Microsoft GH-600 dumps - in .pdf

GH-600 pdf
  • Exam Code: GH-600
  • Exam Name: GitHub Agentic AI Developer
  • Updated: Aug 23, 2026
  • Q & A: 85 Questions and Answers
  • PDF Price: $59.99

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  • Exam Code: GH-600
  • Exam Name: GitHub Agentic AI Developer
  • Updated: Aug 23, 2026
  • Q & A: 85 Questions and Answers
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Microsoft GH-600 dumps - Testing Engine

GH-600 Testing Engine
  • Exam Code: GH-600
  • Exam Name: GitHub Agentic AI Developer
  • Updated: Aug 23, 2026
  • Q & A: 85 Questions and Answers
  • Software Price: $59.99
  • Testing Engine

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Microsoft GH-600 Exam Syllabus Topics:

SectionWeightObjectives
Evaluation, error analysis, and tuning15–20%- Tuning agent behavior
  • 1. Optimize memory usage and constraints
    • 2. Refine prompts, tools, and workflows
      - Define evaluation criteria
      • 1. Define success metrics and constraints
        • 2. Generate automated evaluation signals
          - Failure analysis
          • 1. Analyze logs, traces, and artifacts
            • 2. Classify reasoning, tool, and context errors
              Implement guardrails and accountability10–15%- Guardrails and human-in-the-loop
              • 1. Enforce least-privilege execution
                • 2. Require approvals for sensitive actions
                  - Autonomy and risk levels
                  • 1. Classify agent actions by risk
                    • 2. Assign autonomy levels with compliance constraints
                      Implement tool use and environment interaction20–25%- Safe execution and error handling
                      • 1. Escalation paths and traceability
                        • 2. Retries and rollback strategies
                          - Agent tool configuration
                          • 1. Select and configure tools
                            • 2. Configure tool permissions and scope
                              - Development environment integration
                              • 1. Enable CI-based agent execution
                                • 2. Enable autonomous actions (PRs, branches)
                                  • 3. Scope agents to repositories or branches
                                    - MCP server configuration
                                    • 1. Add MCP servers to agents
                                      • 2. Configure registries and allow lists
                                        Prepare agent architecture and SDLC processes15–20%- Integrate agents into SDLC workflows
                                        • 1. Identify and mitigate agent anti-patterns
                                          • 2. Define agent steps in SDLC
                                            • 3. Define inputs, outputs, and success criteria
                                              - Observability and control
                                              • 1. Produce inspectable artifacts in GitHub
                                                • 2. Define autonomy levels and guardrails
                                                  • 3. Enable human-in-the-loop controls
                                                    - Planning vs execution boundaries
                                                    • 1. Separate planning and execution phases
                                                      • 2. Prevent execution before approval
                                                        • 3. Validate structured agent plans
                                                          Orchestrate multi-agent coordination15–20%- Failure handling and recovery
                                                          • 1. Implement rollback and recovery patterns
                                                            • 2. Detect stalled or degraded agents
                                                              - Multi-agent workflows
                                                              • 1. Coordinate parallel agent execution
                                                                • 2. Resolve conflicts and overlaps
                                                                  - Observability and auditability
                                                                  • 1. Generate logs and artifacts for review
                                                                    • 2. Document agent handoffs and decisions
                                                                      - Lifecycle management
                                                                      • 1. Add/replace/retire agents safely
                                                                        Manage memory, state, and execution10–15%- Cross-tool continuity
                                                                        • 1. Share state across tools and environments
                                                                          • 2. Prevent stale or conflicting context
                                                                            - Agent memory strategies
                                                                            • 1. Memory scoping and expiration rules
                                                                              • 2. Short-term vs long-term memory selection
                                                                                - State persistence and drift control
                                                                                • 1. Detect and correct context drift
                                                                                  • 2. Persist task progress as artifacts

                                                                                    Microsoft GitHub Agentic AI Developer Sample Questions:

                                                                                    1. You want the Copilot coding agent to scan a large codebase and propose a full, human- reviewable step-by-step plan before writing any code. What should you do first?

                                                                                    A) Configure an MCP server
                                                                                    B) Switch to plan mode
                                                                                    C) Enable --allow-all
                                                                                    D) Run /compact


                                                                                    2. Your team wants Copilot's suggestions to reflect knowledge of internal library APIs that are not publicly documented and not present in the codebase being edited. What is the most appropriate solution?

                                                                                    A) Add the internal docs as copilot-instructions.md
                                                                                    B) Connect an MCP server that exposes the internal documentation
                                                                                    C) Use .copilotignore to hide public APIs
                                                                                    D) Enable plan mode


                                                                                    3. You want to prevent GitHub Copilot from ever suggesting completions or making edits inside a directory containing sensitive credentials templates. What should you configure?

                                                                                    A) A CODEOWNERS file
                                                                                    B) A .copilotignore file
                                                                                    C) A repository ruleset
                                                                                    D) Branch protection rules


                                                                                    4. Case Study 1 - Contoso, Ltd
                                                                                    Overview
                                                                                    Contoso Ltd. is a software development company located in the United States.
                                                                                    Existing Environment
                                                                                    GitHub Environment
                                                                                    Contoso uses GitHub Enterprise and assigns GitHub Copilot Pro+ licenses to its developers. The developers use Microsoft Visual Studio Code as their IDE.
                                                                                    Contoso has a customer portal. The code for the portal is stored in a GitHub repository named repo1that contains the following:
                                                                                    - A custom agent named agent1 that includes instructions to review specs related to best practices
                                                                                    - A custom instruction file named validate-instructions.md that is used to validate tone of voice and applies to all .md and .txt files
                                                                                    - A custom instruction file named codereview.instructions.md that is used by the Copilot coding agent but is excluded for use by the Copilot code review repo1 has the following structure:
                                                                                    - The front-end is stored in the /frontend folder.
                                                                                    - The API logic is stored in the /api folder.
                                                                                    Contoso has a second repository named repo2 that contains a legacy .NET application named App1 built by using .NET 6. repo2 has a multi-agent workflow for modernization tasks.
                                                                                    Contoso enables the Model Context Protocol (MCP) registry and allows the Microsoft Learn MCP Server. Every developer must configure their own connection to the Learn MCP Server.
                                                                                    Problem Statements
                                                                                    The developers working in repo1 report that the Microsoft Learn documentation is NOT being retrieved when they attempt to validate a design by using agent1.
                                                                                    The testing team at Contoso identifies that the customer portal uses inconsistent UI styles, which leads to customer confusion and branding issues. The UI inconsistencies stem from variations in the folder structure.
                                                                                    Agent Logs
                                                                                    You have the following logs for the multi-agent workflow used in repo2.

                                                                                    Requirements
                                                                                    Planned Changes
                                                                                    Contoso plans to have all agents and developers in repo1use the Microsoft Learn MCP to ensure that reviews are validated by using the appropriate documentation. This must be implemented centrally.
                                                                                    Contoso plans to leverage AI-powered coding agents to implement new portal features and pages.
                                                                                    Technical Requirements
                                                                                    App1 must be upgraded to .NET 10. A previous upgrade attempt was started by using the Copilot modernization agent, but the attempt was never finalized.
                                                                                    You plan to retry the upgrade. You must first analyze App1 by using AI, and then generate a report that contains breaking changes and deprecated patterns before retrying the upgrade.
                                                                                    All AI-generated code for UI styling must adhere to a predefined folder structure.
                                                                                    The architects at Contoso need help building implementation plans for repo1. The company wants to implement a new agent named agent2 to analyze the code base and the code requirements, and then respond with a detailed plan. The agent must NOT be able to edit files or run local commands.
                                                                                    The developers must be able to delegate work to the Copilot coding agent by assigning issues to the agent.
                                                                                    Before App1 is upgraded, you need to verify each individual upgrade step and whether all tests have passed.
                                                                                    Which file should you use?

                                                                                    A) tasks.md
                                                                                    B) plan.md
                                                                                    C) agent.md
                                                                                    D) assessment.md


                                                                                    5. Case Study 1 - Contoso, Ltd
                                                                                    Overview
                                                                                    Contoso Ltd. is a software development company located in the United States.
                                                                                    Existing Environment
                                                                                    GitHub Environment
                                                                                    Contoso uses GitHub Enterprise and assigns GitHub Copilot Pro+ licenses to its developers. The developers use Microsoft Visual Studio Code as their IDE.
                                                                                    Contoso has a customer portal. The code for the portal is stored in a GitHub repository named repo1that contains the following:
                                                                                    - A custom agent named agent1 that includes instructions to review specs related to best practices
                                                                                    - A custom instruction file named validate-instructions.md that is used to validate tone of voice and applies to all .md and .txt files
                                                                                    - A custom instruction file named codereview.instructions.md that is used by the Copilot coding agent but is excluded for use by the Copilot code review repo1 has the following structure:
                                                                                    - The front-end is stored in the /frontend folder.
                                                                                    - The API logic is stored in the /api folder.
                                                                                    Contoso has a second repository named repo2 that contains a legacy .NET application named App1 built by using .NET 6. repo2 has a multi-agent workflow for modernization tasks.
                                                                                    Contoso enables the Model Context Protocol (MCP) registry and allows the Microsoft Learn MCP Server. Every developer must configure their own connection to the Learn MCP Server.
                                                                                    Problem Statements
                                                                                    The developers working in repo1 report that the Microsoft Learn documentation is NOT being retrieved when they attempt to validate a design by using agent1.
                                                                                    The testing team at Contoso identifies that the customer portal uses inconsistent UI styles, which leads to customer confusion and branding issues. The UI inconsistencies stem from variations in the folder structure.
                                                                                    Agent Logs
                                                                                    You have the following logs for the multi-agent workflow used in repo2.

                                                                                    Requirements
                                                                                    Planned Changes
                                                                                    Contoso plans to have all agents and developers in repo1use the Microsoft Learn MCP to ensure that reviews are validated by using the appropriate documentation. This must be implemented centrally.
                                                                                    Contoso plans to leverage AI-powered coding agents to implement new portal features and pages.
                                                                                    Technical Requirements
                                                                                    App1 must be upgraded to .NET 10. A previous upgrade attempt was started by using the Copilot modernization agent, but the attempt was never finalized.
                                                                                    You plan to retry the upgrade. You must first analyze App1 by using AI, and then generate a report that contains breaking changes and deprecated patterns before retrying the upgrade.
                                                                                    All AI-generated code for UI styling must adhere to a predefined folder structure.
                                                                                    The architects at Contoso need help building implementation plans for repo1. The company wants to implement a new agent named agent2 to analyze the code base and the code requirements, and then respond with a detailed plan. The agent must NOT be able to edit files or run local commands.
                                                                                    The developers must be able to delegate work to the Copilot coding agent by assigning issues to the agent.
                                                                                    You need to make changes to repo1 to support the planned changes for the agents.
                                                                                    What should you modify?

                                                                                    A) .github/agents/*.agent.md
                                                                                    B) <project>/.mcp/server.json
                                                                                    C) .vscode/settings.json
                                                                                    D) .vscode/mcp.json


                                                                                    Solutions:

                                                                                    Question # 1
                                                                                    Answer: B
                                                                                    Question # 2
                                                                                    Answer: B
                                                                                    Question # 3
                                                                                    Answer: B
                                                                                    Question # 4
                                                                                    Answer: D
                                                                                    Question # 5
                                                                                    Answer: D

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