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| Section | Weight | Objectives |
|---|---|---|
| Perform evaluation, error analysis, and tuning | 15-20% | - Evaluate agent performance
|
| Implement tool use and environment interaction | 20-25% | - Select and configure agent tools
|
| Manage memory, state, and execution | 10-15% | - Manage context and memory
|
| Orchestrate multi-agent coordination | 15-20% | - Coordinate multiple agents
|
| Implement guardrails and accountability | 10-15% | - Implement governance controls
|
| Prepare agent architecture and SDLC processes | 15-20% | - Define boundaries between planning, reasoning, and action
|
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NEW QUESTION # 13
While using agent mode in VS Code, you want Copilot to run a specific test suite as a validation step after making changes, without manually invoking the terminal each time. What feature enables this?
Answer: C
Explanation:
Agent mode can be granted permission to invoke terminal commands (like running a test suite) as part of its autonomous workflow, allowing it to self-validate changes rather than requiring the developer to run tests manually each time.
NEW QUESTION # 14
You have a GitHub repository that uses a custom GitHub Copilot coding agent defined in the rollout-bot.agent.md file.
You need to update a workflow so that agent-profile changes can be rolled back by reverting a single commit and rerunning the workflow. The workflow must check out the exact commit being deployed and apply the agent profile from the repository at that commit.
How should you complete the workflow? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 15
You have a GitHub repository that uses GitHub Copilot code review on pull requests.
You plan to add repository-wide code review guidance that will apply to all files.
You need Copilot code review to consistently apply the guidance during pull request reviews.
What should you do?
Answer: C
Explanation:
The repository-wide Copilot instruction file is .github/copilot-instructions.md. GitHub explicitly identifies this file as the location for review guidance that should apply throughout the codebase. It can describe coding standards, security expectations, error-handling requirements, and other review criteria that should be considered across pull requests.
A pull request template primarily structures the description supplied when a pull request is created. It is not the designated repository-wide Copilot instruction mechanism. Files beneath .github/instructions support instructions with defined applicability, commonly using path patterns. The filename in option C alone does not establish repository-wide scope. Custom agent profiles define the behavior of particular agents and do not replace the standard configuration for Copilot code review.
The instructions should state concrete, reviewable requirements rather than vague pReference. For example, a rule about checking authorization at a defined service boundary is more actionable than a general instruction to "ensure security." Repository settings must also allow custom instructions to be used for code review.
This configuration improves consistency while human reviewers remain responsible for evaluating the resulting findings.
Relevant curriculum topics are tuning instructions, defining evaluation criteria, and aligning automated review with development intent.
Reference:
NEW QUESTION # 16
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: B
Explanation:
The correct answer is A because tool availability must be constrained at the agent/MCP integration boundary. GitHub custom-agent configuration provides a tools property that explicitly determines which tools the agent is permitted to use, including tools supplied by configured MCP servers. Rather than enabling all tools with ["*"] or omitting the restriction, administrators can specify individual tool names or aliases and thereby implement an explicit allow-list.
This is a fundamental least-privilege control for agentic systems. An MCP server can expose numerous capabilities, but exposure by the server does not mean every capability should automatically be available to every agent. Restricting the agent to the tools required for its assigned role reduces unintended actions and limits the blast radius of erroneous reasoning, prompt injection, or excessive autonomy. GitHub also supports repository MCP configuration through which administrators determine which external MCP integrations are available.
.copilotignore does not implement MCP authorization. Repository rulesets govern GitHub repository operations such as branches and merges, while /usage reports runtime/session information.
Study Guide Reference Topics: Implement Tool Use and Environment Interaction; MCP integration; tool allow-listing; least privilege; execution boundaries.
NEW QUESTION # 17
You have a GitHub Copilot coding agent named Orchestrator that runs a multi-phase workflow by using the following subagents:
- Explorer gathers context by using read-only tools.
- Modifier applies focused edits.
You are adding a new agent named Summarizer that generates a concise summary after modifications are complete. Summarizer includes the following YAML frontmatter.
The Orchestrator agent lists all three agents in its agents property.
After adding the Summarizer agent, Orchestrator successfully runs Explorer and Modifier but fails to run Summarizer.
What is a possible cause of the failure?
Answer: A
Explanation:
The primary reason for this failure is the disable-model-invocation: true setting in the Summarizer's YAML frontmatter.In the GitHub Copilot Agent configuration framework, when an orchestrator agent automates a multi-agent workflow, it relies on the base LLM model to agentically trigger and delegate tasks to its subagents.
Blocks Subagent Delegation: Setting disable-model-invocation: true instructs GitHub Copilot to completely prevent the model from automatically invoking or calling this agent as a subagent.
Requires Manual Intervention: When this property is true, the agent can only be triggered via a direct manual request by the user (such as explicitly picking it from a chat menu or a slash command). Because user-invocable is also set to false, it becomes completely unreachable in this workflow.
Contradicts Orchestration: Even though Orchestrator explicitly registers Summarizer in its agents property, the underlying model respects the disable-model-invocation: true safety/routing block and refuses to spin up the subagent loop for it.
Reference:
https://docs.github.com/en/copilot/how-tos/copilot-sdk/features/custom-agents
NEW QUESTION # 18
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