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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: Implement guardrails and accountability | 10–15% | - Implement guardrails and human-in-the-loop workflows
|
| Topic 3: Prepare agent architecture and SDLC processes | 15–20% | - Configure observability and control for autonomous agents
|
| Topic 4: Implement tool use and environment interaction | 20–25% | - Select and configure agent tools
|
| Topic 5: Manage memory, state, and execution | 10–15% | - Persist agent state and manage context drift
|
| Topic 6: Orchestrate multi-agent coordination | 15–20% | - Detect and respond to multi-agent failures and degraded behavior
|
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NEW QUESTION # 101
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 # 102
You have a GitHub Copilot Enterprise subscription.
Developers use Microsoft Visual Studio Code.
You need to configure an array of subagents that can be invoked by an agent.
What should you configure in the YAML frontmatter for the agent?
Answer: C
Explanation:
The agents frontmatter property defines the custom agents that the current agent can invoke as subagents. It establishes the delegation boundary for an orchestrator or other coordinating agent.
The tools property grants operational capabilities such as file access, terminal commands, MCP tools, or the agent invocation tool itself. It does not identify the specific custom-agent profiles available for delegation. The mcp-servers property configures external MCP servers; it does not define subagent membership.
Handoffs are user-facing transitions from one custom agent to another. They are useful for moving a conversation from planning to implementation but are not the configuration mechanism for an agent's available subagent array. The target field identifies an execution target or context and is unrelated to delegation membership.
For a reliable orchestration design, include only the required specialized agents in the agents list and ensure the parent agent has the appropriate agent-invocation capability.
Study-guide topics: subagent lists, delegation boundaries, orchestrator design, and multi-agent coordination.
NEW QUESTION # 103
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
Drag and Drop Question
You need to implement the security requirements for agent1.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
Scenario:
Security requirements, Litware identifies the following security requirements: Only the members of SG_Review must be able to approve agent1 plan outputs.
Agent environment: The product-api repository uses a GitHub Copilot coding agent named agent1 Step 1: In the product-api repositary settings,..
Create a new environment and add SG_review as the required reviewers.
Step 2: To the agent1 workflow,..
Create a new workflow related to the plan outoput.
Step 3: Add an execute job...
Create a job referencing the plan and the environment.
NEW QUESTION # 104
You have a GitHub repository.
Developers use the GitHub Copilot CLI and repository-scoped hooks under .github/hooks/*.json.
You need to allow the Copilot CLI to automatically run low-risk Bash commands. The solution must prevent the autonomous execution of high-risk commands, such as sudo, rm -rf /, and curl ... | bash.
What should you do?
Answer: A
Explanation:
To safely restrict high-risk commands while allowing low-risk execution, you must configure a preToolUse hook that returns permissionDecision: "deny" when a restricted pattern is detected.
The preToolUse lifecycle hook executes immediately before GitHub Copilot CLI attempts to invoke any tool or execute a command. It evaluates the tool arguments and can programmatically intercept execution by writing a specific JSON payload to stdout. Returning
{"permissionDecision": "deny"} explicitly instructs Copilot CLI to block the autonomous execution of that command.
Reference:
https://code.visualstudio.com/docs/agent-customization/hooks
NEW QUESTION # 105
An agentic coding session in the terminal has grown too long, and you want to discard all prior conversation history and start clean, without summarization. Which slash command should you run?
Answer: A
Explanation:
/clear wipes the entire conversation history and starts a fresh session. This differs from /compact, which summarizes history to save tokens while retaining prior progress.
NEW QUESTION # 106
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