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| Section | Weight | Objectives |
|---|---|---|
| Perform evaluation, error analysis, and tuning | 15–20% | - Analyze agent failures and identify root causes
|
| Prepare agent architecture and SDLC processes | 15–20% | - Integrate agents into the software development lifecycle (SDLC)
|
| Implement guardrails and accountability | 10–15% | - Implement guardrails and human-in-the-loop workflows
|
| Implement tool use and environment interaction | 20–25% | - Integrate agents within development environments
|
| Orchestrate multi-agent coordination | 15–20% | - Operate and manage multi-agent workflows
|
| Manage memory, state, and execution | 10–15% | - Persist agent state and manage context drift
|
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NEW QUESTION # 109
You have a GitHub Enterprise Cloud organization that has Copilot agents enabled.
You discover activity in a repository.
You need to identify whether the activity was initiated by an agent.
Which two audit log fields can you use? Each correct answer presents a complete solution.
Answer: A,C
NEW QUESTION # 110
Hotspot Question
You have a GitHub repository that contains the following custom agent files:
- A file named planner.agent.md that includes YAML frontmatter with a
handoffs entry that has label: Start Implementation, agent:
implementer, and prompt: Now implement the plan outlined above
- A file named implementer.agent.md that is in the same directory as
planner.agent.md and includes YAML frontmatter that has name:
IMPLEMENTER
You add a third agent file named review.agent.md.review.agent.md includes YAML frontmatter that has name: code-review.
You make the following changes to planner.agent.md:
- Update the existing handoff to include send: true and model: GPT-5.2
(copilot).
- Add a second handoff that has label: Run Review, agent: code-review,
and prompt: Review the code changes made in the previous step.
No other agent files are modified.
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: Yes
GitHub Copilot Extensions and agent frameworks route handoffs by matching the agent identifier in the YAML frontmatter to the filename or the name property defined within that repository.
Box 2: Yes
The failure happens because the agent target pointer in the handoff configuration does not match the actual target agent's declaration or file name due to a mismatch introduced by your changes.
Box 3: Yes
Yes, selecting "Start Implementation" will switch the chat to the implementer agent and automatically submit the prompt using the specified model.
The updates made to your GitHub agent repository configure a seamless workflow transition.
Here is how your new settings process that action:
Trigger action: Clicking Start Implementation activates the specific handoff block you modified.
Agent switch: The agent: implementer property targets the file with name: IMPLEMENTER, routing the conversation there.Automated submission: Setting send: true forces the interface to submit the text immediately instead of leaving it in the text box.
Model override: The model: GPT-5.2 (copilot) line forces the system to process that automated submission using that specific model version.
Reference:
https://arinco.com.au/blog/github-copilot-multi-repo-instructions/
https://aminrj.com/posts/building-a-multi-agents-coding-workflow/
NEW QUESTION # 111
You have a GitHub repository that uses the GitHub Copilot coding agent. The repository uses strict branch protections on the main branch.
You maintain a long-lived branch named release/1.4 that has different dependencies and CI checks.
You need the agent to create a hotfix that is implemented and validated against the release/1.4 branch. The solution must prevent the agent from basing its work on main.
What should you do?
Answer: C
Explanation:
Selecting release/1.4 as the base branch establishes the correct repository context before the agent begins its assignment. GitHub documents that Copilot creates its working branch from the selected base branch. This ensures that the initial code, dependency declarations, and branch-specific files correspond to the release being repaired.
That distinction matters because a hotfix developed against main may depend on APIs, package versions, or build behavior that do not exist in the maintained release. Choosing the release branch at task creation removes the need for the agent to infer or subsequently correct its starting point. The task should also require validation using the release's applicable checks.
Option A controls concurrency rather than branch selection. Option B governs review requirements for changes to main; it does not determine where the agent starts. Option C supplies a natural-language instruction but is less direct than configuring the actual base branch before execution.
The selected answer configures the execution environment through the platform's supported task controls.
Relevant curriculum topics are branch-based scope, execution-context selection, and environment-specific constraints.
Reference:
NEW QUESTION # 112
You have a GitHub Enterprise organization that has Copilot memory enabled.
You create a new repository.
What are two ways that memories will be deleted from the repository? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: B,C
Explanation:
GitHub Copilot repository memories can be removed manually and through automatic retention cleanup. Repository owners or administrators can review repository-level facts under the repository's Copilot Memory settings and delete stored facts that are obsolete, misleading, or no longer appropriate. This makes A a valid deletion mechanism.
D is also correct. GitHub applies an inactivity-based retention policy: a stored repository fact or user preference that goes unused for 28 days is automatically deleted. The 28-day period can restart when Copilot successfully validates and uses that memory, so this should be understood as an unused-memory expiration period rather than an unconditional deletion exactly 28 days after creation.
Deleting the source code referenced by a memory does not directly delete that memory. Repository-level facts retain citations and are validated against the current branch before use; if the supporting information is no longer valid, Copilot does not use the fact. Likewise, archiving a repository or merging a pull request is not documented as an automatic memory-deletion event.
Study Guide Reference Topics: Manage Memory, State, and Execution; repository memory lifecycle; memory validation; memory retention and deletion; persistent agent knowledge.
NEW QUESTION # 113
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 need to implement agent2 to meet the technical requirements.
How should you complete the YAML configuration? 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: 'search',
Search allows the agent to look for specific keywords, classes, or patterns across your repository to understand the current structure and code requirements.
Box 2: 'read'
Read grants the agent read-only permission to examine the full contents of the codebase files without having the capability to alter them.
Reference:
https://github.com/github/copilot-cli/issues/1663
NEW QUESTION # 114
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