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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement agents and multi-agent systems | 30% | - Orchestrate multi-agent collaboration
|
| Topic 2: Design agentic AI solutions | 25% | - Define requirements for agentic systems
|
| Topic 3: Test, deploy, and monitor agentic AI systems | 20% | - Validate agent performance and safety
|
| Topic 4: Integrate tools, data, and services | 25% | - Connect data sources and knowledge bases
|
>> New GH-600 Exam Question <<
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NEW QUESTION # 64
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?
Answer: B
Explanation:
To analyze a legacy application using GitHub Copilot modernization agents before executing a .NET 10 upgrade, you must use the assessment.md file Scenario:
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.
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.
Reference:
https://learn.microsoft.com/en-us/dotnet/core/porting/github-copilot-app-modernization/overview
NEW QUESTION # 65
You have a GitHub repository.
Developers use the GitHub Copilot CLI and repository-scoped hooks under .github/hooks/*.json.
You need to allow the Copilot CLI to automatically run low-risk Bash commands. The solution must prevent the autonomous execution of high-risk commands, such as sudo, rm -rf, and curl ... | bash.
What should you do?
Answer: B
Explanation:
A preToolUse hook evaluates the proposed command before the shell tool executes it. The hook can inspect command arguments and return permissionDecision: "deny" when a pattern indicates a high-risk operation, while allowing low-risk commands to continue automatically.
This is the correct enforcement point because the requirement is preventive. A policy banner only informs the user or agent; it cannot block execution. Logging submitted prompts provides audit information but does not evaluate the actual shell command. Ignoring the hooks directory simply removes the repository-scoped enforcement mechanism.
The hook should use precise matching rules. For example, it can deny sudo, destructive recursive deletion, and piping untrusted remote content into a shell, while allowing commands such as git status, package metadata inspection, and test execution. Rules should avoid broad string matching that blocks harmless commands merely because they contain similar text.
Study-guide topics: pre-execution guardrails, shell-command approval, policy enforcement, and autonomous tool safety. Reference: GitHub Copilot-Hooks reference.
NEW QUESTION # 66
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.
Answer:
Explanation:
Explanation:
Target
Correct selection
Checkout ref
${{ github.sha }}
File checked by test -f
github/agents/rollout-bot.agent.md
Profile passed to the deployment script
github/agents/rollout-bot.agent.md
The workflow must deploy an agent profile from a specific repository snapshot. Setting the checkout reference to ${{ github.sha }} identifies the commit associated with the workflow event, rather than following a branch reference that might move as new commits arrive. The checkout action supports selecting a commit SHA through its ref input.
Both subsequent operations must use .github/agents/rollout-bot.agent.md. The existence check confirms that the expected profile is present in the checked-out revision, and the deployment script receives that same version-controlled file. Using a different path would break the relationship between the validated profile and the deployed profile.
Rollback requires one additional operational distinction: reverting a change creates a new commit containing the restored content. The deployment must run against that revert commit. Re-running the original failed workflow run retains its original SHA and does not automatically deploy the later revert.
This approach keeps agent behavior, repository history, and deployment provenance aligned, making the profile's effective version identifiable and recoverable.
Relevant curriculum topics are durable state, controlled rollback, and continuity of agent configuration across executions.
References: actions/checkout , Re-running workflows and jobs .
NEW QUESTION # 67
You have a repository that uses a GitHub Actions workflow to run an agent-driven change plan as part of a CI pipeline. The workflow generates an artifact named plan.json that includes a field named risk. Risk has possible values of low, medium, or high.
You need to ensure that a human must confirm the execution of the workflow when risk is medium or high. The workflow must proceed automatically only when risk is low.
How should you complete the workflow? To answer, drag the appropriate values to the correct targets.
Answer:
Explanation:
NEW QUESTION # 68
You have a GitHub repository that stores custom GitHub Copilot agents in the .github/agents/ folder.
You have an agent named lead-dev.agent.md that must invoke a specialist subagent named security-reviewer.
Evaluation results show that lead-dev attempts to invoke security-reviewer, but the subagent call fails to run.
You need to ensure that lead-dev can invoke security-reviewer.
How should you complete the YAML frontmatter of the agent profile? To answer, drag the appropriate values to the correct targets.
Answer:
Explanation:
NEW QUESTION # 69
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