AI-200 practice questions
376 questions mapped to the 27 objectives in the official skills outline. Every answer comes with an explanation of why each option is right or wrong.
Want a full run instead? Take a mock exam.
Develop containerized solutions on Azure
20–25% of the exam- Build, store, version, and manage container images by using Azure Container Registry11 questionsNot started
- Build and run images by using Azure Container Registry Tasks10 questionsNot started
- Deploy containers to Azure App Service, including configuring App Service to supply environment variables and secrets11 questionsNot started
- Deploy applications to Azure Container Apps, including environment configuration and revision management13 questionsNot started
- Implement event-driven scaling by using Kubernetes Event‑driven Autoscaling (KEDA) in Container Apps11 questionsNot started
- Deploy and manage applications to Azure Kubernetes Service (AKS) by using manifest files13 questionsNot started
- Monitor and troubleshoot solutions on AKS and Container Apps by inspecting logs, events, and end-to-end connectivity12 questionsNot started
Develop AI solutions by using Azure data management services
30–35% of the exam- Connect to Azure Cosmos DB for NoSQL by using the SDK and run queries11 questionsNot started
- Optimize query performance and Request Units (RUs) consumption by using indexing policies and consistency levels11 questionsNot started
- Store and retrieve embeddings and execute vector similarity search for semantic retrieval13 questionsNot started
- Implement a change feed processor to detect and handle new or updated items11 questionsNot started
- Connect and query Azure Database for PostgreSQL by using SDKs10 questionsNot started
- Model schemas and implement indexing strategies, including designing tables and choosing appropriate data types11 questionsNot started
- Implement indexing strategies, including optimizing query latency and reducing pgvector compute overhead11 questionsNot started
- Configure compute, memory, and storage resources to support vector workloads11 questionsNot started
- Run vector similarity search, including storing embeddings, semantic retrieval, and implementing retrieval-augmented generation (RAG) patterns by using metadata filter11 questionsNot started
- Implement connection optimization to improve throughput and minimize latency11 questionsNot started
- Implement Azure Managed Redis data operations, including caching, expiration, and invalidation11 questionsNot started
- Implement vector indexing to enable similarity search10 questionsNot started
Connect to and consume Azure services
20–25% of the exam- Queue and process back-end operations by using Azure Service Bus, including dead-letter queue handling, messages, topics, and subscriptions19 questionsNot started
- Implement event-driven workflows by using Azure Event Grid, including filters, custom events, and retries13 questionsNot started
- Build serverless APIs, including implementing triggers and bindings12 questionsNot started
- Configure and deploy function apps11 questionsNot started
Secure, monitor, and troubleshoot Azure solutions
20–25% of the exam- Secure secrets by using Azure Key Vault, including rotation and retrieval21 questionsNot started
- Store and retrieve app configuration information by using Azure App Configuration10 questionsNot started
- Trace distributed systems by using OpenTelemetry SDKs12 questionsNot started
- Write KQL queries to analyze logs and metrics12 questionsNot started