Free Practice Questions for Microsoft Certified: Azure AI Cloud Developer Associate (AI-200) Certification
Study with 348 exam-style practice questions designed to help you prepare for the Microsoft Certified: Azure AI Cloud Developer Associate (AI-200).
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Key information about Microsoft Certified: Azure AI Cloud Developer Associate (AI-200)
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Proficiency in Azure SDKs and third-party SDKs, Azure data management services, Azure monitoring and troubleshooting, Azure messaging and eventing, vector databases, Python programming, and implementing containerized applications on Azure.
Candidates responsible for contributing to all phases of implementing AI solutions on Azure, with an emphasis on back-end services and components.
Exam Topics & Skills Assessed
Skills measured (from the official study guide)
Domain 1: Develop containerized solutions on Azure
Subdomain 1.1: 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
Subdomain 1.2: 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
Domain 2: Develop AI solutions by using Azure data management services
Subdomain 2.1: 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
Subdomain 2.2: 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
Subdomain 2.3: 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
Domain 3: Connect to and consume Azure services
Subdomain 3.1: 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
Subdomain 3.2: Develop and implement Azure Functions
Build serverless APIs, including implementing triggers and bindings Configure and deploy function apps
Domain 4: Secure, monitor, and troubleshoot Azure solutions
Subdomain 4.1: 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
Subdomain 4.2: Monitor and troubleshoot Azure solutions
Trace distributed systems by using OpenTelemetry SDKs Write KQL queries to analyze logs and metrics
Techniques & products