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Microsoft Azure AI Cloud Developer Associate Study Guide
Phase 1: First Principles of AI Cloud Development on Azure1.1.Why AI Workloads Reshape Back-End Architecture1.1.1.The Anatomy of a Production AI Solution1.1.2.Embeddings and Vector Search from First Principles1.2.The Azure Back-End Toolkit1.2.1.The Compute Spectrum: Choosing Where Code Runs1.2.2.Data, Messaging, and Observability Building Blocks1.3.Reflection CheckpointPhase 2: Develop AI Solutions by Using Azure Data Management Services (25–30%)2.1.Azure Cosmos DB for NoSQL2.1.1.Connecting and Querying with the SDK2.1.2.Request Units, Indexing Policies, and Consistency Levels2.1.3.Storing Embeddings and Vector Similarity Search2.1.4.The Change Feed Processor2.2.Azure Database for PostgreSQL2.2.1.Connecting and Querying with SDKs2.2.2.Schema Modeling and Data Types2.2.3.Indexing Strategies and pgvector Optimization2.2.4.Vector Search and RAG Patterns2.2.5.Compute Sizing and Connection Optimization2.3.Azure Managed Redis in AI Solutions2.3.1.Caching, Expiration, and Invalidation2.3.2.Vector Indexing for Similarity Search2.4.Reflection CheckpointPhase 3: Develop Containerized Solutions on Azure (20–25%)3.1.Container Application Hosting3.1.1.Azure Container Registry: Build, Store, Version, Manage3.1.2.ACR Tasks3.1.3.Deploying Containers to App Service3.2.Container-Orchestrated Solutions3.2.1.Azure Container Apps: Environments and Revisions3.2.2.Event-Driven Scaling with KEDA3.2.3.Deploying to AKS with Manifest Files3.2.4.Monitoring and Troubleshooting AKS and Container Apps3.3.Reflection CheckpointPhase 4: Connect to and Consume Azure Services (20–25%)4.1.Event- and Message-Based AI Solutions4.1.1.Azure Service Bus: Queues, Topics, and Dead-Lettering4.1.2.Azure Event Grid: Filters, Custom Events, and Retries4.2.Azure Functions4.2.1.Triggers and Bindings for Serverless APIs4.2.2.Configuring and Deploying Function Apps4.3.Reflection CheckpointPhase 5: Secure, Monitor, and Troubleshoot Azure Solutions (20–25%)5.1.Implement Secure Azure Solutions5.1.1.Azure Key Vault: Secrets, Rotation, and Retrieval5.1.2.Azure App Configuration5.2.Monitor and Troubleshoot Azure Solutions5.2.1.Distributed Tracing with OpenTelemetry5.2.2.Analyzing Logs and Metrics with KQL5.3.Reflection CheckpointPhase 6: Exam Readiness & Strategy6.1.Exam Strategy6.2.Quick Reference6.3.Practice QuestionsPhase 7: Glossary7.1.Glossary of TermsPhase 8: Conclusion8.1.Summary and Next Steps
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7. Glossary

Related sections

  • 1Phase 1: First Principles of AI Cloud Development on Azure
  • 2Phase 2: Develop AI Solutions by Using Azure Data Management Services (25–30%)
  • 3Phase 3: Develop Containerized Solutions on Azure (20–25%)
  • 4Phase 4: Connect to and Consume Azure Services (20–25%)
  • 5Phase 5: Secure, Monitor, and Troubleshoot Azure Solutions (20–25%)
  • 6Phase 6: Exam Readiness & Strategy
Up next:8 Phase 8: Conclusion
Start with 40 free practice questions
Alvin Varughese
Written byAlvin Varughese
Founder•18 professional certifications
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