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| Section | Weight | Objectives |
|---|---|---|
| Implement agents and multi-agent systems | 30% | - Orchestrate multi-agent collaboration
|
| Design agentic AI solutions | 25% | - Define requirements for agentic systems
|
| Test, deploy, and monitor agentic AI systems | 20% | - Validate agent performance and safety
|
| Integrate tools, data, and services | 25% | - Connect data sources and knowledge bases
|
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NEW QUESTION # 47
Drag and Drop Question
Your team uses a remote GitHub Model Context Protocol (MCP) server for workflows in the software development life cycle (SDLC).
You need to commit a workspace-scoped MCP configuration to ensure that GitHub Copilot can connect to the GitHub-hosted MCP endpoint and authenticate by using a GitHub personal access token (PAT).
How should you complete the mcp.json configuration file? To answer, drag the appropriate values to the correct targets. Each value 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: http
Type (http): The Model Context Protocol (MCP) configuration requires specifying the transport layer type. Since you are communicating via an API endpoint URL (https://...), the transport type is http (or in some implementations, standard variants like sse or streamable-http).
Box 2: Bearer
Authorization (Bearer ): GitHub's API endpoints authenticate Personal Access Tokens using the standard HTTP Bearer token schema. Note that a space is required between the word Bearer and the token variable itself.
Reference:
https://docs.github.com/en/copilot/how-tos/copilot-on-github/customize-copilot/configure-mcp-servers
NEW QUESTION # 48
You need to enable Copilot memory to support the planned changes.
What should you configure?
Answer: A
NEW QUESTION # 49
You have a GitHub Enterprise Cloud organization that uses the GitHub Copilot coding agent.
Copilot creates a draft pull request for an assigned issue, and the pull request timeline shows Copilot started work.
After 70 minutes, the agent session log stops updating, and the pull request body status stops changing.
You need to restart the agent so that it continues the task from the issue context and produces new commits to the existing draft pull request.
What should you do?
Answer: C
Explanation:
Unassigning and reassigning the issue is GitHub's documented retry action for an issue-driven agent session that remains stuck and times out. The troubleshooting guidance states that a persistently stuck session times out after an hour. At 70 minutes with no continuing activity, the scenario therefore calls for a fresh execution trigger rather than indefinite waiting.
Reassignment reissues the task through its original issue context. This makes C the appropriate selection among the available actions. The issue should contain current requirements so that the retry begins with the intended scope rather than repeating an outdated assignment.
Option B is incorrect because a draft pull request should not be merged simply to restart implementation, and Copilot does not respond to new mentions on merged or closed pull requests. Option D authorizes repository workflow execution rather than restarting the agent.
For operational precision, reassignment is a documented retry mechanism, not a universal guarantee that every retry reuses the same pull request. Where preserving a particular pull-request branch is essential, an authorized @copilot comment on that open pull request directly targets it.
Relevant curriculum topics are timeout recovery, task reinitialization, and preserving execution continuity.
Reference:
NEW QUESTION # 50
Case Study 2
Existing Environment
GitHub Environment
The GitHub environment contains the following:
- Three repositories named product-api, billing-service, and infra-terraform.
- Branch protection on the main branch in all repositories that requires at least one pull request review before merging
- GitHub Actions runners used across all workflows
- A GitHub team named SG_Dev that contains developers
- A GitHub team named SG_Review that contains senior engineers and a security team
- A .github/copilot-instructions.md file that includes general coding conventions for all features Agent environment The product-api repository uses a GitHub Copilot coding agent named agent1 that has the following configurations:
- No custom agent profile is defined.
- A Model Context Protocol (MCP) server named MCP1 is deployed to
https://mcp.litwareinc.internal and provides access to internal ticketing and deployment APIs.
MCP1 requires an API key for authentication.
A second Copilot coding agent named agent2 handles changes in infra-terraform and runs in parallel with agent1 when both agents have open assigned issues.
Copilot memory is NOT enabled for the organization.
Problem Statements
Litware identifies the following issues:
- During two recent sessions, agent1 accessed files in billing-service, which is outside the agent's intended scope.
- agent1 makes code changes immediately after receiving a task.
- A developer named Ben, who is on the SG_Dev team, reports that agent1 completed a session with a successful status and opened a pull request, but the pull request contains no file changes.
Other developers report this intermittently as well.
- Both agent1 and agent2 modified shared/config.yaml in a parallel test run, generating conflicting outputs.
agent1 consistently uses raw try-catch blocks for error handling, which violates the defined implementation guidelines of SG_Dev.
Requirements
Planned Changes
Litware plans to make the following changes:
- Ensure that agent1 can access all the tools in the environment.
- Provide product-api with specific instructions to agent1 without affecting Copilot Chat or Copilot code review.
- Configure MCP1 as a tool for agent1 by modifying the product-api repository MCP configuration.
- Ensure that Copilot retains details that it has learned and uses that knowledge for future work.
This must be applied to all licensed members of the organization.
Implementation guidelines
The development team at Litware identifies the following implementation guidelines:
- Agent workflows must be able to run in parallel.
- Application error handling must use the repository ErrorHandler class.
- agent1 and agent2 must run on isolated branches during parallel execution. File-level conflicts must be detected before merges, and both agents must be able to run concurrently.
Security requirements
Litware identifies the following security requirements:
- Only the members of SG_Review must be able to approve agent1 plan outputs.
- All API keys must be stored and accessed securely.
- The developers must NOT be able to self-approve.
Agent configuration
You need to troubleshoot the issue reported by Ben.
What should you review?
Answer: C
Explanation:
Scenario: A developer named Ben, who is on the SG_Dev team, reports that agent1 completed a session with a successful status and opened a pull request, but the pull request contains no file changes. Other developers report this intermittently as well.
To investigate a successful Copilot agent session that created an empty pull request, you should check the agent session log in the Agents panel.
Internal behavior: The empty pull request means the agent completed its logical execution loop without generating or committing code modifications.
Session context: The agent session log contains the specific LLM prompts, tool execution outputs, and file parsing steps that explain why the agent decided no changes were necessary.
Reference:
https://github.blog/ai-and-ml/github-copilot/whats-new-with-github-copilot-coding-agent/
NEW QUESTION # 51
You have a GitHub repository that uses the GitHub Copilot coding agent.
The agent is assigned to a long-running issue and has already opened a draft pull request linked to the issue. The pull request timeline shows Copilot started work and the pull request description shows periodic status updates.
After 70 minutes, the pull request stops receiving new commits, and the agent session log indicates that the session has timed out. In a pull request comment thread, the agent then proposes changes that no longer match the latest repository guidance.
You need to resume execution in a way that reestablishes the correct context and produces new commits in the existing draft pull request.
What should you do?
Answer: B
Explanation:
A comment mentioning @copilot on the existing pull request provides the direct continuation mechanism. GitHub documents that this can start a new agent session and, by default, push additional commits to the pull request's branch. The comment should explicitly identify the latest repository guidance and the work that remains, correcting the context that produced the outdated proposal.
This approach preserves the existing branch, accumulated changes, review discussion, and relationship to the original issue. It also gives the agent a clear instruction tied to the artifact that must be updated. The commenter must have the required repository write access.
Creating another issue would establish a separate assignment and could duplicate work. Approve and run workflows authorizes GitHub Actions validation; it does not itself resume the coding agent's implementation session. Closing and reopening the issue is likewise not the direct pull-request continuation control.
The useful recovery action therefore combines a new execution trigger with an explicit statement of current intent. Restarting without correcting the outdated requirement could reproduce the same drift.
Relevant curriculum topics are resuming execution, restoring task context, and maintaining continuity without discarding prior work.
Reference:
NEW QUESTION # 52
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