and vector indexes with Azure Managed Redis.
Service Bus & Event Grid for AI Pipelines
Build event-driven AI workflows with messaging and event routing.
Azure Functions for Serverless AI
Trigger AI processing with HTTP, queues, blobs, and Event Grid in Azure Functions.
Security with Key Vault & App Configuration
Secure secrets, keys, and feature flags for AI applications.
Monitoring & KQL Troubleshooting
Observe AI workloads with OpenTelemetry, Application Insights, Log Analytics, and KQL — traces, tables, queries, dashboards, workbooks, and alerts.
Areas covered
All topics across the AI-200 learning paths.
Application DevelopmentArtificial IntelligenceAzure App ConfigurationAzure App ServiceAzure Container AppsAzure Container RegistryAzure Cosmos DBAzure Database for PostgreSQLAzure Event GridAzure FunctionsAzure Key VaultAzure Kubernetes Service (AKS)Azure Managed RedisAzure MonitorAzure Service BusBackend DevelopmentCacheContainersDatabasesDeveloperIntermediateSecurity