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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Perform evaluation, error analysis, and tuning | 15–20% | - Define metrics and quality standards for outputs - Diagnose failures, hallucinations, and unexpected behavior - Optimize prompts, tools, and behavior through iteration - Test, validate, and compare agent results |
| Topic 2: Manage memory, state, and execution | 10–15% | - Implement memory cleanup and expiration rules - Handle execution flow, retries, and interruptions - Choose memory types: short-term, long-term, external - Scope and persist agent state correctly |
| Topic 3: Prepare agent architecture and SDLC processes | 15–20% | - Integrate agents into software development lifecycle - Plan agent deployment, monitoring, and maintenance - Define agent purpose, scope, and success criteria - Design agent autonomy and decision boundaries |
| Topic 4: Implement guardrails and accountability | 10–15% | - Add validation, review, and approval gates - Enforce least privilege and security boundaries - Log actions, decisions, and changes for audit - Ensure compliance, safety, and responsible use |
| Topic 5: Orchestrate multi-agent coordination | 15–20% | - Prevent conflicts and manage shared resources - Design workflows for multiple agents - Define communication and handoff protocols - Monitor and troubleshoot multi-agent execution |
| Topic 6: Implement tool use and environment interaction | 20–25% | - Connect agents to codebase, APIs, and external systems - Manage permissions and environment access - Configure and extend GitHub Copilot agents - Implement tools, custom actions, and MCP servers |
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NEW QUESTION # 89
Drag and Drop Question
You have a GitHub repository that uses GitHub Actions for CI on pull requests.
You have a GitHub Copilot coding agent that opens pull requests for backlog items, and your company requires automated checks for agent-generated changes.
You plan to standardize success criteria so that pull requests created by agents only succeed when unit tests pass and CodeQL analysis completes.
You need to configure a GitHub Actions workflow that runs on pull requests, executes unit tests, and performs CodeQL analysis.
How should you complete the workflow? 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: npm test
npm test (or mvn test, pytest, dotnet test depending on your language stack).
The run keyword requires a shell command to execute your test runner.
Box 2: init
The CodeQL workflow must initialize its database and environment before building code. The official action for this is github/codeql-action/init.
Box 3: analyze
After the code is prepared (and built if using a compiled language), the final step scans the codebase and uploads the SARIF results to GitHub Security. The official action is github/codeql- action/analyze.
Reference:
https://gitprotect.io/blog/github-actions-how-to-use-it-for-security-and-compliance-needs/
NEW QUESTION # 90
Hotspot Question
You have a GitHub repository that uses GitHub Actions to validate pull requests opened by the GitHub Copilot coding agent. The workflow runs unit tests and a linter on pull request triggers, and Copilot opens draft pull requests on dedicated branches while iterating by using commits.
You discover that when multiple Copilot sessions push updates to the same pull request branch in quick succession, multiple workflow runs execute concurrently.
You need to enable parallel workflow executions across different pull request branches.
How should you configure workflow-level concurrency? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Set the concurrency at the workflow level
Isolates this concurrency rule to this specific workflow so it does not accidentally cancel other automation types.
Box 2: group:${{ github.head_ref || github.run_id}}
${{ github.head_ref }}: Evaluates to the source branch name during pull_request triggers, grouping all consecutive Copilot pushes to that specific branch together.
|| github.run_id: Serves as a fallback for non-PR triggers (like a direct push to main), ensuring the workflow still runs safely without canceling itself.
Reference:
https://www.meziantou.net/how-to-cancel-github-workflows-when-pushing-new-commits-on-a-branch.htm
NEW QUESTION # 91
You have a GitHub repository that uses a GitHub Actions workflow to run an agent-driven change plan as part of a CI pipeline. The workflow generates an artifact named plan.json that includes a field named risk. risk has possible values of low, medium, or high.
You need to ensure that a human must confirm the execution of the workflow when risk is medium or high. The workflow must proceed automatically only when risk is low.
How should you complete the workflow? 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:
NEW QUESTION # 92
You have a GitHub Copilot coding agent named CodeAgent. The .agent.md file of CodeAgent contains the following YAML frontmatter.
---
name: CodeAgent
description: Performs repository analysis and code review tasks.
tools: ['edit', 'execute', 'read', 'search']
---
You need to issue a GitHub Copilot CLI command that preserves execution velocity for read-only tasks by eliminating approval prompts for low-risk tools. The solution must ensure that high-risk tools that can make changes remain available but still require explicit user approval before running.
Which command should you run?
Answer: A
Explanation:
To configure your GitHub Copilot agent (CA) to run low-risk, read-only tools without approval prompts while keeping high-risk tools strictly gated, you need to use the gh copilot CLI configuration command to set specific tool permissions.
gh copilot config set-agent-policy CA --allow-without-approval read search --require-approval edit execute Preserves Velocity: Bypasses confirmation prompts for read and search so code analysis runs instantly.
Maintains Security: Explicitly forces a manual user prompt before edit (modifying code) or execute (running commands) can run.
Targets the Agent: Uses the --allow-without-approval and --require-approval flags specifically mapped to the CA agent name declared in your YAML frontmatter.
Reference:
https://vivekfordevsecopsciso.medium.com/github-copilot-remote-code-execution-via-prompt-injection-cve-2025-53773-38b4792e70fb
NEW QUESTION # 93
You need to enable Copilot memory to support the planned changes.
What should you configure?
Answer: A
NEW QUESTION # 94
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