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| Section | Weight | Objectives |
|---|---|---|
| Manage memory, state, and execution | 10–15% | - Ensure continuity of agent memory and state across tools and environments
|
| Orchestrate multi-agent coordination | 15–20% | - Detect and respond to multi-agent failures and degraded behavior
|
| Implement tool use and environment interaction | 20–25% | - Configure MCP servers
|
| Prepare agent architecture and SDLC processes | 15–20% | - Configure observability and control for autonomous agents
|
| Perform evaluation, error analysis, and tuning | 15–20% | - Analyze agent failures and identify root causes
|
| Implement guardrails and accountability | 10–15% | - Implement guardrails and human-in-the-loop workflows
|
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NEW QUESTION # 45
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 # 46
You have a GitHub Enterprise Cloud organization that uses the GitHub Copilot coding agent to resolve issues asynchronously.
When an issue is assigned to GitHub Copilot, the agent creates a draft pull request, but your team cannot always tell whether the agent is actively working, has completed its session, or is awaiting workflow approval.
Which execution context does each signal indicate? To answer, drag the appropriate context to the correct signals.
Answer:
Explanation:
NEW QUESTION # 47
Drag and Drop Question
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:
Explanation:
Box 1: Use the upload-artifact action and configure artifact retention in the CI workflow To meet this requirement you should use the actions/upload-artifact action and configure the retention-days property in your GitHub Actions CI workflow.
By default, GitHub retains workflow artifacts for a maximum of 90 days for public and private repositories (customizable down to 1 day). Utilizing these configurations ensures that human reviewers can download and inspect the agent's background outputs long after the initial execution concludes.
Box 2: Assign an issue and wait for the agent to view.
The best action is to assign an issue and wait for the agent to view.
Assigning a GitHub issue to the Copilot coding agent triggers it to autonomously start working on the background task. While investigating code and implementing the required fixes, it provides real-time, human-reviewable progress updates directly within the issue or pull request timeline (such as showing a "Copilot has started work" event, or tracking its steps inside the agent workflow). This completely satisfies the requirement for ongoing, transparent signaling for reviewers.
Box 3: Select View session to stream live agent logs
You should select "View session" (or navigate to the Agents tab) on GitHub to stream live agent logs.
When using the GitHub Copilot cloud agent asynchronously to resolve issues and generate draft pull requests, the process occurs entirely in a GitHub-hosted background environment. While automated status summaries eventually populate the pull request timeline, real-time auditing during an active run requires looking directly at the active session stream.
Reference:
https://docs.github.com/en/organizations/managing-organization-settings/configuring-the-retention-period-for-github-actions-artifacts-and-logs-in-your-organization
https://docs.github.com/en/copilot/how-tos/use-copilot-agents/cloud-agent/troubleshoot-cloud-agent
NEW QUESTION # 48
You have a GitHub repository that stores custom GitHub Copilot agents in the .github/agents/ folder.
You have an agent named lead-dev.agent.md that must invoke a specialist subagent named security-reviewer.
Evaluation results show that lead-dev attempts to invoke security-reviewer, but the subagent call fails to run.
You need to ensure that lead-dev can invoke security-reviewer.
How should you complete the YAML frontmatter of the agent profile? To answer, drag the appropriate values to the correct targets.
Answer:
Explanation:
NEW QUESTION # 49
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 resolve the issue of the agents generating conflicting output. The solution must meet the implementation guidelines.
What should you do?
Answer: D
Explanation:
Scenario: Both agent1 and agent2 modified shared/config.yaml in a parallel test run, generating conflicting outputs.
To avoid race conditions and conflicting outputs when multiple Copilot agents modify the same shared/config.yaml file in parallel, the best practice is to configure a concurrency group on both agent workflows so that only one workflow runs at a time.
Workflow Concurrency
Using a concurrency group locks the shared resource. If one Copilot agent is already processing and modifying shared/config.yaml, the second agent's workflow will either queue or be canceled, preventing concurrent writes and eliminating file overwrites.
Incorrect:
Branch isolation is useful for developer edits, but in automated parallel agent operations, it still leads to frequent and painful merge conflicts if both agents make asynchronous structural updates to the same config file.
Reference:
https://docs.github.com/en/actions/how-tos/write-workflows/choose-when-workflows-run/control-workflow-concurrency
NEW QUESTION # 50
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