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Microsoft AI-500 Exam Syllabus Topics:

SectionWeightObjectives
Architect multi-agent solutions15-20%- Design logical architecture for multi-agent solutions
  • 1. Specify observability components including tracing, structured logging, and replay
    • 2. Select developer tools and environments for the software development lifecycle
      • 3. Specify monitoring components for coordination, drift detection, and remediation
        • 4. Design workflows with agents, subagents, control loops, and human-in-the-loop
          • 5. Decompose goals into workflows, agents, and tools
            • 6. Specify compute components for scalability, reliability, security, and cost optimization
              Develop multi-agent solutions in Azure30-35%- Design and implement agent memory, context management, and knowledge integration
              • 1. Design multi-agent RAG architectures
                • 2. Implement memory strategies
                  • 3. Implement knowledge integration using search, MCP, and semantic search
                    • 4. Implement context management across agents
                      - Implement multi-agent orchestration
                      • 1. Monitor availability, performance, and SLA compliance
                        • 2. Use Microsoft Agent Framework, LangChain, LangGraph, and Hugging Face Transformers
                          • 3. Design caching strategies
                            • 4. Implement orchestration patterns
                              • 5. Integrate agents using Agent2Agent and MCP
                                • 6. Implement tracing with Microsoft Foundry
                                  • 7. Implement scalable concurrent execution
                                    • 8. Implement human-in-the-loop processes
                                      • 9. Design reusable middleware
                                        • 10. Optimize token usage and cost management
                                          - Build and integrate tool ecosystems
                                          • 1. Implement tool validation, error handling, and fallback mechanisms
                                            • 2. Design and build MCP servers and clients
                                              • 3. Integrate external tools and function calling
                                                - Design and implement advanced prompt engineering strategies
                                                • 1. Design context-aware agent behaviors
                                                  • 2. Implement advanced prompting techniques
                                                    • 3. Implement agent and model fine-tuning strategies
                                                      Secure, govern, and deploy multi-agent solutions20-25%- Design and implement guardrails
                                                      • 1. Design custom guardrails
                                                        • 2. Validate guardrails with testing and synthetic data
                                                          • 3. Implement guardrails for inputs, tools, and outputs
                                                            - Design and implement security
                                                            • 1. Apply shift-left security practices
                                                              • 2. Implement authentication and authorization
                                                                • 3. Implement identity, RBAC, and network security
                                                                  • 4. Manage secrets with Azure Key Vault
                                                                    - Deploy multi-agent solutions to Azure
                                                                    • 1. Implement CI/CD and infrastructure as code
                                                                      • 2. Implement multi-environment release strategies
                                                                        • 3. Choose deployment and release methodologies
                                                                          • 4. Design testing strategies
                                                                            Evaluate, optimize, and monitor multi-agent solutions20-25%- Optimize operational performance
                                                                            • 1. Monitor production workloads and operational health
                                                                              • 2. Optimize latency, throughput, and token consumption
                                                                                - Evaluate solution quality
                                                                                • 1. Evaluate workflows and orchestration
                                                                                  • 2. Measure agent performance and quality

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