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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement guardrails and accountability | 10-15% | - Define autonomy levels
|
| Topic 2: Orchestrate multi-agent coordination | 15-20% | - Operate and manage multi-agent workflows
|
| Topic 3: Manage memory, state, and execution | 10-15% | - Ensure continuity of agent memory and state across tools and environments
|
| Topic 4: Implement tool use and environment interaction | 20-25% | - Operate agents with safe execution paths and robust error handling
|
| Topic 5: Prepare agent architecture and SDLC processes | 15-20% | - Configure observability and control for autonomous agents
|
| Topic 6: Perform evaluation, error analysis, and tuning | 15-20% | - Define success criteria and evaluation signals for agent tasks
|
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NEW QUESTION # 44
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: B
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 # 45
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: B
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 # 46
You have a GitHub Actions workflow that runs two jobs named agent-plan and agent-implement on pull requests.
The agent-plan job generates an implementation plan that downstream jobs must consume, but the job currently writes the plan only to the console.
You need to capture the implementation plan as a step output and configure the workflow so that downstream jobs can reference the output.
How should you configure the workflow? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 47
You have a GitHub Copilot coding agent named CodeAgent. The .agent.md file of CodeAgent contains the following YAML frontmatter.
---
name: CodeAgent
description: Performs repository analysis and code review tasks.
tools: ['edit', 'execute', 'read', 'search']
---
You need to issue a GitHub Copilot CLI command that preserves execution velocity for read-only tasks by eliminating approval prompts for low-risk tools. The solution must ensure that high-risk tools that can make changes remain available but still require explicit user approval before running.
Which command should you run?
Answer: E
Explanation:
To configure your GitHub Copilot agent (CA) to run low-risk, read-only tools without approval prompts while keeping high-risk tools strictly gated, you need to use the gh copilot CLI configuration command to set specific tool permissions.
gh copilot config set-agent-policy CA --allow-without-approval read search --require-approval edit execute Preserves Velocity: Bypasses confirmation prompts for read and search so code analysis runs instantly.
Maintains Security: Explicitly forces a manual user prompt before edit (modifying code) or execute (running commands) can run.
Targets the Agent: Uses the --allow-without-approval and --require-approval flags specifically mapped to the CA agent name declared in your YAML frontmatter.
Reference:
https://vivekfordevsecopsciso.medium.com/github-copilot-remote-code-execution-via-prompt-injection-cve-2025-53773-38b4792e70fb
NEW QUESTION # 48
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 # 49
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