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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement tool use and environment interaction | 20–25% | - Select and configure agent tools
|
| Topic 2: Prepare agent architecture and SDLC processes | 15–20% | - Integrate agents into the software development lifecycle (SDLC)
|
| Topic 3: Perform evaluation, error analysis, and tuning | 15–20% | - Define success criteria and evaluation signals for agent tasks
|
| Topic 4: Implement guardrails and accountability | 10–15% | - Define autonomy levels
|
| Topic 5: Orchestrate multi-agent coordination | 15–20% | - Configure observability for multi-agent behavior by using logs, artifacts, and operational signals
|
| Topic 6: Manage memory, state, and execution | 10–15% | - Implement agent memory strategies
|
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NEW QUESTION # 38
You have a multi-agent GitHub Actions workflow that uploads review artifacts for each run.
You discover that some workflow run artifacts are being deleted manually.
You need to use your organization's audit log data to identify which user deleted the artifacts.
Which audit log search filter should you use?
Answer: C
Explanation:
The filter action:artifact.destroy selects the audit event associated with manual deletion of a workflow artifact. It directly targets the activity described in the scenario, allowing the investigator to examine the recorded actor and associated event details.
A repository filter narrows the search to a particular repository but does not distinguish artifact deletion from other recorded operations. It can usefully accompany the action filter when investigating a specific project. A workflow-run action concerns workflow execution rather than the manual removal of its stored artifacts. The generic operation:remove option does not select the documented artifact deletion event.
The investigation should correlate the deletion event with its timestamp and repository information, then inspect the actor recorded for that event. This identifies the account associated with the action; additional context may be needed when an application or automated identity performed it.
Artifact retention and audit logging serve separate purposes. Retention controls how long outputs are normally preserved, while the audit log records relevant administrative and user activity. Searching the audit log does not restore deleted content.
Study-guide topics: accountability, evidence preservation, actor attribution, and audit trails. Reference: GitHub-Organization audit log events.
NEW QUESTION # 39
You need to implement agent2 to meet the technical requirements.
How should you complete the YAML configuration? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
name: implementation-planner
description: Creates detailed implementation plans and technical specifications in markdown format tools: [
<Dropdown 1>,
<Dropdown 2>,
'microsoftdocs/mcp/docs_search',
'microsoftdocs/mcp/docs_fetch'
]
The accompanying image includes empty dropdown controls and recreated practice alternatives.
Answer:
Explanation:
Topic 1, Contoso Ltd,
Overview
Contoso Ltd. is a software development company located in the United States.
Existing Environment
GitHub Environment
Contoso uses GitHub Enterprise and assigns GitHub Copilot Pro+ licenses to its developers. The developers use Microsoft Visual Studio Code as their IDE.
Contoso has a customer portal. The code for the portal is stored in a GitHub repository named repo1 that contains the following:
A custom agent named agent1 that includes instructions to review specs related to best practices A custom instruction file named validate-instructions.md that is used to validate tone of voice and applies to all .md and .txt files A custom instruction file named codereview.instructions.md that is used by the Copilot coding agent but is excluded for use by the Copilot code review repo1 has the following structure:
The front-end is stored in the /frontend folder.
The API logic is stored in the /api folder.
Contoso has a second repository named repo2 that contains a legacy .NET application named App1 built by using .NET 6. repo2 has a multi-agent workflow for modernization tasks.
Contoso enables the Model Context Protocol (MCP) registry and allows the Microsoft Learn MCP Server. Every developer must configure their own connection to the Learn MCP Server.
Problem Statements
The developers working in repo1 report that the Microsoft Learn documentation is NOT being retrieved when they attempt to validate a design by using agent1.
The testing team at Contoso identifies that the customer portal uses inconsistent UI styles, which leads to customer confusion and branding issues. The UI inconsistencies stem from variations in the folder structure.
Agent Logs
You have the following logs for the multi-agent workflow used in repo2.
Requirements
Planned Changes
Contoso plans to have all agents and developers in repo1 use the Microsoft Learn MCP to ensure that reviews are validated by using the appropriate documentation. This must be implemented centrally.
Contoso plans to leverage AI-powered coding agents to implement new portal features and pages.
Technical Requirements
App1 must be upgraded to .NET 10. A previous upgrade attempt was started by using the Copilot modernization agent, but the attempt was never finalized.
You plan to retry the upgrade. You must first analyze App1 by using AI, and then generate a report that contains breaking changes and deprecated patterns before retrying the upgrade.
All AI-generated code for UI styling must adhere to a predefined folder structure.
The architects at Contoso need help building implementation plans for repo1. The company wants to implement a new agent named agent2 to analyze the code base and the code requirements, and then respond with a detailed plan. The agent must NOT be able to edit files or run local commands.
The developers must be able to delegate work to the Copilot coding agent by assigning issues to the agent.
NEW QUESTION # 40
You have a GitHub Enterprise Cloud Organization that uses the GitHub Copilot coding agent.
Copilot creates a draft pull request for an assigned issue, and the pull request timeline shows Copilot started work.
After 70 minutes, the agent session log stops updating, and the pull request body status stops changing.
You need to restart the agent so that it continues the task from the issue context and produces new commits to the existing draft pull request.
What should you do?
Answer: A
Explanation:
To restart the background session and force the agent to resume its task, unassign the issue from GitHub Copilot and then reassign it to Copilot.
This specific operational cycle terminates the frozen cloud background process and launches a fresh agent session. Because a draft pull request already exists and is bound to the issue context, the newly initiated session automatically detects the linked branch, picks up the previous implementation plan, and begins pushing new commits directly to that existing draft PR.
Reference:
https://docs.github.com/en/copilot/how-tos/use-copilot-agents/cloud-agent/troubleshoot-cloud-agent
NEW QUESTION # 41
You need to provide access to the API key of MCP1. The solution must meet the security requirements.
What should you do?
Answer: D
Explanation:
The API key should be stored as a secret in the Copilot environment and referenced by name from the MCP configuration. This keeps the secret value out of repository files, agent prompts, pull requests, and ordinary workflow output while allowing the MCP server to receive the required credential at runtime.
A plaintext key in a configuration file is unacceptable because repository history, forks, logs, and code review systems can expose it. A Codespaces user secret is scoped to a user development environment and does not provide the intended runtime secret for the Copilot agent environment. A GitHub Actions secret is useful for workflow expressions, but the scenario requires the key to be available to the MCP server configuration itself.
The prefixed environment-secret approach creates a clear boundary: the configuration Reference an identifier, while the secret value is resolved only in the authorized execution environment. This supports rotation without changing tracked configuration content.
Study-guide topics: MCP authentication, secrets management, secure environment configuration, and credential isolation.
NEW QUESTION # 42
You use the GitHub Copilot CLI in a developer terminal.
You plan to use Copilot CLI programmatically to summarize recent commits.
You need to ensure that Copilot performs a low-risk, read-only action without granting Copilot broad write or execution capability.
Which Copilot CLI configuration should you use?
Answer: A
Explanation:
Option C is the intended answer because it grants a specific tool permission associated with Git rather than enabling every tool or allowing general file modification. The -p argument supplies the prompt programmatically, making the command suitable for a script that requests a summary of recent repository activity.
Option A grants unnecessarily broad tool access. Option B denies the Git capability needed to inspect commit history. Option D authorizes file-writing functionality, which is unrelated to producing a read-only summary.
The permission boundary nevertheless requires precise interpretation. Allowing Git is not inherently equivalent to enforcing read-only behavior: Git includes commands that modify files, repository state, and remote branches. A strict implementation should authorize only the required inspection commands, such as git log, using the permission-pattern syntax supported by the installed CLI version. The prompt should request summarization, but the prompt itself is not an access-control mechanism.
Therefore, C is the narrowest relevant selection among the supplied options, while command-specific restrictions are necessary to establish a genuinely read-only execution policy.
Relevant curriculum topics are selecting required tools, configuring tool permissions, and applying least privilege to agent execution.
Reference:
NEW QUESTION # 43
......
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