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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement agents and multi-agent systems | 30% | - Orchestrate multi-agent collaboration
|
| Topic 2: Design agentic AI solutions | 25% | - Design agent architecture
|
| Topic 3: Test, deploy, and monitor agentic AI systems | 20% | - Validate agent performance and safety
|
| Topic 4: Integrate tools, data, and services | 25% | - Connect data sources and knowledge bases
|
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NEW QUESTION # 79
You want to prevent GitHub Copilot from ever suggesting completions or making edits inside a directory containing sensitive credentials templates. What should you configure?
Answer: A
Explanation:
A is the intended answer among the supplied choices, because the required control is a Copilot-specific content exclusion mechanism rather than Git authorization or branch governance. The important technical concept is excluding the sensitive directory from Copilot's usable context and suggestions.
Current GitHub documentation implements this through Copilot Content exclusion settings at repository, organization, or enterprise scope. Administrators can specify exact files, directory paths, and patterns to exclude. For excluded content, GitHub states that inline suggestions are unavailable in the affected files, excluded file content does not inform suggestions elsewhere, and the files are excluded from supported Copilot responses and code review.
Neither CODEOWNERS nor branch protection prevents Copilot from reading or generating suggestions for a particular directory. CODEOWNERS establishes reviewers/ownership, while branch protections and rulesets govern repository operations such as pushes, merges, and review requirements. Those mechanisms operate too late in the lifecycle to satisfy a requirement concerning Copilot's access to sensitive file content.
For exam purposes, therefore, A maps to the content-exclusion concept. In current GitHub administration, implement the control through Settings → Copilot → Content exclusion and specify the sensitive directory path.
Study Guide Reference Topics: Implement Guardrails and Accountability; sensitive-context exclusion; least exposure; repository security boundaries.
NEW QUESTION # 80
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: C
Explanation:
Unassigning and reassigning the issue is GitHub's documented retry action for an issue-driven agent session that remains stuck and times out. The troubleshooting guidance states that a persistently stuck session times out after an hour. At 70 minutes with no continuing activity, the scenario therefore calls for a fresh execution trigger rather than indefinite waiting.
Reassignment reissues the task through its original issue context. This makes C the appropriate selection among the available actions. The issue should contain current requirements so that the retry begins with the intended scope rather than repeating an outdated assignment.
Option B is incorrect because a draft pull request should not be merged simply to restart implementation, and Copilot does not respond to new mentions on merged or closed pull requests. Option D authorizes repository workflow execution rather than restarting the agent.
For operational precision, reassignment is a documented retry mechanism, not a universal guarantee that every retry reuses the same pull request. Where preserving a particular pull-request branch is essential, an authorized @copilot comment on that open pull request directly targets it.
Relevant curriculum topics are timeout recovery, task reinitialization, and preserving execution continuity.
Reference:
NEW QUESTION # 81
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: D
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 # 82
Drag and Drop Question
You have a GitHub repository that uses GitHub Actions for CI on pull requests.
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. 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:
Explanation:
Box 1: npm test
npm test (or mvn test, pytest, dotnet test depending on your language stack).
The run keyword requires a shell command to execute your test runner.
Box 2: init
The CodeQL workflow must initialize its database and environment before building code. The official action for this is github/codeql-action/init.
Box 3: analyze
After the code is prepared (and built if using a compiled language), the final step scans the codebase and uploads the SARIF results to GitHub Security. The official action is github/codeql- action/analyze.
Reference:
https://gitprotect.io/blog/github-actions-how-to-use-it-for-security-and-compliance-needs/
NEW QUESTION # 83
Case Study 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 repo1that 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 repo1use 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.
Hotspot Question
You are evaluating the logs of the multi-agent workflow in repo2.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: No
Scenario: Note the line: [CopilotCLIMPCHandler] loadMcpConfig called.
CLIMCPServerEnabled=false
That log line indicates that the Model Context Protocol (MCP) server is disabled.
The log explicit parameter CLIMCPServerEnabled=false confirms that the GitHub Copilot CLI MCP handler loaded a configuration where the server functionality is turned off for that specific session.
Box 2: Yes
Scenario: Note the line: [FolderRepositoryManager] Workspace isolation mode selected for session utitle=xxxx, skipping worktree creation That log line confirms the agent session is running in workspace isolation. In this mode, the agent operates directly on the files in your current workspace and applies changes in place, eliminating the need to create a separate Git worktree for the session.
Box 3: Yes
Scenario: Note the two lines with [CopilotCLISession] Invoking session .., Every time you see that line with a new or unique session ID, it means a distinct agent session has been initiated.
New Sessions: When the log says [CopilotCLISession] Invoking session <ID> and assigns a brand-new GUID, it is spinning up a fresh environment with a new workspace isolation state and conversation history.
Reference:
https://github.com/anomalyco/opencode/issues/8990
https://www.kenmuse.com/blog/workspace-vs-worktree-isolation-in-copilot-cli/
https://code.visualstudio.com/learn/foundations/agent-sessions-and-where-agents-run
NEW QUESTION # 84
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