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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement agents and multi-agent systems | 30% | - Build agents with Azure AI tools and frameworks
|
| Topic 2: Design agentic AI solutions | 25% | - Design agent architecture
|
| Topic 3: Test, deploy, and monitor agentic AI systems | 20% | - Deploy and monitor agents at scale
|
| Topic 4: Integrate tools, data, and services | 25% | - Incorporate external tools and APIs
|
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NEW QUESTION # 11
You use the GitHub Copilot CLI in ephemeral dev containers.
You need to provide Copilot with reusable guidance for a specific task only. The guidance must be stored in the repository and invoked only when the task is relevant.
What should you do?
Answer: D
Explanation:
A repository skill stored under .github/skills/<skill-name>/SKILL.md provides reusable, task-specific guidance that can be invoked when relevant. Because it is committed to the repository, the guidance is available in ephemeral development containers without relying on a developer's local configuration.
Repository-wide instructions in .github/copilot-instructions.md are applied broadly and are suitable for enduring conventions, architectural rules, or team standards. They are not the best choice when guidance should apply only to a particular task or workflow.
Personal instructions are user-specific and do not provide a version-controlled, team-shared implementation. A .copilot/skills path does not represent the repository-scoped skill location required by the scenario.
Task-specific skills improve precision by supplying focused procedures, reference material, and constraints only when the related task is active. This prevents unrelated work from being burdened by instructions that do not apply.
Study-guide topics: agent skills, repository-scoped guidance, contextual instructions, and ephemeral development environments.
NEW QUESTION # 12
You have a GitHub Enterprise Cloud organization that uses the GitHub Copilot coding agent to resolve issues asynchronously.
When an issue is assigned to GitHub Copilot, the agent creates a draft pull request, but your team cannot always tell whether the agent is actively working, has completed its session, or is awaiting workflow approval.
Which execution context does each signal indicate? To answer, drag the appropriate context to the correct signals.
Answer:
Explanation:
NEW QUESTION # 13
Drag and Drop Question
You have a GitHub repository that uses the GitHub Copilot CLI to run autonomous tasks.
You need to validate each generated command before it runs. Any commands that attempt to modify paths outside the repository must be blocked.
How should you complete the YAML? 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: pre-exec
The hook needs to inspect and validate the generated shell commands before they are allowed to execute. The pre-exec event interceptor serves exactly this guardrail purpose. (post-exec would run only after the command has already finished).
Box 2: .copilot/hooks.yaml
The config property under the cli.hooks block points to the local path of the hooks configuration file itself so that the CLI can parse and load the defined Reference:
https://docs.github.com/en/copilot/how-tos/copilot-cli/cli-best-practices
NEW QUESTION # 14
You have a custom agent profile file named test-agent.agent.md that contains the following YAML frontmatter:
---
name: test-agent
description: Custom agent description
tools: ['tool-a', 'tool-b']
---
In the same repository, you have an MCP configuration file named mcp.json.
You need to ensure that the GitHub Model Context Protocol (MCP) server is available to test the agent. The solution must allow only the Copilot toolset.
What should you do?
Answer: A
Explanation:
The copilot/* toolset grants the agent access to the Copilot-provided tools while avoiding unrelated tool groups. This directly meets the requirement to allow only the Copilot toolset for the test agent.
Keeping tool-a and tool-b would expand access beyond the stated requirement. Using github/* would expose the broader GitHub tool group rather than limiting the profile to the Copilot toolset. The MCP configuration retains its server structure; changing the server property name does not configure which tools the agent may use.
Toolsets are an important control surface for specialized agents. They let an architect define a narrow capability set for testing, planning, review, or implementation roles. The selected toolset should still be backed by an available and authorized MCP configuration; naming a toolset does not bypass server authentication or policy restrictions.
Study-guide topics: MCP toolsets, custom agent capabilities, and least-privilege tool access.
NEW QUESTION # 15
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.
While upgrading App1, the agent identifies 47 issues, including a security vulnerability, and 46 API incompatibilities across different projects.
Which two actions are unsafe to delegate to the agent and require human involvement? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: D,E
Explanation:
The two actions that are unsafe to delegate to the agent and require human involvement are Approve all Git commits and Validate the assessment.md file for accuracy.
Validate the assessment.md file for accuracy: The agent generated this file based on its own scan. A human expert must cross-check its findings to catch false positives, false negatives, and misclassified security vulnerabilities.
Approve all Git commits: Automated agents can introduce unintended code changes, logic flaws, or broken builds. A human must review and approve commits to maintain code quality and prevent security regressions.
Scenario:
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.
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.
Reference:
https://learn.microsoft.com/en-us/dotnet/core/porting/github-copilot-app-modernization/overview
NEW QUESTION # 16
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