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Microsoft GH-600 Exam Syllabus Topics:

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

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Microsoft GitHub Agentic AI Developer Sample Questions (Q22-Q27):

NEW QUESTION # 22
You are evaluating the following workflow YAML.
jobs:
agent1:
runs-on: ubuntu-latest
environment: agent-sandbox
steps:
- uses: actions/checkout@v4
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 # 23
Hotspot Question
You have a GitHub repository that uses GitHub Actions for CI.
Your team is piloting the GitHub Copilot coding agent to autonomously create branches and open pull requests. The repository follows trunk-based development that uses main as the default branch.
You need to ensure that the agent meets the following requirements:
- Changes to main can occur only by using pull requests that have at
least one approval.
- When a pull request is opened, a validation workflow runs, and the
agent can still create branches and open pull requests autonomously.
How should you configure the repository? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Box 1: Enabled
To meet the requirements, the Branch protection rule (main) - Require pull request before merging must be Enabled. This forces everyone-including GitHub Actions and AI agents-to create a branch and open a pull request first.
Box 2: on: pull_request
To allow your GitHub Copilot agent to autonomously open PRs while maintaining branch protection, you must trigger your validation workflow on the pull_request event.
Bypasses Protection: Agents cannot push directly to main due to your branch protection rule.
They must create a feature branch and open a PR.Triggers on Creation: The pull_request trigger fires automatically the moment the agent opens a PR.Runs in Isolation: It validates the merged result of the agent's branch and main before any actual code is merged.
Reference:
https://tutorialsdojo.com/github-copilot-coding-agent/


NEW QUESTION # 24
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?

Answer: A

Explanation:
Scenario, Planned Changes: Contoso plans to have all agents and developers in repo1 use the Microsoft Learn MCP to ensure that reviews are validated by using the appropriate documentation. This must be implemented centrally.
To centrally configure the Microsoft Learn Model Context Protocol (MCP) server for all developers and agents within a shared repository, you should modify the .vscode/mcp.json file.
Central Repository Configuration: In GitHub Copilot and Visual Studio Code, placing an mcp.json file inside the workspace root's .vscode/ directory ensures that the defined MCP servers are automatically loaded and shared with any developer or Copilot agent who opens that specific project repository.
Tool Exposure: This configuration file maps the external tools provided by the Microsoft Learn Docs MCP server (such as searching and fetching official documentation) directly into the Copilot agentic workflow.
Reference:
https://docs.github.com/en/copilot/how-tos/copilot-on-github/customize-copilot/configure-mcp-servers


NEW QUESTION # 25
You have a GitHub Enterprise Cloud repository that uses the GitHub Copilot coding agent to resolve backlog issues by creating draft pull requests. The repository uses a GitHub Actions workflow for deployments to the production environment.
You discover that the workflow is being triggered before human review.
You need to configure GitHub controls to meet the following requirements:
The workflow for the agent's draft pull requests must NOT run until a user that has write access approves the pull requests.
Production deployment jobs must start only after a user with write access explicitly approves the jobs.
Which controls should you configure? To answer, drag the appropriate controls to the correct requirements.

Answer:

Explanation:


NEW QUESTION # 26
A developer uses the GitHub Copilot CLI in plan mode.
Copilot produces a plan.
What does Copilot do next?

Answer: D

Explanation:
In plan mode, after GitHub Copilot CLI analyzes your codebase and gathers requirements, it saves the plan to plan.md in your session folder.
What Happens Next?
1. Saves to plan.md: It writes the structured implementation plan with checkable tasks into a file called plan.md.
2. Waits for Approval: Copilot pauses and waits for your review. It will not begin implementation or make code modifications until you explicitly approve the strategy.
3. Execution: Once approved, you can hand the plan off to Copilot's agent/autopilot mode to begin the implementation.
Reference:
https://docs.github.com/en/copilot/how-tos/copilot-cli/cli-best-practices


NEW QUESTION # 27
......

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