Microsoft GH-600 exam - in .pdf

GH-600 pdf
  • Exam Code: GH-600
  • Exam Name: GitHub Agentic AI Developer
  • Updated: Aug 02, 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 02, 2026
  • Q & A: 85 Questions and Answers
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Microsoft GH-600 exam - Testing Engine

GH-600 Testing Engine
  • Exam Code: GH-600
  • Exam Name: GitHub Agentic AI Developer
  • Updated: Aug 02, 2026
  • Q & A: 85 Questions and Answers
  • Software Price: $59.99
  • Testing Engine

About Microsoft GitHub Agentic AI Developer : GH-600 Exam Torrent

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Microsoft GH-600 Exam Syllabus Topics:

SectionWeightObjectives
Orchestrate multi-agent coordination15–20%- Design workflows for multiple agents
- Monitor and troubleshoot multi-agent execution
- Prevent conflicts and manage shared resources
- Define communication and handoff protocols
Implement tool use and environment interaction20–25%- Manage permissions and environment access
- Connect agents to codebase, APIs, and external systems
- Configure and extend GitHub Copilot agents
- Implement tools, custom actions, and MCP servers
Perform evaluation, error analysis, and tuning15–20%- Define metrics and quality standards for outputs
- Test, validate, and compare agent results
- Diagnose failures, hallucinations, and unexpected behavior
- Optimize prompts, tools, and behavior through iteration
Manage memory, state, and execution10–15%- Scope and persist agent state correctly
- Handle execution flow, retries, and interruptions
- Implement memory cleanup and expiration rules
- Choose memory types: short-term, long-term, external
Implement guardrails and accountability10–15%- Log actions, decisions, and changes for audit
- Add validation, review, and approval gates
- Ensure compliance, safety, and responsible use
- Enforce least privilege and security boundaries
Prepare agent architecture and SDLC processes15–20%- Integrate agents into software development lifecycle
- Define agent purpose, scope, and success criteria
- Design agent autonomy and decision boundaries
- Plan agent deployment, monitoring, and maintenance

Microsoft GitHub Agentic AI Developer Sample Questions:

1. Case Study 2
Existing Environment
GitHub Environment
The GitHub environment contains the following:
- Three repositories named product-api, billing-service, and infra-terraform.
- Branch protection on the main branch in all repositories that requires at least one pull request review before merging
- GitHub Actions runners used across all workflows
- A GitHub team named SG_Dev that contains developers
- A GitHub team named SG_Review that contains senior engineers and a security team
- A .github/copilot-instructions.md file that includes general coding conventions for all features Agent environment The product-api repository uses a GitHub Copilot coding agent named agent1 that has the following configurations:
- No custom agent profile is defined.
- A Model Context Protocol (MCP) server named MCP1 is deployed to
https://mcp.litwareinc.internal and provides access to internal ticketing and deployment APIs.
MCP1 requires an API key for authentication.
A second Copilot coding agent named agent2 handles changes in infra-terraform and runs in parallel with agent1 when both agents have open assigned issues.
Copilot memory is NOT enabled for the organization.
Problem Statements
Litware identifies the following issues:
- During two recent sessions, agent1 accessed files in billing-service, which is outside the agent's intended scope.
- agent1 makes code changes immediately after receiving a task.
- A developer named Ben, who is on the SG_Dev team, reports that agent1 completed a session with a successful status and opened a pull request, but the pull request contains no file changes.
Other developers report this intermittently as well.
- Both agent1 and agent2 modified shared/config.yaml in a parallel test run, generating conflicting outputs.
agent1 consistently uses raw try-catch blocks for error handling, which violates the defined implementation guidelines of SG_Dev.
Requirements
Planned Changes
Litware plans to make the following changes:
- Ensure that agent1 can access all the tools in the environment.
- Provide product-api with specific instructions to agent1 without affecting Copilot Chat or Copilot code review.
- Configure MCP1 as a tool for agent1 by modifying the product-api repository MCP configuration.
- Ensure that Copilot retains details that it has learned and uses that knowledge for future work.
This must be applied to all licensed members of the organization.
Implementation guidelines
The development team at Litware identifies the following implementation guidelines:
- Agent workflows must be able to run in parallel.
- Application error handling must use the repository ErrorHandler class.
- agent1 and agent2 must run on isolated branches during parallel execution. File-level conflicts must be detected before merges, and both agents must be able to run concurrently.
Security requirements
Litware identifies the following security requirements:
- Only the members of SG_Review must be able to approve agent1 plan outputs.
- All API keys must be stored and accessed securely.
- The developers must NOT be able to self-approve.
Agent configuration

You need to troubleshoot the issue reported by Ben.
What should you review?

A) the GitHub Actions runner log for the session job
B) the GitHub Actions usage metrics of repo1
C) the agent session log in the Agents panel
D) the GITHUB_TOKEN permissions block in the agent1 workflow


2. After several long agentic sessions, you notice degraded response quality and want the agent to retain a concise summary of prior progress rather than the full raw history, while continuing without interruption. Which approach addresses this?

A) .copilotignore
B) /compact
C) --allow-all
D) /clear


3. Your organization wants to ensure the Copilot coding agent cannot push directly to main, and all its changes must go through a pull request review process like any other contributor. What enforces this?

A) .copilotignore
B) /context
C) copilot-instructions.md
D) Standard branch protection rules on the main branch


4. During an agentic session, you want to see exactly how many tokens are being consumed by the system prompt, tools, and message history before deciding whether to compact. Which command should you run?

A) /diff
B) /context
C) /tokens
D) /usage


5. Drag and Drop Question
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.


Solutions:

Question # 1
Answer: C
Question # 2
Answer: B
Question # 3
Answer: D
Question # 4
Answer: B
Question # 5
Answer: Only visible for members

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