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| Section | Weight | Objectives |
|---|---|---|
| Test, deploy, and monitor agentic AI systems | 20% | - Validate agent performance and safety
|
| Implement agents and multi-agent systems | 30% | - Orchestrate multi-agent collaboration
|
| Integrate tools, data, and services | 25% | - Incorporate external tools and APIs
|
| Design agentic AI solutions | 25% | - Define requirements for agentic systems
|
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NEW QUESTION # 82
After several long agentic sessions, you notice degraded response quality and want the agent to retain a concise summary of prior progress rather than the full raw history, while continuing without interruption. Which approach addresses this?
Answer: D
Explanation:
/compact summarizes the existing conversation history via the model and replaces the raw history with the condensed summary, preserving continuity and prior progress while freeing up context window space -- unlike /clear, which discards everything.
NEW QUESTION # 83
You have a GitHub repository that uses the GitHub Copilot coding agent.
You need to complete the hooks configuration to meet the following requirements:
Tool usage must be evaluated before execution.
Build results must be logged after execution.
Tool usage must be logged after execution.
How should you complete the hooks configuration? To answer, drag the appropriate values to the correct targets.
Answer:
Explanation:
NEW QUESTION # 84
You want the GitHub Copilot coding agent to follow project-specific conventions (coding style, testing requirements, folder structure) on every task it performs in a repository. What should you create?
Answer: D
Explanation:
The correct configuration is .github/copilot-instructions.md. GitHub defines this file as the repository-wide custom-instructions mechanism for providing persistent project-specific guidance to Copilot. Instructions stored there are automatically incorporated when Copilot works in the repository, making the file appropriate for conventions that should apply repeatedly, such as coding standards, architectural expectations, preferred test frameworks, build and validation commands, repository layout, and required implementation patterns.
This is particularly important for a coding agent because the instructions establish persistent SDLC context rather than relying on developers to repeat requirements in every issue or prompt. GitHub explicitly describes repository instructions as a way to tell Copilot how to understand, build, test, and validate repository changes.
CODEOWNERS controls ownership and review assignment rather than Copilot behavior. A .copilotignore file is not the repository-wide custom-instructions mechanism, and agents.yml is not the prescribed file for persistent repository conventions.
Study Guide Reference Topics: Prepare agent architecture and SDLC processes; repository-level agent instructions; persistent development conventions; automated build and test guidance.
NEW QUESTION # 85
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-invocable 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:
NEW QUESTION # 86
Drag and Drop Question
Your company uses GitHub Copilot Enterprise.
Developers use GitHub Copilot agent mode in Microsoft Visual Studio Code on their laptops and Copilot Chat on github.com when they are away from their laptops.
When switching between environments, the developers notice that agent workflows lose continuity because the tools available in Visual Studio Code are unavailable on github.com.
You need to ensure that the agent tools and state are available consistently across environments and can be used from any device without local setup.
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:
Explanation:
Box 1: Use a Github-hosted Model Context Protocol (MCP) server for cloud-based workflows.
To ensure tools and states remain identical whether developers are on local laptops or on github.com (or GitHub Mobile), you must deploy Copilot Cloud Agents paired with remote cloud sessions.
Utilize Cloud Sessions: Shift developers away from local-only agent workflows. Cloud sessions run agents inside isolated, GitHub-hosted cloud environments rather than relying on local machine resources. This permits developers to resume, steer, and monitor active workflows from any device using the browser or the GitHub Copilot App.
Box 2: Enable MCP servers in Copilot policy
To ensure that agent tools and states are available consistently across all environments (including github.com) and can be used from any device without local setup, you must Enable MCP servers in Copilot policy.
Centralized Enforcement: Enabling the Model Context Protocol (MCP) policy at the enterprise or organization level allows administrators to define a central registry of approved tools.
Cross-Environment Continuity: Once the policy is active and servers are configured in the repository or organization settings, the cloud-based Copilot environment (github.com) natively inherits those exact tools. This removes dependency on individual laptop configurations or local extension states.
Reference:
https://docs.github.com/en/copilot/how-tos/administer-copilot/manage-for-enterprise/manage-agents/enable-copilot-cloud-agent
https://docs.github.com/en/copilot/how-tos/provide-context/use-mcp-in-your-ide/extend-copilot-chat-with-mcp
NEW QUESTION # 87
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