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Microsoft AI-200 Exam Syllabus

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Before starting your AI-200 exam preparation, it is recommended to review the complete Microsoft Developing AI Cloud Solutions on Azure exam syllabus and carefully go through the exam objectives listed below. Once you understand the exam structure and objectives, you should practice using our free AI-200 questions. We also provide premium AI-200 practice test, fully updated according to the latest exam objectives, to help you accurately assess your preparedness for the actual exam.

Microsoft
Vendor
AI-200
Exam Code
50
Total Questions
4
Total Exam Domains

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AI-200 EXAM QUESTIONS

Microsoft AI-200 Exam Objectives

Section 1: Develop containerized solutions on Azure
Weight:
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
Section 2: Develop AI solutions by using Azure data management services
Weight:
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
Section 3: Connect to and consume Azure services
Weight:
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
Section 4: Secure, monitor, and troubleshoot Azure solutions
Weight:
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
Info
https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ai-200