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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Design agentic AI solutions | 25% | - Design agent architecture
|
| Topic 2: Integrate tools, data, and services | 25% | - Connect data sources and knowledge bases
|
| Topic 3: Test, deploy, and monitor agentic AI systems | 20% | - Validate agent performance and safety
|
| Topic 4: Implement agents and multi-agent systems | 30% | - Build agents with Azure AI tools and frameworks
|
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NEW QUESTION # 87
Hotspot Question
You have a GitHub repository that uses the GitHub Copilot coding agent to resolve issues and create draft pull requests.
You assign an issue to Copilot. Copilot creates a draft pull request. The pull request timeline shows Copilot started work, followed by status updates. The most recent status update is 55 minutes old.
You discover that the agent is no longer making progress.
You need to ensure that the work resumes without redoing the completed steps or changing the previously chosen approach.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Select View session from the pull request
To best confirm the agent's progress, troubleshoot the stagnation, and ensure the work resumes seamlessly from where it left off, you should interact directly with the agent's active workspace.
Open the Session View: Navigate to the pull request on GitHub and click the View session button.
This opens the step-by-step agent workflow and reasoning interface.
Inspect the Logs: Review the detailed execution log in the session timeline to identify exactly where the background task (running in GitHub Actions) stalled or encountered an error.
Steer or Prompt to Resume: Instead of unassigning and reassigning the issue (which completely restarts the task from scratch), use the chat input or steering tools directly inside the View session page or Agents panel to nudge Copilot. Providing a clarification prompt allows the agent to continue its current approach without losing completed steps.
Box 2: Post a pull request comment that mentions @copilot
You can resume the work by commenting on the pull request and mentioning @copilot, along with a prompt instructing it to continue.When a GitHub Copilot coding agent stalls or pauses on a draft pull request, posting a comment mentioning @copilot wakes the agent back up. Because the agent evaluates the entire conversation history, code changes, and task checklists within that pull request lifecycle, it will resume from its last saved state. It maintains the previously chosen approach and carries on with the remaining items without repeating already completed steps.
Reference:
https://docs.github.com/en/copilot/how-tos/use-copilot-agents/cloud-agent/troubleshoot-cloud-agent
NEW QUESTION # 88
You have a GitHub Actions workflow that runs GitHub Copilot-driven integration tests across multiple jobs.
You need to persist the agent memory/state so that it remains available across the jobs.
What should you do?
Answer: C
Explanation:
Workflow artifacts provide an explicit mechanism for transferring serialized agent state between jobs. The producing job writes the required state to files and uploads those files as an artifact. Subsequent jobs download the artifact and load the state before continuing the integration-test workflow.
This is necessary because jobs do not automatically share process memory or a common workspace. They may execute on different runners, and even jobs assigned to similar runner types must not depend on incidental machine reuse. Uploading the state establishes a deliberate persistence boundary rather than relying on temporary execution infrastructure.
Committing state after every job introduces repository mutations merely to transport workflow data. Environment variables are also unsuitable as a general cross-job state store: setting a variable in one job does not automatically expose it in another, and complex state is better represented in structured files.
The application must serialize the information needed for continuation, such as completed steps, relevant decisions, and test progress. Uploading an arbitrary directory does not automatically restore a running agent process.
Relevant curriculum topics are durable state, cross-environment continuity, and resuming work without repeating completed steps.
Reference:
NEW QUESTION # 89
You have a GitHub Actions workflow that starts multiple agent jobs in parallel.
You need to prevent two runs in the same job from executing simultaneously on the same branch. The solution must ensure, at most, one running instance and one pending instance per branch.
What should you add to the job?
Answer: A
Explanation:
A job-level concurrency group coordinates executions that share the same group value across workflow runs. The expression ci-${{ github.ref }} places executions for the same ref into the same group while allowing other refs to use independent groups.
Under the default concurrency behavior, a group permits at most one running execution and one pending execution. When another execution enters an occupied group, it becomes pending; an existing pending execution can be replaced by the newer one. This is a concurrency limit rather than a guarantee that every queued execution will eventually run.
The strategy.max-parallel setting controls simultaneous jobs generated by a matrix within a workflow run. It does not establish the required coordination between separate workflow runs. A matrix definition also does not create a shared concurrency lock. The cancel-in-progress property controls whether an active execution is cancelled, but a cancellation expression alone does not define the necessary group.
Group names should be chosen carefully when multiple jobs or workflows share a repository, because identical names can coordinate those executions too.
Study-guide topics: job synchronization, concurrency groups, and bounded execution. Reference: GitHub Actions-Workflow syntax: job concurrency.
NEW QUESTION # 90
During an agentic session, you want to see exactly how many tokens are being consumed by the system prompt, tools, and message history before deciding whether to compact. Which command should you run?
Answer: D
Explanation:
/context displays a breakdown of the active context window, including tokens used by the system prompt, MCP/system tools, and message history, along with remaining free space.
NEW QUESTION # 91
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: C
Explanation:
Edit mode is designed for targeted, developer-directed changes: you select the files in scope and describe the specific modification, giving you more granular control than the fully autonomous agent mode.
NEW QUESTION # 92
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