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| Section | Weight | Objectives |
|---|---|---|
| Test, deploy, and monitor agentic AI systems | 20% | - Validate agent performance and safety
|
| Implement agents and multi-agent systems | 30% | - Orchestrate multi-agent collaboration
|
| Design agentic AI solutions | 25% | - Define requirements for agentic systems
|
| Integrate tools, data, and services | 25% | - Incorporate external tools and APIs
|
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NEW QUESTION # 70
You have a GitHub Enterprise repository.
An agent opens pull requests to the main branch.
You need to ensure that changes to .github/workflows/* and /infra/* require approval from designated reviewers before merge.
What should you configure?
Answer: D
Explanation:
To secure these specific directories in your GitHub Enterprise repository, you need to configure CODEOWNERS combined with a Repository Branch Protection Rule.
Reference:
https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/reviewing-changes-in-pull-requests/approving-a-pull-request-with-required-reviews
NEW QUESTION # 71
You have a repository that uses the GitHub Copilot coding agent and supports hooks stored under .github/hooks.
You need a Shell command to run automatically whenever an agent execution fails.
Which type of hook should you use?
Answer: D
Explanation:
To automatically run a Shell command whenever a GitHub Copilot coding agent execution fails, you should use the errorOccurred (also referred to as onErrorOccurred) hook.
Hook Mechanics & ConfigurationGitHub Copilot agent hooks are defined using JSON configuration files placed in the .github/hooks/ directory.
Event Type: errorOccurred (or onErrorOccurred depending on your specific environment and version).
Execution Behavior: When an execution fails, the agent stops, triggers this hook, and passes detailed error metrics and session context as a JSON payload to the script's standard input (stdin).
Incorrect:
[Not B]
sessionEnd - Agent session completes or is terminated.
Reference:
https://awesome-copilot.github.com/learning-hub/automating-with-hooks/
NEW QUESTION # 72
You have multiple GitHub Copilot coding agents that run tasks concurrently.
You are monitoring the agents from the terminal by using the GitHub CLI.
An agent appears stalled.
You need to live stream the session log output.
What should you do?
Answer: D
Explanation:
The gh agent-task view command supports inspecting an individual agent task. Combining --log with --follow selects session log output and continues following that output as the session progresses. The official CLI reference describes these flags as "Show agent session logs" and "Follow agent session logs," respectively.
This combination is appropriate when an agent appears stalled because a task-level status alone cannot explain what the agent is currently doing. Following the log allows the operator to observe additional activity, identify the last reported operation, and distinguish ongoing execution from a task awaiting intervention.
The list command is useful for discovering tasks and obtaining an overview of concurrent activity. It does not replace following the detailed output of the relevant session. The --jq parameter filters structured JSON output; it does not provide the continuous log-following behavior requested here. Opening a browser with a web option also fails the terminal streaming requirement.
Viewing logs is an observational action. It does not itself restart, cancel, or unblock the agent.
Study-guide topics: monitoring concurrent agents, execution visibility, and troubleshooting. Reference: GitHub CLI-gh agent-task view.
NEW QUESTION # 73
Drag and Drop Question
You have a GitHub repository that runs an agentic software development lifecycle (SDLC) workflow by using GitHub Actions. The workflow uses the following three executors implemented as scripts: spec_analyzer, risk_reviewer, and plan_merger.
You need to coordinate multiple specialized agents so that analysis and risk review run in parallel, and then a final executor merges the outputs into a single plan. The orchestration pattern must fan out one request to multiple executors, and then fan in the results to a final executor.
How should you complete the workflow definition? 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: needs
Needs & [spec_analyzer, risk_reviewer] forces plan_merger to wait until both the analysis and risk review jobs have successfully executed in parallel, completing the fan-out portion of your pipeline.
Box 2: [spec_analyzer, risk_reviewer]
Box 3: concurrency
The top-level key prevents race conditions by grouping active workflow runs together. Using
${{ github.run_id }} (replacing the typo ${{ github.reg }}) correctly locks concurrent executions for the specific pipeline run.
Reference:
https://github.com/openclaw/openclaw/issues/38433
NEW QUESTION # 74
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:
NEW QUESTION # 75
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