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| Section | Weight | Objectives |
|---|---|---|
| Design agentic AI solutions | 25% | - Design agent architecture
|
| 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% | - Connect data sources and knowledge bases
|
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NEW QUESTION # 89
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 # 90
You have a GitHub Enterprise organization that has Copilot memory enabled.
You create a new repository.
What are two ways that memories will be deleted from the repository? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: B,D
Explanation:
The two ways repository-level memories will be deleted or removed from a repository are automatic time-based expiration and manual deletion by a repository administrator.
1. Automatic 28-Day Expiration
GitHub Copilot Memory utilizes an automated lifecycle policy to prevent stale knowledge from remaining in your codebase indefinitely. Any repository-level facts captured by Copilot will automatically expire and be deleted after 28 days if they are not actively updated or verified against the codebase.
2. Manual Admin Deletion
Repository administrators can manually curate and erase stored memories directly from the GitHub interface.
Reference:
https://docs.github.com/en/copilot/how-tos/use-copilot-agents/copilot-memory/manage-as-administrator
NEW QUESTION # 91
A team assigns an issue to the GitHub Copilot coding agent by using the following one-line description: Fix the login bug.
Copilot creates a pull request, but the pull request is missing changes and has an incorrect scope.
How should you resolve the issue?
Answer: A
Explanation:
The primary failure is insufficient task definition. "Fix the login bug" does not identify the observed behavior, expected behavior, reproduction conditions, affected component, or completion criteria. The agent must infer these details, creating a substantial risk of incomplete changes or work outside the intended scope. A clear issue description supplies the information needed to construct and implement an appropriate plan.
For this scenario, the description should identify how the login failure occurs, which authentication path is affected, what successful behavior looks like, and which existing behavior must remain intact. Relevant error messages, reproduction steps, and expected tests make the task independently verifiable. These details turn an ambiguous request into a bounded engineering assignment.
Enabling memory does not supply missing requirements reliably. MCP rate limits govern service interaction rather than task clarity. Additional setup resources address environmental capacity or dependency preparation, not uncertainty about what must change.
The corrective action therefore belongs at task intake, where developers define the agent's inputs and success conditions. The relevant curriculum topics are defining agent inputs, outputs, and success criteria and mitigating poorly scoped task assignments.
Reference:
NEW QUESTION # 92
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 # 93
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: C
Explanation:
The filter action:artifact.destroy selects the audit event associated with manual deletion of a workflow artifact. It directly targets the activity described in the scenario, allowing the investigator to examine the recorded actor and associated event details.
A repository filter narrows the search to a particular repository but does not distinguish artifact deletion from other recorded operations. It can usefully accompany the action filter when investigating a specific project. A workflow-run action concerns workflow execution rather than the manual removal of its stored artifacts. The generic operation:remove option does not select the documented artifact deletion event.
The investigation should correlate the deletion event with its timestamp and repository information, then inspect the actor recorded for that event. This identifies the account associated with the action; additional context may be needed when an application or automated identity performed it.
Artifact retention and audit logging serve separate purposes. Retention controls how long outputs are normally preserved, while the audit log records relevant administrative and user activity. Searching the audit log does not restore deleted content.
Study-guide topics: accountability, evidence preservation, actor attribution, and audit trails. Reference: GitHub-Organization audit log events.
NEW QUESTION # 94
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