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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Perform evaluation, error analysis, and tuning | 15–20% | - Tune agent behavior based on evaluation results
|
| Topic 2: Prepare agent architecture and SDLC processes | 15–20% | - Configure observability and control for autonomous agents
|
| Topic 3: Implement tool use and environment interaction | 20–25% | - Integrate agents within development environments
|
| Topic 4: Orchestrate multi-agent coordination | 15–20% | - Detect and respond to multi-agent failures and degraded behavior
|
| Topic 5: Manage memory, state, and execution | 10–15% | - Ensure continuity of agent memory and state across tools and environments
|
| Topic 6: Implement guardrails and accountability | 10–15% | - Implement guardrails and human-in-the-loop workflows
|
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NEW QUESTION # 68
Your team wants Copilot's suggestions to reflect knowledge of internal library APIs that are not publicly documented and not present in the codebase being edited. What is the most appropriate solution?
Answer: A
Explanation:
An MCP server exposing the internal documentation is the appropriate solution because Model Context Protocol extends Copilot with information and capabilities located outside the repository's native context. GitHub describes MCP as a mechanism for integrating Copilot with external systems, enabling it to obtain context or invoke functionality that would otherwise be unavailable from the codebase alone.
This architecture is appropriate for proprietary library documentation because the internal API knowledge can remain centrally maintained rather than being duplicated into every repository. The MCP integration can expose a controlled documentation lookup/search capability, allowing Copilot to retrieve relevant definitions, usage patterns, or internal API information when required.
Option A is inferior because copilot-instructions.md is designed for concise repository-specific instructions and conventions, not as a substitute for a potentially large external documentation corpus. GitHub recommends it for persistent guidance such as building, testing, and repository conventions. Option C would remove context rather than add proprietary knowledge. Plan mode affects workflow sequencing and does not provide new external information.
Study Guide Reference Topics: Implement Tool Use and Environment Interaction; MCP-based context augmentation; external knowledge integration; tool-mediated retrieval.
NEW QUESTION # 69
You have a GitHub repository that uses GitHub Actions to run an autonomous coding-agent workflow.
Your team requires that session logs be captured as workflow artifacts so that agent runs can be audited and traced back to a specific execution and code state.
How should you complete the workflow? To answer, drag the appropriate values to the correct targets.
Answer:
Explanation:
Explanation:
Target
Value
Save logs action
actions/upload-artifact@v4
Artifact name
agent-session-${{ github.run_id }}-${{ github.sha }}
Artifact path
agent/session/
actions/upload-artifact@v4 captures the generated session-log directory as a workflow artifact. The correct path is .agent/session/, which contains the log file produced during the agent execution.
Using both github.run_id and github.sha in the artifact name links the artifact to a unique workflow execution and the exact source revision associated with that execution. This makes later audit and troubleshooting more reliable than a generic artifact name that may be repeated across runs.
actions/download-artifact retrieves an existing artifact and cannot upload current-session evidence. Storing the logs under .github/workflows/ would incorrectly identify the workflow-definition directory rather than the session-output directory.
Artifact retention should be configured according to the organization's audit requirements. The workflow should also avoid writing secrets into session logs before uploading them.
Study-guide topics: execution artifacts, traceability, workflow auditing, and durable session records.
NEW QUESTION # 70
You have a GitHub repository.
You use the GitHub Copilot CLI to run an agentic workflow from the terminal.
During execution, the conversation history approaches the context limit. The agent cannot continue the current session unless the amount of retained context is reduced.
You need to continue the current session without losing all the prior progress.
Which Copilot CLI slash command should you run?
Answer: A
Explanation:
You should run the /compact slash command.
Context Management in GitHub Copilot CLI/compact: This command triggers the compaction process manually. It takes a snapshot of your full conversation history, sends it to the AI model to generate a summary, and replaces the bulky history with that concise summary. This reduces token usage instantly while preserving prior progress.
Reference:
https://docs.github.com/en/copilot/how-tos/copilot-cli/use-copilot-cli/overview
NEW QUESTION # 71
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 # 72
A team assigns an issue to the GitHub Copilot coding agent by using the following one-line description: Fix the login bug.
Copilot creates a pull request, but the pull request is missing changes and has an incorrect scope.
How should you resolve the issue?
Answer: A
Explanation:
Adding a clear description of the problem to the issue will directly remedy this problem.Coding agents like GitHub Copilot rely heavily on the context, details, and constraints provided in the issue to understand what needs to be fixed. A one-line description like "Fix the login bug" is too vague, leading to guesswork, incorrect scope, and incomplete code changes.
Defines Scope: Explicitly stating what is broken prevents the agent from changing unrelated files.
Identifies the Root Cause: Providing error logs or steps to reproduce guides the agent to the exact lines of code that need fixing.
Sets Expectations: Specifying the expected correct behavior ensures the agent generates all the necessary changes, preventing missing code.
Reference:
https://docs.github.com/en/copilot/how-tos/use-copilot-agents/cloud-agent/troubleshoot-cloud-agent
NEW QUESTION # 73
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