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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Perform evaluation, error analysis, and tuning | 15–20% | - Tune agent behavior based on evaluation results
|
| Topic 2: Manage memory, state, and execution | 10–15% | - Persist agent state and manage context drift
|
| Topic 3: Orchestrate multi-agent coordination | 15–20% | - Detect and respond to multi-agent failures and degraded behavior
|
| Topic 4: Implement guardrails and accountability | 10–15% | - Implement guardrails and human-in-the-loop workflows
|
| Topic 5: Implement tool use and environment interaction | 20–25% | - Configure MCP servers
|
| Topic 6: Prepare agent architecture and SDLC processes | 15–20% | - Define boundaries between planning, reasoning, and action
|
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NEW QUESTION # 55
Hotspot Question
You have a GitHub repository that uses the GitHub Copilot coding agent to resolve issues and create draft pull requests.
You assign an issue to Copilot. Copilot creates a draft pull request. The pull request timeline shows Copilot started work, followed by status updates. The most recent status update is 55 minutes old.
You discover that the agent is no longer making progress.
You need to ensure that the work resumes without redoing the completed steps or changing the previously chosen approach.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Select View session from the pull request
To best confirm the agent's progress, troubleshoot the stagnation, and ensure the work resumes seamlessly from where it left off, you should interact directly with the agent's active workspace.
Open the Session View: Navigate to the pull request on GitHub and click the View session button.
This opens the step-by-step agent workflow and reasoning interface.
Inspect the Logs: Review the detailed execution log in the session timeline to identify exactly where the background task (running in GitHub Actions) stalled or encountered an error.
Steer or Prompt to Resume: Instead of unassigning and reassigning the issue (which completely restarts the task from scratch), use the chat input or steering tools directly inside the View session page or Agents panel to nudge Copilot. Providing a clarification prompt allows the agent to continue its current approach without losing completed steps.
Box 2: Post a pull request comment that mentions @copilot
You can resume the work by commenting on the pull request and mentioning @copilot, along with a prompt instructing it to continue.When a GitHub Copilot coding agent stalls or pauses on a draft pull request, posting a comment mentioning @copilot wakes the agent back up. Because the agent evaluates the entire conversation history, code changes, and task checklists within that pull request lifecycle, it will resume from its last saved state. It maintains the previously chosen approach and carries on with the remaining items without repeating already completed steps.
Reference:
https://docs.github.com/en/copilot/how-tos/use-copilot-agents/cloud-agent/troubleshoot-cloud-agent
NEW QUESTION # 56
An agent working through the Copilot coding agent in GitHub Actions needs additional build dependencies installed before it can run tests. Where should you define this setup?
Answer: A
Explanation:
copilot-setup-steps.yml defines a custom setup workflow (e.g., installing dependencies, configuring environment variables) that runs before the coding agent starts its task, ensuring it has the environment it needs.
NEW QUESTION # 57
You have a multi-agent GitHub Actions workflow that uploads review artifacts for each run.
You discover that some workflow run artifacts are being deleted manually.
You need to use your organization's audit log data to identify which user deleted the artifacts.
Which audit log search filter should you use?
Answer: D
Explanation:
To identify which user manually deleted the workflow run artifacts, you should use the action:artifact.destroy search filter.When searching your GitHub Organization Audit Log, this filter targets the exact event triggered when a workflow run artifact is manually removed.
Reference:
https://docs.github.com/en/organizations/keeping-your-organization-secure/managing-security-settings-for-your-organization/reviewing-the-audit-log-for-your-organization
NEW QUESTION # 58
You are architecting an agentic AI system and need the agent's tool-calling behavior to be constrained so it can only call a specific allow-listed set of MCP tools, never arbitrary ones. What should you configure?
Answer: A
Explanation:
The correct answer is A because tool availability must be constrained at the agent/MCP integration boundary. GitHub custom-agent configuration provides a tools property that explicitly determines which tools the agent is permitted to use, including tools supplied by configured MCP servers. Rather than enabling all tools with ["*"] or omitting the restriction, administrators can specify individual tool names or aliases and thereby implement an explicit allow-list.
This is a fundamental least-privilege control for agentic systems. An MCP server can expose numerous capabilities, but exposure by the server does not mean every capability should automatically be available to every agent. Restricting the agent to the tools required for its assigned role reduces unintended actions and limits the blast radius of erroneous reasoning, prompt injection, or excessive autonomy. GitHub also supports repository MCP configuration through which administrators determine which external MCP integrations are available.
.copilotignore does not implement MCP authorization. Repository rulesets govern GitHub repository operations such as branches and merges, while /usage reports runtime/session information.
Study Guide Reference Topics: Implement Tool Use and Environment Interaction; MCP integration; tool allow-listing; least privilege; execution boundaries.
NEW QUESTION # 59
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: C
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 # 60
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