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Microsoft GH-600 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Implement agents and multi-agent systems30%- Orchestrate multi-agent collaboration
  • 1. Define communication protocols between agents
    • 2. Manage agent handoffs and task distribution
      • 3. Implement workflows and coordination strategies
        - Build agents with Azure AI tools and frameworks
        • 1. Integrate models and prompts
          • 2. Develop using Semantic Kernel and Azure AI Foundry
            • 3. Implement agent logic and reasoning
              Topic 2: Integrate tools, data, and services25%- Incorporate external tools and APIs
              • 1. Design and register tool definitions
                • 2. Handle authentication and error resilience
                  • 3. Implement function calling and service integration
                    - Connect data sources and knowledge bases
                    • 1. Implement retrieval-augmented generation (RAG)
                      • 2. Integrate vector databases and search
                        • 3. Ensure data security and access control
                          Topic 3: Test, deploy, and monitor agentic AI systems20%- Validate agent performance and safety
                          • 1. Apply guardrails and content safety
                            • 2. Test reasoning accuracy and consistency
                              • 3. Evaluate quality metrics and iterate
                                - Deploy and monitor agents at scale
                                • 1. Deploy to Azure AI and cloud environments
                                  • 2. Optimize cost, latency, and throughput
                                    • 3. Implement logging, telemetry, and observability
                                      Topic 4: Design agentic AI solutions25%- Design agent architecture
                                      • 1. Design memory and state management
                                        • 2. Plan tool integration and orchestration
                                          • 3. Select agent patterns and topologies
                                            - Define requirements for agentic systems
                                            • 1. Define functional and non-functional requirements
                                              • 2. Identify use cases and scenarios
                                                • 3. Plan for responsible AI and governance

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                                                  Microsoft Developing in Agentic AI Systems Sample Questions (Q51-Q56):

                                                  NEW QUESTION # 51
                                                  You assigned an issue to the Copilot coding agent, and it opened a pull request. You want to inspect exactly what code changes were made before merging. Which CLI slash command lets you view the change set directly in the terminal?

                                                  Answer: C

                                                  Explanation:
                                                  The correct command is /diff. GitHub Copilot CLI provides /diff specifically for reviewing code changes directly from the terminal. GitHub's current CLI command reference defines /diff as a command that reviews changes in the current directory and can automatically switch to a branch comparison when the working tree is clean. This makes it the appropriate command when validating an agent-generated change set before accepting or merging the work.
                                                  The other commands serve different purposes. /context displays context-window utilization, including token allocation and remaining context capacity; it does not show code modifications. /compact summarizes conversation history to reclaim context-window space during long-running sessions. /plan creates an implementation plan before coding and restricts normal project-file modifications while the plan is being prepared.
                                                  Using /diff supports an essential human-in-the-loop control in agentic software development: generated code should be inspected before it crosses a merge or deployment boundary. Reviewing the diff exposes additions, deletions, and modifications so that unintended or unsafe changes can be identified before integration.
                                                  Study Guide Reference Topics: Implement Tool Use and Environment Interaction; agent-generated code review; human-in-the-loop validation; controlled execution and change inspection.


                                                  NEW QUESTION # 52
                                                  You have a GitHub Actions workflow that runs two jobs named agent-plan and agent-implement on pull requests.
                                                  The agent-plan job generates an implementation plan that downstream jobs must consume, but the job currently writes the plan only to the console.
                                                  You need to capture the implementation plan as a step output and configure the workflow so that downstream jobs can reference the output.
                                                  How should you configure the workflow? To answer, select the appropriate options in the answer area.
                                                  NOTE: Each correct selection is worth one point.

                                                  Answer:

                                                  Explanation:


                                                  NEW QUESTION # 53
                                                  You need to enable Copilot memory to support the planned changes.
                                                  What should you configure?

                                                  Answer: A


                                                  NEW QUESTION # 54
                                                  You need finer control, selecting specific files and describing precise natural-language changes to apply, rather than letting the agent decide the full scope of changes. Which Copilot Chat mode should you use?

                                                  Answer: B

                                                  Explanation:
                                                  Edit mode is designed for targeted, developer-directed changes: you select the files in scope and describe the specific modification, giving you more granular control than the fully autonomous agent mode.


                                                  NEW QUESTION # 55
                                                  You have a GitHub repository that uses the GitHub Copilot coding agent to resolve issues and create draft pull requests. The repository uses GitHub Actions for CI, and reviewers rely on pull request timelines and workflow artifacts to understand what the agent did.
                                                  During long-running agent tasks, the reviewers lose track of decisions and validation steps, which causes repeated questions and reworks when context drifts between iterations.
                                                  You need to persist task progress and decisions as durable artifacts and ensure that the reviewers can verify what the agent did during and after execution by using GitHub as the system of record.
                                                  What should you do for each requirement? To answer, drag the appropriate actions to the correct requirements. Each action may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
                                                  NOTE: Each correct selection is worth one point.

                                                  Answer:

                                                  Explanation:


                                                  NEW QUESTION # 56
                                                  ......

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