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Microsoft AI-500 Exam Syllabus Topics:

SectionWeightObjectives
Evaluate, optimize, and monitor multi-agent solutions20–25%- Implement observability
  • 1. Use Azure-native monitoring tools
    • 2. Monitor agent interactions and outcomes
      • 3. Enable logging and tracing
        - Assess performance and reliability
        • 1. Define and measure success metrics
          • 2. Diagnose failures and bottlenecks
            • 3. Optimize latency and scalability
              Secure, govern, and deploy multi-agent solutions20–25%- Apply security and compliance
              • 1. Configure authentication and access control
                • 2. Enforce data protection and privacy
                  • 3. Manage governance and audit requirements
                    - Deploy and maintain solutions
                    • 1. Manage lifecycle and retirement
                      • 2. Deploy agents to production environments
                        • 3. Implement versioning and update strategies
                          Develop multi-agent solutions in Azure30–35%- Implement agents using Azure AI services
                          • 1. Orchestrate workflows with Azure AI Foundry
                            • 2. Integrate tools, plugins, and APIs
                              • 3. Build agents with Azure AI Agent Service
                                - Manage state and memory
                                • 1. Use frameworks like Microsoft Agent Framework and MCP
                                  • 2. Configure short-term and long-term memory
                                    • 3. Handle multi-turn conversations
                                      Architect multi-agent solutions15–20%- Design workflow and tool integration
                                      • 1. Incorporate human-in-the-loop oversight
                                        • 2. Apply responsible AI principles
                                          • 3. Plan tool ecosystems and permissions
                                            - Design logical architecture for multi-agent systems
                                            • 1. Design agent communication and handoff protocols
                                              • 2. Define agent patterns and roles
                                                • 3. Specify autonomy levels and guardrails

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