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Microsoft GH-600 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Implement agents and multi-agent systems30%- Build agents with Azure AI tools and frameworks
  • 1. Develop using Semantic Kernel and Azure AI Foundry
    • 2. Implement agent logic and reasoning
      • 3. Integrate models and prompts
        - Orchestrate multi-agent collaboration
        • 1. Define communication protocols between agents
          • 2. Implement workflows and coordination strategies
            • 3. Manage agent handoffs and task distribution
              Topic 2: Test, deploy, and monitor agentic AI systems20%- 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
                    - Validate agent performance and safety
                    • 1. Test reasoning accuracy and consistency
                      • 2. Evaluate quality metrics and iterate
                        • 3. Apply guardrails and content safety
                          Topic 3: 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. Ensure data security and access control
                                  • 2. Implement retrieval-augmented generation (RAG)
                                    • 3. Integrate vector databases and search
                                      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. Plan for responsible AI and governance
                                              • 2. Identify use cases and scenarios
                                                • 3. Define functional and non-functional requirements

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                                                  Microsoft Developing in Agentic AI Systems Sample Questions (Q90-Q95):

                                                  NEW QUESTION # 90
                                                  You want to grant the Copilot coding agent access to only a narrowly scoped set of repository permissions (e.g., read code, write pull requests) rather than full admin access. What governs this scope?

                                                  Answer: A

                                                  Explanation:
                                                  The correct answer is the GITHUB_TOKEN permission configuration. GitHub uses the automatically generated GITHUB_TOKEN to authenticate workflow operations against the repository, and its authorization scope can be reduced through repository defaults and the workflow permissions key. Permissions can be set globally for a workflow or narrowed further at the individual job level. GitHub recommends granting only the minimum access necessary for the task.
                                                  For example, a workflow that only needs repository content access and pull-request modification can explicitly grant appropriate contents and pull-requests permissions rather than broad write access. Importantly, once individual permissions are specified, unspecified permission categories are set to none, providing a concrete least-privilege security boundary. Repository or organization policy can also establish restrictive default GITHUB_TOKEN permissions.
                                                  .copilotignore concerns which content Copilot should exclude, while copilot-instructions.md provides behavioral and coding guidance; neither is an authorization mechanism. /context reports information about the Copilot CLI context and does not control repository privileges.
                                                  Study Guide Reference Topics: Implement Guardrails and Accountability; least-privilege authorization; tool and execution permissions; repository security boundaries; controlled agent access.


                                                  NEW QUESTION # 91
                                                  You have multiple GitHub Copilot coding agents that run tasks concurrently.
                                                  You live stream the session log output and see the following.

                                                  What is a possible cause of the error?

                                                  Answer: B

                                                  Explanation:
                                                  In GitHub Copilot Extensions and Agent frameworks (such as the GitHub Copilot Chat architecture), preToolUse hooks are explicitly designed to intercept, evaluate, and validate a tool's arguments before the agent executes the underlying command or function. Because the agent attempted to execute rm -rf /infra and was immediately blocked with a specific destructive_operation_detected reason, a preToolUse lifecycle hook intercepted the function call, identified the risk, and rejected the execution before the system could actually run the command.
                                                  Reference:
                                                  https://docs.github.com/en/copilot/how-tos/copilot-on-github/use-copilot-agents/manage-and-track-agents


                                                  NEW QUESTION # 92
                                                  You have a GitHub Enterprise Cloud repository that uses GitHub Actions for CI and requires pull requests for all changes.
                                                  You are planning a GitHub Actions workflow where a coding agent drafts implementation changes and tests from GitHub issues, and an automated review runs before human review.
                                                  You need the agent to draft changes from an assigned issue, open a pull request, and add an automated review to the pull request before requesting a human review.
                                                  What should you do? To answer, select the appropriate options in the answer area.
                                                  NOTE: Each correct selection is worth one point.

                                                  Answer:

                                                  Explanation:


                                                  NEW QUESTION # 93
                                                  Drag and Drop Question
                                                  You have a GitHub Enterprise Cloud Organization that uses the GitHub Copilot coding agent to resolve issues asynchronously.
                                                  When an issue is assigned to GitHub Copilot, the agent creates a draft pull request, but your team cannot always tell whether the agent is actively working, has completed its session, or is awaiting workflow approval.
                                                  Which execution context does each signal indicate? To answer, drag the appropriate context to the correct signals. Each signal 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:

                                                  Explanation:
                                                  Box 1: The agent acknowledges the assignment and will create the draft pull request.
                                                  When an issue is assigned to the GitHub Copilot coding agent, the eyes emoji reaction indicates that the agent has acknowledged the task and is actively starting work in the background.
                                                  Box 2: The agent session is actively running and generating live logs.
                                                  The signal indicating that "the pull request timeline shows Copilot started work" means that the agent session is actively running and generating live logs.
                                                  When a pull request timeline shows that Copilot started work, it indicates that the execution context is actively working.
                                                  Actively working: Indicated when the pull request timeline explicitly logs that Copilot started work or updates the PR body with a list of in-progress sub-tasks.
                                                  Box 3: A human must manually approve and run the workflow.
                                                  When a draft pull request exists but GitHub Actions checks are not running, it indicates that the execution context is awaiting workflow approval.
                                                  Reference:
                                                  https://docs.github.com/en/copilot/how-tos/copilot-on-github/use-copilot-agents/kick-off-a-task


                                                  NEW QUESTION # 94
                                                  You have a GitHub Copilot coding agent that has completed a pull request for a security fix in your repository.
                                                  Before merging, you need to evaluate the quality of the agent's work by using both automated evaluation signals and human review.
                                                  You review the session log and the pull request.
                                                  What are two automated evaluation signals generated by the coding agent's built-in scanning tools? Each correct answer presents a complete solution.
                                                  NOTE: Each correct selection is worth one point.

                                                  Answer: C,D

                                                  Explanation:
                                                  The two automated evaluation signals generated by the coding agent's built-in scanning tools are:
                                                  CodeQL findings that identify security vulnerabilities in the generated code.
                                                  The detection of hardcoded secrets, such as API keys and tokens.
                                                  Reference:
                                                  https://itacademy.com.ua/en/articles/2026-06-11/security-validation-third-party-coding-agents-github/


                                                  NEW QUESTION # 95
                                                  ......

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