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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement guardrails and accountability | 10–15% | - Implement guardrails and human-in-the-loop workflows
|
| Topic 2: Implement tool use and environment interaction | 20–25% | - Select and configure agent tools
|
| Topic 3: Prepare agent architecture and SDLC processes | 15–20% | - Define boundaries between planning, reasoning, and action
|
| Topic 4: Orchestrate multi-agent coordination | 15–20% | - Configure observability for multi-agent behavior by using logs, artifacts, and operational signals
|
| Topic 5: Manage memory, state, and execution | 10–15% | - Persist agent state and manage context drift
|
| Topic 6: Perform evaluation, error analysis, and tuning | 15–20% | - Analyze agent failures and identify root causes
|
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NEW QUESTION # 25
You have a GitHub Enterprise repository that uses the GitHub Copilot coding agent and opens draft pull requests for assigned issues.
Evaluation results show that the agent repeatedly opens pull requests that modify .github/workflows/*.yml to bypass failing checks instead of fixing the underlying code.
You need to ensure that the agent fixes the underlying code instead of bypassing the failing checks.
What should you do?
Answer: B
Explanation:
A properly configured branch ruleset establishes enforcement outside the agent's editable task instructions. It can require pull requests, successful checks, and appropriate reviews before changes enter protected branches.
In an enterprise setup, required workflows can also be governed centrally so that changing a repository-local workflow does not remove the organization's validation requirement.
The ruleset must be configured to preserve the intended checks. Merely creating an empty ruleset does not solve the problem, and requiring a check whose implementation the agent can freely weaken is insufficient.
Protecting workflow changes through required ownership review or centrally managed validation addresses that remaining gap. The agent should not receive bypass permission for these controls.
Option A increases authority rather than constraining the unwanted behavior. Option C removes the checks that are supposed to detect defective code. Option D affects treatment of untracked files and does not prevent changes to already tracked workflow definitions.
The control cannot guarantee successful reasoning, but it can prevent acceptance of a shortcut that defeats validation.
Relevant curriculum topics are blocking prohibited actions, enforcing independent controls, and correcting undesirable behavior identified through evaluation.
References: Available rules for rulesets , Enterprise required workflows .
NEW QUESTION # 26
You need to enable Copilot memory to support the planned changes.
What should you configure?
Answer: D
Explanation:
Copilot memory is an organization-level capability. Configuring it through the organization's Copilot settings establishes the governance boundary, availability, and policy controls consistently for the developers and repositories covered by that organization.
Personal settings are unsuitable because they create inconsistent behavior between developers and do not provide centralized administration. A repository-level configuration can supply repository instructions and scoped context, but it does not replace the organization-level control that enables and governs the memory capability. An individual agent profile defines role behavior, tool access, prompts, and delegation settings; it is not the tenant-level location for enabling Copilot memory.
Organization configuration is particularly important when agents must retain approved context, recurring pReference, and workflow guidance across sessions without each developer configuring behavior independently. Central governance also allows administrators to maintain predictable controls over which organizational information is available to Copilot.
Study-guide topics: organizational governance, persistent context, and centralized agent configuration.
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:
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 # 28
You have a GitHub repository that uses the GitHub Copilot coding agent to resolve issues and create draft pull requests.
You assign an issue to Copilot. Copilot creates a draft pull request. The pull request timeline shows Copilot started work, followed by status updates. The most recent status update is 55 minutes old.
You discover that the agent is no longer making progress.
You need to ensure that the work resumes without redoing the completed steps or changing the previously chosen approach.
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:
Answer Area
Correct Selection
To confirm progress of the agent
Select View session from the pull request.
To resume the work
Post a pull request comment that mentions @copilot.
To determine whether the coding agent has actually stalled, select View session from the pull request. GitHub exposes live Copilot coding-agent session logs through the pull request timeline. These logs show the agent ' s actions, tool activity, file operations, and execution progress and are GitHub ' s documented troubleshooting mechanism when a pull request appears inactive.
To continue the existing work, post a pull request comment that mentions @copilot and instructs Copilot to continue. This starts another agent session against the same pull request branch . Critically, GitHub states that Copilot retains context from previous sessions associated with the same pull request, making follow-up work faster and preserving continuity rather than beginning the implementation from scratch. If a custom agent created the pull request, an @copilot follow-up also continues with that same agent.
Closing and reopening the issue does not provide the required session-continuation semantics. Marking the draft pull request Ready for review signals review readiness rather than restarting execution, while opening another pull request would unnecessarily fragment the existing work.
Study Guide Reference Topics: Manage Memory, State, and Execution; agent-session observability; preserving execution context; resuming interrupted work; human steering of autonomous agents.
NEW QUESTION # 29
You have a GitHub repository that uses three GitHub Copilot coding agents named agent1, agent2, and agent3.
During structured evaluation runs, agent1 frequently returns Markdown narratives instead of a machine- parsable result.
You need to ensure that agent1 consistently returns only a predefined JSON structure without affecting the output of the other agents.
Which file should you modify?
Answer: B
Explanation:
The required change concerns one agent's behavior, making its custom agent profile the appropriate scope.
The file .github/agents/agent1.agent.md defines instructions for agent1. Its Markdown instruction body can specify the required JSON structure, mandatory properties, allowed values, and prohibition of explanatory prose or Markdown fences.
Repository-wide instructions apply more broadly and could alter the behavior of agent2 and agent3, contrary to the requirement. The setup workflow prepares the execution environment rather than defining the agent's response contract. A file under .github/instructions uses instruction applicability rules; naming it after an agent does not automatically bind it exclusively to that agent.
The evaluation finding should be converted into a precise behavioral requirement and tested against representative inputs. For example, checks should distinguish valid JSON from text containing a JSON fragment and should reject missing fields or unexpected properties.
An agent instruction improves adherence but is not a mathematical guarantee of schema compliance.
Production consumers should validate the returned object before using it. Relevant curriculum topics are revising instructions based on evaluation results and specifying expected output constraints.
Reference: Custom agents configuration .
NEW QUESTION # 30
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