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GH-600 pdf
  • Exam Code: GH-600
  • Exam Name: GitHub Agentic AI Developer
  • Q & A: 85 Questions and Answers
  • PDF Price: $59.99
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  • Exam Code: GH-600
  • Exam Name: GitHub Agentic AI Developer
  • Q & A: 85 Questions and Answers
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  • Exam Code: GH-600
  • Exam Name: GitHub Agentic AI Developer
  • Q & A: 85 Questions and Answers
  • Software Price: $59.99
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Microsoft GH-600 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Manage memory, state, and execution10-15%- Control execution flow
  • 1. Monitor execution lifecycle
  • 2. Recover from interruptions and failures
- Manage context and memory
  • 1. Handle long-running execution
  • 2. Maintain agent state
Topic 2: Implement guardrails and accountability10-15%- Ensure accountability
  • 1. Establish human-in-the-loop processes
  • 2. Track actions and decisions
- Implement governance controls
  • 1. Apply security and compliance requirements
  • 2. Configure approval workflows
Topic 3: Perform evaluation, error analysis, and tuning15-20%- Improve agent behavior
  • 1. Perform error analysis
  • 2. Tune prompts, tools, and configurations
- Evaluate agent performance
  • 1. Measure outcomes and quality
  • 2. Analyze scans, logs, and artifacts
Topic 4: Orchestrate multi-agent coordination15-20%- Ensure safe collaboration
  • 1. Prevent conflicts and unsafe actions
  • 2. Coordinate execution across agents
- Coordinate multiple agents
  • 1. Design multi-agent workflows
  • 2. Manage communication and task delegation
Topic 5: Implement tool use and environment interaction20-25%- Select and configure agent tools
  • 1. Identify required tools
  • 2. Configure tools and permissions
- Manage execution environments
  • 1. Configure environments for agents
  • 2. Control access to resources and services
Topic 6: Prepare agent architecture and SDLC processes15-20%- Configure observability and control
  • 1. Generate inspectable artifacts
  • 2. Enable human intervention when required
  • 3. Implement autonomy levels and guardrails
- Integrate agents into the software development lifecycle
  • 1. Define inputs, outputs, and success criteria
  • 2. Identify steps for agents to perform
  • 3. Identify and mitigate agent anti-patterns
- Define boundaries between planning, reasoning, and action
  • 1. Configure structured plans
  • 2. Validate and approve plans before execution
  • 3. Separate planning from execution

Microsoft GitHub Agentic AI Developer Sample Questions:

1. Hotspot Question
Your company uses Microsoft Visual Studio Code and GitHub Copilot Chat.
You have a GitHub repository that uses main as the default branch. The repository contains a workspace custom agent stored at .github/agents/release-notes.agent.md.
A developer switches to a branch named branch1 where the agent file does NOT exist. In the same session, the developer switches to a user profile named profile1.profile1 contains a custom agent file named release-notes.agent.md that has user-invokable set to false.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.


2. 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 provide access to the API key of MCP1. The solution must meet the security requirements.
What should you do?

A) Store the API key as a secret in the Copilot environment of product-api by using a name prefix of COPILOT_MCP_, and then reference the variable name in the mcp.json configuration.
B) In product-api, add the API key as a GitHub Actions encrypted secret and reference the secret by using ${{ secrets.KEY }} in the workflow YAML of agent1.
C) In the product-api repository settings, add the API key directly to the .mcp/server.json file by using a plaintext apiKey field.
D) Store the API key as a GitHub Codespaces user secret scoped to product-api.


3. 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

Hotspot Question
You are evaluating how agent1 will behave after you implement the planned changes.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.


4. You have a GitHub Copilot coding agent named CodeAgent. The .agent.md file of CodeAgent contains the following YAML frontmatter.
---
name: CodeAgent
description: Performs repository analysis and code review tasks.
tools: ['edit', 'execute', 'read', 'search']
---
You need to issue a GitHub Copilot CLI command that preserves execution velocity for read-only tasks by eliminating approval prompts for low-risk tools. The solution must ensure that high-risk tools that can make changes remain available but still require explicit user approval before running.
Which command should you run?

A) copilot agent run CodeAgent
B) copilot agent run CodeAgent --allow-all-tools
C) copilot agent run CodeAgent --allow-tool 'read,search' --deny-tool 'edit,execute'
D) copilot agent run CodeAgent --allow-tool 'read,search'
E) copilot agent run CodeAgent --deny-tool 'edit,execute'


5. Your organization requires that any workflow file changes proposed by an autonomous agent be reviewed by a member of the security team before merging, regardless of who opened the pull request. What combination should you configure?

A) CODEOWNERS + a required branch protection rule
B) copilot-instructions.md + required status checks
C) .copilotignore + agents.md
D) MCP server permissions + repository ruleset


Solutions:

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

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