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| Section | Weight | Objectives |
|---|---|---|
| Implement tool use and environment interaction | 20-25% | - Select and configure agent tools
|
| Implement guardrails and accountability | 10-15% | - Implement guardrails and human-in-the-loop workflows
|
| Perform evaluation, error analysis, and tuning | 15-20% | - Define success criteria and evaluation signals for agent tasks
|
| Orchestrate multi-agent coordination | 15-20% | - Manage the lifecycle of agents within multi-agent workflows
|
| Prepare agent architecture and SDLC processes | 15-20% | - Integrate agents into the software development lifecycle (SDLC)
|
| Manage memory, state, and execution | 10-15% | - Ensure continuity of agent memory and state across tools and environments
|
>> Valid GH-600 Test Questions <<
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NEW QUESTION # 22
You have a GitHub Enterprise repository that runs an autonomous agent by using a GitHub Actions workflow. The workflow has the following jobs:
* agent-run that generates trace.json and plan.md
* review that waits for human approval before continuing
* deploy that uses the outputs from agent-run
You need to make the files inspectable in the GitHub Actions UI and ensure that the files are available to the review and deploy jobs.
What should you do in the workflow?
Answer: C
Explanation:
Workflow artifacts provide the required combination of retained execution evidence and file transfer between jobs. The agent-run job uploads trace.json and plan.md, associating both files with the workflow run.
Reviewers can access the resulting artifact through the run's GitHub Actions interface, while downstream jobs download it to their own execution environments.
This is necessary because separate jobs do not automatically share a local filesystem. Declaring a dependency establishes execution order but does not transfer the generated files. The review and deployment stages therefore need explicit artifact downloads in addition to their appropriate job dependencies.
Committing transient traces and plans introduces repository changes solely to transport run-specific evidence.
A network share adds external infrastructure and does not directly satisfy the requirement for standard Actions artifact access. Dependency caching is intended for reusable inputs such as downloaded dependencies; it is not the appropriate mechanism for authoritative outputs from a particular agent run.
Artifacts preserve the plan and trace that informed the review, allowing subsequent deployment activity to use the corresponding files. Relevant curriculum topics are inspectable agent artifacts, observability, and human intervention.
Reference: Store and share data with workflow artifacts .
NEW QUESTION # 23
You need to provide access to the API key of MCP1. The solution must meet the security requirements.
What should you do?
Answer: B
Explanation:
The API key should be stored as a secret in the Copilot environment and referenced by name from the MCP configuration. This keeps the secret value out of repository files, agent prompts, pull requests, and ordinary workflow output while allowing the MCP server to receive the required credential at runtime.
A plaintext key in a configuration file is unacceptable because repository history, forks, logs, and code review systems can expose it. A Codespaces user secret is scoped to a user development environment and does not provide the intended runtime secret for the Copilot agent environment. A GitHub Actions secret is useful for workflow expressions, but the scenario requires the key to be available to the MCP server configuration itself.
The prefixed environment-secret approach creates a clear boundary: the configuration Reference an identifier, while the secret value is resolved only in the authorized execution environment. This supports rotation without changing tracked configuration content.
Study-guide topics: MCP authentication, secrets management, secure environment configuration, and credential isolation.
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: D
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
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.
Answer:
Explanation:
Explanation:
Box 1: No
The agent will not appear in the agents dropdown with a display name of release-notes because the developer is currently on branch1 and profile1, which effectively hides it.
Box 2: No
The workspace version of the release-notes agent will not be available in the agents dropdown when the developer is on branch2.
Box 3: No
The user profile agent will not appear in the agents dropdown, nor will it override a workspace agent in this scenario.
Reference:
https://code.visualstudio.com/docs/agent-customization/custom-agents
NEW QUESTION # 26
You need to resolve the scoping issue associated to agent1.
What should you do?
Answer: D
Explanation:
A fine-grained PAT restricted to product-api provides an enforceable repository-level authorization boundary for agent1. GitHub fine-grained personal access tokens can be limited to selected repositories and assigned only the repository permissions required by the workload. Consequently, even if agent1 attempts to access billing-service or infra-terraform, the credential does not authorize access to those repositories. GitHub specifically recommends fine-grained PATs when repository access must be restricted to particular repositories.
This directly aligns with the GH-600 objective to configure an agent ' s scope to a specific repository and define execution boundaries through repository context and permissions. Microsoft Learn defines execution context as including the repository an agent can access and emphasizes that repository scope is the first boundary limiting agent behavior.
Option A provides behavioral guidance but is not a security authorization boundary. Option B controls the permissions available to the workflow token but does not, by itself, establish the required cross-repository credential restriction. Option D only prevents certain push operations; it does not stop agent1 from reading or otherwise accessing the other repositories.
Study Guide Reference Topics: Implement Tool Use and Environment Interaction # execution context and boundaries; repository-specific agent scope; tool permissions; least-privilege authentication.
NEW QUESTION # 27
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