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| Section | Weight | Objectives |
|---|---|---|
| Manage memory, state, and execution | 10–15% | - Agent memory strategies
|
| Prepare agent architecture and SDLC processes | 15–20% | - Planning vs execution boundaries
|
| Orchestrate multi-agent coordination | 15–20% | - Multi-agent workflows
|
| Implement tool use and environment interaction | 20–25% | - Safe execution and error handling
|
| Evaluation, error analysis, and tuning | 15–20% | - Tuning agent behavior
|
| Implement guardrails and accountability | 10–15% | - Guardrails and human-in-the-loop
|
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NEW QUESTION # 24
You have a GitHub repository that uses GitHub Actions for CI on pull requests. The repository contains a Node.js app.
You have a GitHub Copilot coding agent that opens pull requests for backlog items, and your company requires automated checks for agent-generated changes.
You plan to standardize success criteria so that pull requests created by agents only succeed when unit tests pass and CodeQL analysis completes.
You need to configure a GitHub Actions workflow that runs on pull requests, executes unit tests, and performs CodeQL analysis.
How should you complete the workflow? To answer, drag the appropriate values to the correct targets.
Answer:
Explanation:
NEW QUESTION # 25
You have a GitHub Copilot coding agent that has completed a pull request for a security fix in your repository.
Before merging, you need to evaluate the quality of the agent's work by using both automated evaluation signals and human review.
You review the session log and the pull request.
What are two automated evaluation signals generated by the coding agent's built-in scanning tools? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: A,C
Explanation:
The two automated evaluation signals generated by the coding agent's built-in scanning tools are:
CodeQL findings that identify security vulnerabilities in the generated code.
The detection of hardcoded secrets, such as API keys and tokens.
Reference:
https://itacademy.com.ua/en/articles/2026-06-11/security-validation-third-party-coding-agents-github/
NEW QUESTION # 26
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: B
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 # 27
You are architecting an agentic AI system and need the agent's tool-calling behavior to be constrained so it can only call a specific allow-listed set of MCP tools, never arbitrary ones. What should you configure?
Answer: A
Explanation:
MCP client configurations (e.g., in VS Code settings or the CLI config) allow administrators to explicitly allow-list which MCP servers/tools an agent may invoke, restricting the attack surface and preventing unintended tool use.
NEW QUESTION # 28
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 # 29
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