GH-600 Latest Test Testking & Hot GH-600 Spot Questions

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Microsoft GH-600 Exam Syllabus Topics:

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

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                                                  Microsoft Developing in Agentic AI Systems Sample Questions (Q16-Q21):

                                                  NEW QUESTION # 16
                                                  You have a private GitHub repository that has Copilot memory enabled.
                                                  Several developers who have write access to the repository make changes across multiple branches, including creating some pull requests that are later closed without merging.
                                                  Your team needs to understand how GitHub Copilot ensures that only task-relevant, up-to-date information influences code suggestions, even when older memories exist.
                                                  How does Copilot manage memories?

                                                  Answer: D

                                                  Explanation:
                                                  Repository memory is supported by citations to the code that establishes the remembered fact. When a fact appears relevant to a new task, Copilot checks its supporting citations against the current branch before relying on it. This validation prevents an observation from an earlier repository state from being treated automatically as current truth.
                                                  The branch check is particularly important when work spans release branches, experimental changes, or pull requests that were never merged. A memory may have been accurate when created but no longer describe the code now being edited. If its supporting code is absent or no longer supports the claim, the remembered fact should not influence the task.
                                                  Option A incorrectly describes memory as permanently retained information requiring only manual intervention. Option B substitutes pull-request disposition for validation of the underlying evidence. Option C incorrectly treats repository knowledge as usable only by its original contributor.
                                                  The governing mechanism is evidence validation at the point of use, not an assumption that all previously stored information remains authoritative.
                                                  Relevant curriculum topics are task-relevant memory, stale-context prevention, and maintaining state consistency across branches and sessions.
                                                  Reference:


                                                  NEW QUESTION # 17
                                                  While using agent mode in VS Code, you want Copilot to run a specific test suite as a validation step after making changes, without manually invoking the terminal each time. What feature enables this?

                                                  Answer: B

                                                  Explanation:
                                                  Agent mode can be granted permission to invoke terminal commands (like running a test suite) as part of its autonomous workflow, allowing it to self-validate changes rather than requiring the developer to run tests manually each time.


                                                  NEW QUESTION # 18
                                                  You need to configure agent1 to support the planned changes.
                                                  What should you do?

                                                  Answer: B

                                                  Explanation:
                                                  The mcp-servers property connects a custom agent to the Model Context Protocol servers required for its planned work. MCP configuration makes defined external tools and services available to the agent in a controlled, declarative manner.
                                                  Adding a broad natural-language instruction such as "Use all available tools" does not configure a server connection, authenticate a tool, or define which external capabilities are available. It is also poor least-privilege design because it encourages unrestricted tool use without establishing a technical permission boundary.
                                                  Setting tools: [] removes all configured tools and would prevent the agent from using required capabilities. Deleting the tools line could cause the agent to inherit or expose a broader default tool set than intended. Repository-wide instructions can guide behavior, but they do not replace the agent-specific configuration needed to associate MCP servers with that agent.
                                                  A well-designed configuration identifies only the MCP servers necessary for the role, supplies secrets through approved environment mechanisms, and limits the resulting tools to the agent's defined task.
                                                  Study-guide topics: MCP integration, agent tool configuration, and least-privilege environment access.


                                                  NEW QUESTION # 19
                                                  You have a GitHub repository that uses a GitHub Actions workflow to run an agent-driven change plan as part of a CI pipeline. The workflow generates an artifact named plan.json that includes a field named risk. risk has possible values of low, medium, or high.
                                                  You need to ensure that a human must confirm the execution of the workflow when risk is medium or high. The workflow must proceed automatically only when risk is low.
                                                  How should you complete the workflow? To answer, drag the appropriate values to the correct targets. Each value 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 # 20
                                                  You have a GitHub repository that uses a custom GitHub Copilot coding agent defined in the rollout-bot.agent.md file.
                                                  You need to update a workflow so that agent-profile changes can be rolled back by reverting a single commit and rerunning the workflow. The workflow must check out the exact commit being deployed and apply the agent profile from the repository at that commit.
                                                  How should you complete the workflow? To answer, drag the appropriate values to the correct targets. Each value 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 # 21
                                                  ......

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