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| Section | Weight | Objectives |
|---|---|---|
| Implement tool use and environment interaction | 20-25% | - Select and configure agent tools
|
| Perform evaluation, error analysis, and tuning | 15-20% | - Analyze agent failures and identify root causes
|
| Prepare agent architecture and SDLC processes | 15-20% | - Configure observability and control for autonomous agents
|
| Implement guardrails and accountability | 10-15% | - Define autonomy levels
|
| Manage memory, state, and execution | 10-15% | - Ensure continuity of agent memory and state across tools and environments
|
| Orchestrate multi-agent coordination | 15-20% | - Manage the lifecycle of agents within multi-agent workflows
|
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NEW QUESTION # 37
You assigned an issue to the Copilot coding agent, and it opened a pull request. You want to inspect exactly what code changes were made before merging. Which CLI slash command lets you view the change set directly in the terminal?
Answer: D
Explanation:
The correct command is /diff. GitHub Copilot CLI provides /diff specifically for reviewing code changes directly from the terminal. GitHub's current CLI command reference defines /diff as a command that reviews changes in the current directory and can automatically switch to a branch comparison when the working tree is clean. This makes it the appropriate command when validating an agent-generated change set before accepting or merging the work.
The other commands serve different purposes. /context displays context-window utilization, including token allocation and remaining context capacity; it does not show code modifications. /compact summarizes conversation history to reclaim context-window space during long-running sessions. /plan creates an implementation plan before coding and restricts normal project-file modifications while the plan is being prepared.
Using /diff supports an essential human-in-the-loop control in agentic software development: generated code should be inspected before it crosses a merge or deployment boundary. Reviewing the diff exposes additions, deletions, and modifications so that unintended or unsafe changes can be identified before integration.
Study Guide Reference Topics: Implement Tool Use and Environment Interaction; agent-generated code review; human-in-the-loop validation; controlled execution and change inspection.
NEW QUESTION # 38
You have a GitHub repository that uses the GitHub Copilot coding agent.
Developers need the Copilot coding agent to call an internal dependency-scanning API during its run. The API requires an access token.
You need to ensure that the Copilot coding agent can use the token during execution without accessing the repository's Actions secrets and variables. The solution must prevent exposing the token in plaintext.
What should you do?
Answer: A
Explanation:
Option D places the token in the secret store intended for the coding agent's execution environment. This gives the agent's tools access to the credential without committing the credential to an agent profile or repository instruction file. The application can consume the injected environment variable when authenticating to the internal scanning API.
GitHub's current interface calls this dedicated category Agents secrets and variables. Its documentation states that secrets previously configured in the repository's copilot environment were automatically migrated to that category. Therefore, D represents the correct agent-specific mechanism using the terminology in the question.
Option A uses the separate Actions secret category, which is not automatically exposed to the cloud agent. Options B and C place sensitive material in repository content, making the token accessible through file access and potentially retained in version history.
Current documentation also confirms that agent secrets are made available as environment variables and their values are masked in session logs. The integration should still avoid deliberately printing credentials or returning them in tool output.
Relevant curriculum topics are secure environment configuration, authenticated tool access, and separation of credential scopes.
Reference:
NEW QUESTION # 39
You have a GitHub Enterprise repository that runs an autonomous agent by using a GitHub Actions workflow. The workflow has the following jobs:
- agent-run that generates trace.json and plan.md
- review that waits for human approval before continuing
- deploy that uses the outputs from agent-run
You need to make the files inspectable in the GitHub Actions UI and ensure that the files are available to the review and deploy jobs.
What should you do in the workflow?
Answer: A
NEW QUESTION # 40
You have a GitHub Enterprise repository that runs an autonomous agent by using a GitHub Actions workflow. The workflow has the following jobs:
agent-run that generates trace.json and plan.md
review that waits for human approval before continuing
deploy that uses the outputs from agent-run
You need to make the files inspectable in the GitHub Actions UI and ensure that the files are available to the review and deploy jobs.
What should you do in the workflow?
Answer: A
Explanation:
Workflow artifacts provide the required combination of retained execution evidence and file transfer between jobs. The agent-run job uploads trace.json and plan.md, associating both files with the workflow run. Reviewers can access the resulting artifact through the run's GitHub Actions interface, while downstream jobs download it to their own execution environments.
This is necessary because separate jobs do not automatically share a local filesystem. Declaring a dependency establishes execution order but does not transfer the generated files. The review and deployment stages therefore need explicit artifact downloads in addition to their appropriate job dependencies.
Committing transient traces and plans introduces repository changes solely to transport run-specific evidence. A network share adds external infrastructure and does not directly satisfy the requirement for standard Actions artifact access. Dependency caching is intended for reusable inputs such as downloaded dependencies; it is not the appropriate mechanism for authoritative outputs from a particular agent run.
Artifacts preserve the plan and trace that informed the review, allowing subsequent deployment activity to use the corresponding files. Relevant curriculum topics are inspectable agent artifacts, observability, and human intervention.
Reference:
NEW QUESTION # 41
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:
---
name: Summarizer
description: Produce a concise summary of recent changes
tools: ['fetch']
user-invocable: false
disable-model-invocation: true
---
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: B
Explanation:
D is the best answer among the available choices. The key detail is that Summarizer is explicitly listed in Orchestrator's agents property. Current VS Code agent orchestration behavior states that explicitly listing a custom agent in the parent's agents array overrides disable-model-invocation: true for that coordinator. Therefore, option A does not explain the failure in this scenario.
Likewise, user-invocable: false does not prohibit programmatic or subagent invocation. It merely prevents users from manually selecting that agent from the agent picker; this setting is commonly used specifically for worker agents that should only operate as subagents.
Option C is also incorrect because Summarizer's function is to produce a summary, not modify repository content. It therefore does not inherently require editing tools.
The agents property establishes which subagents the coordinator is permitted to invoke; it does not itself define the sequence of workflow transitions. Handoffs are the mechanism for defining explicit guided transitions between agents. If the workflow expects Summarizer to run as the next defined phase but no corresponding transition/invocation exists, the missing handoff can explain why execution stops after Modifier.
Study Guide Reference Topics: Orchestrate Multi-Agent Coordination; custom subagents; agents restrictions; agent invocation controls; sequential handoffs and workflow transitions.
NEW QUESTION # 42
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