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| Section | Weight | Objectives |
|---|---|---|
| Perform evaluation, error analysis, and tuning | 15–20% | - Tune agent behavior based on evaluation results
|
| Manage memory, state, and execution | 10–15% | - Persist agent state and manage context drift
|
| Prepare agent architecture and SDLC processes | 15–20% | - Configure observability and control for autonomous agents
|
| Implement tool use and environment interaction | 20–25% | - Integrate agents within development environments
|
| Implement guardrails and accountability | 10–15% | - Implement guardrails and human-in-the-loop workflows
|
| Orchestrate multi-agent coordination | 15–20% | - Configure observability for multi-agent behavior by using logs, artifacts, and operational signals
|
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NEW QUESTION # 108
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?
Answer: A
Explanation:
Allowing only read and search removes approval friction for operations that inspect repository content without modifying it. The edit and execute tools remain available in the agent profile, but they continue to require explicit approval because they are not included in the command's approval bypass.
Using no option preserves the default approval behavior for all tools, including low-risk inspection tools, which does not meet the velocity requirement. Allowing all tools would remove the approval safeguard for file modifications and shell execution. Denying edit and execute prevents those tools from being used at all, rather than keeping them available under human control.
This design separates tool availability from automatic authorization. Read-only operations can proceed autonomously, while potentially impactful operations remain subject to a human decision at the point of use.
Study-guide topics: tool approval boundaries, least privilege, read-only execution, and controlled agent autonomy.
NEW QUESTION # 109
You have a custom agent profile file named test-agent.agent.md that contains the following YAML frontmatter:
---
name: test-agent
description: Custom agent description
tools: ['tool-a', 'tool-b']
---
In the same repository, you have an MCP configuration file named mcp.json.
You need to ensure that the GitHub Model Context Protocol (MCP) server is available to test the agent. The solution must allow only the Copilot toolset.
What should you do?
Answer: C
Explanation:
The copilot/* toolset grants the agent access to the Copilot-provided tools while avoiding unrelated tool groups. This directly meets the requirement to allow only the Copilot toolset for the test agent.
Keeping tool-a and tool-b would expand access beyond the stated requirement. Using github/* would expose the broader GitHub tool group rather than limiting the profile to the Copilot toolset. The MCP configuration retains its server structure; changing the server property name does not configure which tools the agent may use.
Toolsets are an important control surface for specialized agents. They let an architect define a narrow capability set for testing, planning, review, or implementation roles. The selected toolset should still be backed by an available and authorized MCP configuration; naming a toolset does not bypass server authentication or policy restrictions.
Study-guide topics: MCP toolsets, custom agent capabilities, and least-privilege tool access.
NEW QUESTION # 110
You use the GitHub Copilot CLI in ephemeral dev containers.
You need to provide Copilot with reusable guidance for a specific task only. The guidance must be stored in the repository and invoked only when the task is relevant.
What should you do?
Answer: D
Explanation:
A repository skill stored under .github/skills/<skill-name>/SKILL.md provides reusable, task-specific guidance that can be invoked when relevant. Because it is committed to the repository, the guidance is available in ephemeral development containers without relying on a developer's local configuration.
Repository-wide instructions in .github/copilot-instructions.md are applied broadly and are suitable for enduring conventions, architectural rules, or team standards. They are not the best choice when guidance should apply only to a particular task or workflow.
Personal instructions are user-specific and do not provide a version-controlled, team-shared implementation. A .copilot/skills path does not represent the repository-scoped skill location required by the scenario.
Task-specific skills improve precision by supplying focused procedures, reference material, and constraints only when the related task is active. This prevents unrelated work from being burdened by instructions that do not apply.
Study-guide topics: agent skills, repository-scoped guidance, contextual instructions, and ephemeral development environments.
NEW QUESTION # 111
You have a GitHub repository that uses GitHub Actions to validate pull requests opened by the GitHub Copilot coding agent. The workflow runs unit tests and a linter on pull request triggers, and Copilot opens draft pull requests on dedicated branches while iterating by using commits.
You discover that when multiple Copilot sessions push updates to the same pull request branch in quick succession, multiple workflow runs execute concurrently.
You need to enable parallel workflow executions across different pull request branches.
How should you configure workflow-level concurrency? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 112
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: A
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 # 113
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