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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: Orchestrate multi-agent coordination | 15-20% | - Operate and manage multi-agent workflows
|
| Topic 3: Implement guardrails and accountability | 10-15% | - Implement guardrails and human-in-the-loop workflows
|
| Topic 4: Prepare agent architecture and SDLC processes | 15-20% | - Configure observability and control for autonomous agents
|
| Topic 5: Implement tool use and environment interaction | 20-25% | - Integrate agents within development environments
|
| Topic 6: Manage memory, state, and execution | 10-15% | - Persist agent state and manage context drift
|
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NEW QUESTION # 91
You have a GitHub repository that uses the GitHub Copilot coding agent.
Developers need the Copilot coding agent to call an internal dependency-scanning API during its run. The API requires an access token.
You need to ensure that the Copilot coding agent can use the token during execution without accessing the repository's Actions secrets and variables. The solution must prevent exposing the token in plaintext.
What should you do?
Answer: C
Explanation:
Option D places the token in the secret store intended for the coding agent's execution environment. This gives the agent's tools access to the credential without committing the credential to an agent profile or repository instruction file. The application can consume the injected environment variable when authenticating to the internal scanning API.
GitHub's current interface calls this dedicated category Agents secrets and variables. Its documentation states that secrets previously configured in the repository's copilot environment were automatically migrated to that category. Therefore, D represents the correct agent-specific mechanism using the terminology in the question.
Option A uses the separate Actions secret category, which is not automatically exposed to the cloud agent. Options B and C place sensitive material in repository content, making the token accessible through file access and potentially retained in version history.
Current documentation also confirms that agent secrets are made available as environment variables and their values are masked in session logs. The integration should still avoid deliberately printing credentials or returning them in tool output.
Relevant curriculum topics are secure environment configuration, authenticated tool access, and separation of credential scopes.
Reference:
NEW QUESTION # 92
You have a GitHub repository that uses the GitHub Copilot coding agent to resolve issues and create draft pull requests. The repository uses GitHub Actions for CI, and reviewers rely on pull request timelines and workflow artifacts to understand what the agent did.
During long-running agent tasks, the reviewers lose track of decisions and validation steps, which causes repeated questions and reworks when context drifts between iterations.
You need to persist task progress and decisions as durable artifacts and ensure that the reviewers can verify what the agent did during and after execution by using GitHub as the system of record.
What should you do for each requirement? To answer, drag the appropriate actions to the correct requirements. Each action 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:
NEW QUESTION # 93
You have the following agent logs.
Answer:
Explanation:
NEW QUESTION # 94
You are troubleshooting why a Copilot coding agent pull request keeps failing CI checks after every attempted fix. What is the most effective first step?
Answer: B
NEW QUESTION # 95
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 # 96
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