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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Test, deploy, and monitor agentic AI systems | 20% | - Validate agent performance and safety
|
| Topic 2: Integrate tools, data, and services | 25% | - Connect data sources and knowledge bases
|
| Topic 3: Implement agents and multi-agent systems | 30% | - Orchestrate multi-agent collaboration
|
| Topic 4: Design agentic AI solutions | 25% | - Define requirements for agentic systems
|
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NEW QUESTION # 45
You have a 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.
Answer:
Explanation:
NEW QUESTION # 46
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 # 47
In Microsoft Visual Studio Code, you are using GitHub Copilot Chat to generate documentation for a new feature.
Earlier in the day, you used Copilot Chat extensively for an unrelated refactoring task.
You discover that the Copilot responses for the new documentation task are influenced by the earlier conversation.
You need Copilot to focus only on the current task and avoid using prior conversational context. The solution must NOT affect other conversations.
What should you do?
Answer: E
Explanation:
Starting a new Copilot Chat conversation establishes a separate conversational context for the documentation task. The unrelated refactoring exchanges remain in their original conversation instead of continuing to influence the new thread. This directly addresses the requirement to separate tasks while preserving other conversations.
Conversation history contributes information beyond the latest prompt. Earlier instructions, assumptions, terminology, and design decisions can continue to affect subsequent responses within the same session. A fresh conversation provides a clear task boundary, allowing the developer to supply the feature requirements and relevant documentation context explicitly.
Changing from sidebar chat to inline chat changes the interaction surface; it does not provide the same explicit separation of conversational history. Referencing a file supplies relevant context but does not reliably remove earlier instructions. The maximum requests setting governs agent execution limits rather than conversation isolation. Clearing history is also unnecessary when the previous work can remain available in its own session.
A new conversation can still receive applicable repository instructions and deliberately supplied context; it is the previous conversation that is separated.
Study-guide topics: conversational state, context isolation, and task boundaries. Reference: VS Code-Manage agent sessions.
NEW QUESTION # 48
You have a GitHub Copilot Enterprise subscription. Developers use Microsoft Visual Studio Code and GitHub Copilot.
The developers want to share a custom agent that can review code changes but cannot modify files.
You need to prevent the agent from modifying repository content and appearing in the agents dropdown.
How should you configure the YAML frontmatter for the agent? 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 # 49
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:
You should check the artifact.destroy audit log event.
This specific event tracks whenever a user or a token manually deletes a workflow run artifact. It provides exactly what you need to troubleshoot why your multi-agent review packages are disappearing.
Reference:
https://docs.github.com/en/organizations/managing-organization-settings/configuring-the-retention-period-for-github-actions-artifacts-and-logs-in-your-organization
NEW QUESTION # 50
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