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| Section | Weight | Objectives |
|---|---|---|
| Design agentic AI solutions | 25% | - Design agent architecture
|
| Integrate tools, data, and services | 25% | - Connect data sources and knowledge bases
|
| Test, deploy, and monitor agentic AI systems | 20% | - Validate agent performance and safety
|
| Implement agents and multi-agent systems | 30% | - Build agents with Azure AI tools and frameworks
|
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NEW QUESTION # 78
You need finer control, selecting specific files and describing precise natural-language changes to apply, rather than letting the agent decide the full scope of changes. Which Copilot Chat mode should you use?
Answer: A
Explanation:
The correct answer is Edit mode. GitHub Copilot's Edit mode is designed for situations where the developer wants granular control over which files may be changed and what modifications should be applied. In Edit mode, you explicitly select the working set of files, provide natural-language instructions describing the required changes, and then review the proposed edits before accepting or discarding them. GitHub describes Edit mode as appropriate for quick, specific updates to a defined set of files and for scenarios where the developer wants tighter control over the editing process.
Agent mode differs because Copilot determines which files and tools are required, can execute terminal commands, and iterates autonomously toward completing the task. Ask mode is intended primarily for explanations, questions, and code suggestions rather than coordinated file modification. Plan mode generates an implementation strategy before execution and is appropriate when the approach must be reviewed before coding begins.
Therefore, where the requirement explicitly emphasizes selecting specific files and prescribing precise edits rather than delegating scope determination to the agent, Edit mode provides the correct level of developer control.
Study Guide Reference Topics: Prepare agent architecture and SDLC processes; selecting appropriate Copilot interaction modes; controlled code modification; human-directed versus autonomous execution.
NEW QUESTION # 79
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 # 80
You use a GitHub Actions workflow to orchestrate a multi-agent draft-review process for pull requests. The workflow uploads a single combined review-package artifact at the end of each run.
You discover that the review-package artifact is occasionally missing from the workflow run history, and you suspect that a user deleted it.
You need to identify which user manually deleted a workflow run artifact and when the deletion occurred.
What should you use?
Answer: C
Explanation:
The artifact.destroy audit log event records manual deletion of workflow artifacts. It provides the event data needed to identify the actor responsible for the deletion and the time at which it occurred.
Pull-request comments and descriptions can describe review activity, but they are not authoritative evidence of artifact deletion. Merge commits only establish repository-history changes and do not record deletion of workflow artifacts. A checks-related log event is unrelated to artifact lifecycle management.
Audit records should be correlated with the affected repository, workflow run, artifact name, and timestamp. This produces a traceable explanation of why evidence from a particular run is no longer present. Where long-term retention is required, the organization should combine artifact policies with external audit-log retention or streaming.
Study-guide topics: artifact accountability, audit events, evidence retention, and workflow investigation.
NEW QUESTION # 81
After App1 is upgraded to meet the technical requirements, you need to validate the output.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 82
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 # 83
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