Microsoft Certified: Azure AI Cloud Developer Associate

categoryTopics Covered

Microsoft's own objectives, in the order the exam presents them. Each area links to its section on Microsoft Learn. Checked 2026-10-26.

1. 1Develop containerized solutions on Azure20–25%expand_more

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   Implement container application hosting

   • Build, store, version, and manage container images by using Azure Container Registry
   • Build and run images by using Azure Container Registry Tasks
   • Deploy containers to Azure App Service, including configuring App Service to supply environment variables and secrets

   Implement container-orchestrated solutions

   • Deploy applications to Azure Container Apps, including environment configuration and revision management
   • Implement event-driven scaling by using Kubernetes Event‑driven Autoscaling (KEDA) in Container Apps
   • Deploy and manage applications to Azure Kubernetes Service (AKS) by using manifest files
   • Monitor and troubleshoot solutions on AKS and Container Apps by inspecting logs, events, and end-to-end connectivity
2. 2Develop AI solutions by using Azure data management services25–30%expand_more

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   Develop AI solutions by using Azure Cosmos DB for NoSQL

   • Connect to Azure Cosmos DB for NoSQL by using the SDK and run queries
   • Optimize query performance and Request Units (RUs) consumption by using indexing policies and consistency levels
   • Store and retrieve embeddings and execute vector similarity search for semantic retrieval
   • Implement a change feed processor to detect and handle new or updated items

   Develop AI solutions by using Azure Database for PostgreSQL

   • Connect and query Azure Database for PostgreSQL by using SDKs
   • Model schemas and implement indexing strategies, including designing tables and choosing appropriate data types
   • Implement indexing strategies, including optimizing query latency and reducing pgvector compute overhead
   • Configure compute, memory, and storage resources to support vector workloads
   • Run vector similarity search, including storing embeddings, semantic retrieval, and implementing retrieval-augmented generation (RAG) patterns by using metadata filter
   • Implement connection optimization to improve throughput and minimize latency

   Integrate Azure Managed Redis in AI solutions

   • Implement Azure Managed Redis data operations, including caching, expiration, and invalidation
   • Implement vector indexing to enable similarity search
3. 3Connect to and consume Azure services20–25%expand_more

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   Develop event- and message-based AI solutions

   • Queue and process back-end operations by using Azure Service Bus, including dead-letter queue handling, messages, topics, and subscriptions
   • Implement event-driven workflows by using Azure Event Grid, including filters, custom events, and retries

   Develop and implement Azure Functions

   • Build serverless APIs, including implementing triggers and bindings
   • Configure and deploy function apps
4. 4Secure, monitor, and troubleshoot Azure solutions20–25%expand_more

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   Implement secure Azure solutions

   • Secure secrets by using Azure Key Vault, including rotation and retrieval
   • Store and retrieve app configuration information by using Azure App Configuration

   Monitor and troubleshoot Azure solutions

   • Trace distributed systems by using OpenTelemetry SDKs
   • Write KQL queries to analyze logs and metrics