2026 GH-600–100% Free New Exam Guide | Professional Exam GH-600 Details

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Microsoft GH-600 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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. Implement logging, telemetry, and observability
          • 2. Optimize cost, latency, and throughput
            • 3. Deploy to Azure AI and cloud environments
              Topic 2: 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. Implement agent logic and reasoning
                      • 2. Integrate models and prompts
                        • 3. Develop using Semantic Kernel and Azure AI Foundry
                          Topic 3: Design agentic AI solutions25%- Design agent architecture
                          • 1. Plan tool integration and orchestration
                            • 2. Select agent patterns and topologies
                              • 3. Design memory and state management
                                - 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
                                      Topic 4: Integrate tools, data, and services25%- Incorporate external tools and APIs
                                      • 1. Handle authentication and error resilience
                                        • 2. Implement function calling and service integration
                                          • 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

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                                                  Microsoft Developing in Agentic AI Systems Sample Questions (Q89-Q94):

                                                  NEW QUESTION # 89
                                                  A team assigns an issue to the GitHub Copilot coding agent by using the following one-line description: Fix the login bug.
                                                  Copilot creates a pull request, but the pull request is missing changes and has an incorrect scope.
                                                  How should you resolve the issue?

                                                  Answer: C

                                                  Explanation:
                                                  The primary failure is insufficient task definition. "Fix the login bug" does not identify the observed behavior, expected behavior, reproduction conditions, affected component, or completion criteria. The agent must infer these details, creating a substantial risk of incomplete changes or work outside the intended scope. A clear issue description supplies the information needed to construct and implement an appropriate plan.
                                                  For this scenario, the description should identify how the login failure occurs, which authentication path is affected, what successful behavior looks like, and which existing behavior must remain intact. Relevant error messages, reproduction steps, and expected tests make the task independently verifiable. These details turn an ambiguous request into a bounded engineering assignment.
                                                  Enabling memory does not supply missing requirements reliably. MCP rate limits govern service interaction rather than task clarity. Additional setup resources address environmental capacity or dependency preparation, not uncertainty about what must change.
                                                  The corrective action therefore belongs at task intake, where developers define the agent's inputs and success conditions. The relevant curriculum topics are defining agent inputs, outputs, and success criteria and mitigating poorly scoped task assignments.
                                                  Reference:


                                                  NEW QUESTION # 90
                                                  You have a GitHub repository that uses the following GitHub Copilot CLI command in a Bash script.
                                                  #!/usr/bin/env bash
                                                  set -euo pipefail
                                                  PROMPT="Update dependencies, run tests, and open a pull request if changes are needed." copilot --autopilot --yolo --max-autopilot-continues 10 -p "$PROMPT" For each of the following statements, select Yes if the statement is true. Otherwise, select No.
                                                  NOTE: Each correct selection is worth one point.

                                                  Answer:

                                                  Explanation:


                                                  NEW QUESTION # 91
                                                  You have multiple GitHub Copilot coding agents that run tasks concurrently.
                                                  You are monitoring the agents from the terminal by using the GitHub CLI.
                                                  An agent appears stalled.
                                                  You need to live stream the session log output.
                                                  What should you do?

                                                  Answer: B

                                                  Explanation:
                                                  The gh agent-task view command supports inspecting an individual agent task. Combining --log with --follow selects session log output and continues following that output as the session progresses. The official CLI reference describes these flags as "Show agent session logs" and "Follow agent session logs," respectively.
                                                  This combination is appropriate when an agent appears stalled because a task-level status alone cannot explain what the agent is currently doing. Following the log allows the operator to observe additional activity, identify the last reported operation, and distinguish ongoing execution from a task awaiting intervention.
                                                  The list command is useful for discovering tasks and obtaining an overview of concurrent activity. It does not replace following the detailed output of the relevant session. The --jq parameter filters structured JSON output; it does not provide the continuous log-following behavior requested here. Opening a browser with a web option also fails the terminal streaming requirement.
                                                  Viewing logs is an observational action. It does not itself restart, cancel, or unblock the agent.
                                                  Study-guide topics: monitoring concurrent agents, execution visibility, and troubleshooting. Reference: GitHub CLI-gh agent-task view.


                                                  NEW QUESTION # 92
                                                  You are running the GitHub Copilot CLI and want the agent to execute file edits and shell commands without prompting for confirmation on each individual action, during a trusted throwaway sandbox session. Which flag/command should you use?

                                                  Answer: A

                                                  Explanation:
                                                  The --allow-all (commonly nicknamed "YOLO mode") flag disables per-action confirmation prompts, letting the agent run autonomously. This should only be used in isolated, low-risk environments since it removes a key safety checkpoint.


                                                  NEW QUESTION # 93
                                                  You have a GitHub repository that uses GitHub Actions to run an autonomous coding-agent workflow.
                                                  Your team requires that session logs be captured as workflow artifacts so that agent runs can be audited and traced back to a specific execution and code state.
                                                  How should you complete the workflow? To answer, drag the appropriate values to the correct targets.

                                                  Answer:

                                                  Explanation:


                                                  NEW QUESTION # 94
                                                  ......

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