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Microsoft GH-600 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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. Evaluate quality metrics and iterate
          • 2. Apply guardrails and content safety
            • 3. Test reasoning accuracy and consistency
              Topic 2: Integrate tools, data, and services25%- Connect data sources and knowledge bases
              • 1. Integrate vector databases and search
                • 2. Ensure data security and access control
                  • 3. Implement retrieval-augmented generation (RAG)
                    - Incorporate external tools and APIs
                    • 1. Handle authentication and error resilience
                      • 2. Implement function calling and service integration
                        • 3. Design and register tool definitions
                          Topic 3: 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. Implement workflows and coordination strategies
                                  • 2. Define communication protocols between agents
                                    • 3. Manage agent handoffs and task distribution
                                      Topic 4: Design agentic AI solutions25%- Define requirements for agentic systems
                                      • 1. Identify use cases and scenarios
                                        • 2. Define functional and non-functional requirements
                                          • 3. Plan for responsible AI and governance
                                            - Design agent architecture
                                            • 1. Select agent patterns and topologies
                                              • 2. Plan tool integration and orchestration
                                                • 3. Design memory and state management

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                                                  2026 Updated Exam GH-600 Cram Questions Help You Pass GH-600 Easily

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                                                  Microsoft Developing in Agentic AI Systems Sample Questions (Q76-Q81):

                                                  NEW QUESTION # 76
                                                  You are architecting an agentic AI system and need the agent's tool-calling behavior to be constrained so it can only call a specific allow-listed set of MCP tools, never arbitrary ones. What should you configure?

                                                  Answer: D

                                                  Explanation:
                                                  MCP client configurations (e.g., in VS Code settings or the CLI config) allow administrators to explicitly allow-list which MCP servers/tools an agent may invoke, restricting the attack surface and preventing unintended tool use.


                                                  NEW QUESTION # 77
                                                  During a Copilot CLI session, an MCP tool call fails because the external service requires re- authentication. What is the most likely resolution path?

                                                  Answer: B

                                                  Explanation:
                                                  MCP tool failures due to expired or invalid credentials require re-authenticating the connector itself; conversation-management commands like /compact or /diff don't address authentication issues.


                                                  NEW QUESTION # 78
                                                  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 security scanning signals available for the pull request? Each correct answer presents a complete solution.
                                                  NOTE: Each correct selection is worth one point.

                                                  Answer: A,B

                                                  Explanation:
                                                  CodeQL findings and secret-detection results are the intended selections. CodeQL analyzes code for patterns associated with vulnerabilities and programming errors. Secret scanning identifies sensitive material, including supported credential and token patterns, that may have been introduced into the generated changes. Both provide concrete security evidence that a reviewer can evaluate alongside the proposed fix.
                                                  Linting generally evaluates coding conventions and selected correctness rules; a generic linting failure is not necessarily a security finding. Median time to merge measures delivery performance rather than the security of a particular change.
                                                  The wording of option D creates an important technical distinction. Dependency vulnerability detection is also a genuine automated security signal. GitHub explicitly documents dependency analysis alongside CodeQL and secret scanning in the coding agent's security checks. Repository-level dependency alerts, however, may concern existing vulnerabilities rather than changes introduced by this pull request. B and C are the clearest intended pair, but D cannot be dismissed as unrelated to security.
                                                  Reviewers should determine whether each finding applies to the proposed changes and whether the underlying vulnerability has actually been resolved.
                                                  Relevant curriculum topics are automated evaluation signals, security scanning, and evidence-based assessment of agent outputs.
                                                  Reference:


                                                  NEW QUESTION # 79
                                                  You have a repository on github.com that uses the GitHub Copilot coding agent.
                                                  You also use the GitHub Copilot CLI locally to reproduce failures and continue the same work from your terminal.
                                                  You need to verify the current token usage.
                                                  Which Copilot CLI slash command should you run?

                                                  Answer: A

                                                  Explanation:
                                                  The correct answer is /context when the requirement is to verify the current token usage of the active context window. GitHub documents /context as the command that displays the active model and the number of tokens currently in use relative to the model's total context capacity. It also provides a breakdown across the system prompt, custom instructions, built-in tools, MCP tools, conversation messages, free space, and reserved buffer.
                                                  This is distinct from /usage. /usage reports broader session statistics, including AI Credits consumed, session duration, lines edited, and per-model token totals. Therefore, /usage is appropriate when reviewing cumulative session consumption, but /context is the more precise answer to a question asking for the current token usage of the working context.
                                                  /compact reduces context pressure by summarizing earlier conversation history; it does not merely report the current token state. /diff reviews code changes and is unrelated to context or token accounting.
                                                  Study Guide Reference Topics: Manage Memory, State, and Execution; context-window monitoring; token-budget management; maintaining effective long-running agent sessions.


                                                  NEW QUESTION # 80
                                                  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 # 81
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