Examens: Develop AI cloud solutions on Azure (AI-200) (AI-200)

As a candidate for this Microsoft Certification, you’re responsible for contributing to all phases of implementing AI solutions on Azure, with an emphasis on back-end services and components. You’re also responsible for supporting all phases of the development lifecycle, including requirements gathering, design, development, deployment, security, and monitoring.

Assessed on this exam:

• Develop containerized solutions on Azure
• Develop AI solutions by using Azure data management services
• Connect to and consume Azure services
• Secure, monitor, troubleshoot Azure solutions

Develop containerized solutions on Azure (20–25%)

• 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

Develop AI solutions by using Azure data management services (25–30%)

• 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

Connect to and consume Azure services (20–25%)

• 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

Secure, monitor, and troubleshoot Azure solutions (20–25%)

• 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

You should be proficient in:

• Azure SDKs and third-party SDKs used in Azure.
• Azure data management services.
• Azure monitoring and troubleshooting.
• Azure messaging and eventing.
• Vector databases.
• Python programming.
• Implementing containerized applications on Azure.