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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prepare agent architecture and SDLC processes | 15–20% | - Define boundaries between planning, reasoning, and action
|
| Topic 2: Implement tool use and environment interaction | 20–25% | - Integrate agents within development environments
|
| Topic 3: Implement guardrails and accountability | 10–15% | - Implement guardrails and human-in-the-loop workflows
|
| Topic 4: Manage memory, state, and execution | 10–15% | - Persist agent state and manage context drift
|
| Topic 5: Orchestrate multi-agent coordination | 15–20% | - Operate and manage multi-agent workflows
|
| Topic 6: Perform evaluation, error analysis, and tuning | 15–20% | - Tune agent behavior based on evaluation results
|
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NEW QUESTION # 93
You want a reusable, shareable set of instructions that defines a specific persona/workflow (e.g.,
"security reviewer") that developers can invoke on demand in Copilot Chat, rather than instructions applied globally to every session. What should you create?
Answer: D
Explanation:
Prompt files define reusable, invocable prompt templates for specific tasks or personas. Unlike copilot-instructions.md, which applies globally and automatically, prompt files are opt-in and triggered explicitly.
NEW QUESTION # 94
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 # 95
You have a GitHub Actions workflow that runs a multi-agent job. Each agent uploads its output as a workflow artifact.
A completed workflow run produces unexpected code changes, and the job logs do NOT show the agents' intermediate outputs.
You need to retrieve the agents' captured outputs from the completed run for post-hoc analysis.
What should you do in GitHub Actions?
Answer: D
Explanation:
The captured outputs were uploaded as workflow artifacts, so the correct action is to download those artifacts from the completed run's summary page. This retrieves the evidence produced during the execution that generated the unexpected changes.
Workflow artifacts and job logs contain different information. Logs record output emitted during workflow execution, while artifacts preserve files explicitly uploaded by workflow steps. An agent can therefore produce an intermediate report, proposed patch, or structured result that exists in an artifact even when its contents were never printed to the job log.
Rerunning with debug logging creates a new execution. Because agent behavior can vary with context, model responses, and repository state, a later run cannot substitute for examining the original captured outputs. Secret masking controls disclosure rather than evidence retrieval. Commit history shows committed changes but does not necessarily contain intermediate agent reasoning or generated files excluded from commits.
For useful error analysis, associate each downloaded output with its producing agent and the original workflow run.
Study-guide topics: post-execution analysis, artifact retrieval, and execution traceability. Reference: GitHub Actions-Downloading workflow artifacts.
NEW QUESTION # 96
Hotspot Question
You have a GitHub Enterprise organization that uses GitHub Copilot.
You discover that GitHub Copilot Chat responses in Microsoft Visual Studio Code are influenced by earlier, unrelated troubleshooting prompts from the same conversation.
You need to ensure that the Copilot Chat conversation context is limited to information relevant to the current work item. The solution must minimize effort.
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: Start a new chat.
Clear the chat session or start a new conversation thread.
Context Reset: Erases the short-term conversation history completely.
Zero Overhead: Requires no configuration changes or administrative interventions.
Fresh State: Forces Copilot to focus only on newly provided code and prompts.
Box 2: Open related files and close unrelated files.
To keep GitHub Copilot Chat focused on the current work item with the minimum amount of effort, you should open related files and close unrelated files.
GitHub Copilot Chat automatically uses the open files and active tabs in your IDE as its immediate context. By closing unrelated files and keeping only relevant code files open, you instantly clean up the context window and force Copilot to focus solely on your current task without needing to modify complex settings.
Reference:
https://docs.github.com/en/copilot/how-tos/troubleshoot-copilot/troubleshoot-common-issues
NEW QUESTION # 97
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: B
Explanation:
To live stream the session log output of a background agent, you must run the gh agent-task view
<task-id> --log --follow command.
Command Syntax
gh agent-task view <task-id> --log --follow
Parameter Breakdown
<task-id>: The unique identifier of the stalled agent task. If you do not know the ID, run gh agent- task list first to look it up.
--log: Instructs the GitHub CLI to pull the specific execution and reasoning logs rather than just a summary of the task state.
--follow: Tells the CLI to keep the connection open and live stream new log events to your terminal in real-time as they happen.
Reference:
https://docs.github.com/en/copilot/how-tos/copilot-on-github/use-copilot-agents/manage-and-track-agents
NEW QUESTION # 98
......
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