Microsoft AI-200 Exam Information and Actual Questions

  • Exam Code/Number: AI-200
  • Exam Name/Title: Developing AI Cloud Solutions on Azure
  • Certification Provider: Microsoft
  • Corresponding Certification: Azure AI Engineer Associate
  • Exam Questions: 93
  • Updated On: Jul 24, 2026

AI-200
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Microsoft
AI-200 Exam
Developing AI Cloud Solutions on Azure

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Microsoft AI-200 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Developing AI Cloud Solutions on Azure
Exam Number:AI-200
Related Certifications:Microsoft Certified: Azure Developer Associate (retiring July 31, 2026)
Passing Score:700 (out of 1000)
Available Languages:English, Japanese, Chinese (Simplified), Korean, German, French, Spanish, Portuguese (Brazil)
Real Exam Qty:40–60
Exam Price:$165 USD
Exam Duration:100 minutes
Certificate Validity Period:1 year, renewable free via online assessment
Exam Format:Multiple choice, Scenario-based questions, Case studies
Recommended Training:AI-200T00: Developing AI Cloud Solutions on Azure
Exam Registration:Microsoft Certification Registration
Pearson VUE Scheduling
Sample Questions:Microsoft AI-200 Sample Questions
Exam Way:Online proctored or onsite at Pearson VUE test centers
Pre Condition:Basic programming experience (Python, .NET, JavaScript, or similar); familiarity with Azure development, containers, and cloud architectures
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ai-200

Microsoft AI-200 Exam Syllabus Topics:

SectionWeightObjectives
Integrate backend services and build event-driven architectures25%- Build serverless APIs and workflows
  • 1. Azure Functions for AI integration and processing
  • 2. Orchestrate AI pipelines and workflows
- Implement messaging and event systems
  • 1. Connect services and expose APIs securely
  • 2. Azure Event Grid for event-driven processing
  • 3. Azure Service Bus for reliable messaging
Develop containerized AI solutions on Azure25%- Implement container hosting environments
  • 1. Configure scaling, networking, and security for containers
  • 2. Azure Container Registry: store, version, manage images
  • 3. Deploy to Azure Container Apps and Azure Kubernetes Service (AKS)
- Monitor and troubleshoot containerized workloads
  • 1. Log analysis, health checks, and performance monitoring
  • 2. Manage configurations and secrets for containers
Develop AI solutions using Azure data services30%- Design and optimize data access and retrieval
  • 1. Indexing strategies, query optimization, and consistency models
  • 2. Implement hybrid search and retrieval patterns
- Implement vector-enabled databases
  • 1. Azure Database for PostgreSQL with pgvector extension
  • 2. Azure Cosmos DB for NoSQL with vector search
  • 3. Azure Managed Redis for caching, streaming, and vector storage
Secure, monitor, and optimize AI solutions20%- Manage security and configuration
  • 1. Managed identities and access control
  • 2. Azure Key Vault for secrets, keys, and certificates
  • 3. App Configuration for dynamic settings
- Implement observability and reliability
  • 1. Optimize performance, cost, and scalability
  • 2. OpenTelemetry and Azure Monitor integration
  • 3. Logging, metrics, and distributed tracing


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