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| Section | Weight | Objectives |
|---|---|---|
| Test, deploy, and monitor agentic AI systems | 20% | - Validate agent performance and safety
|
| Integrate tools, data, and services | 25% | - Incorporate external tools and APIs
|
| Design agentic AI solutions | 25% | - Define requirements for agentic systems
|
| Implement agents and multi-agent systems | 30% | - Orchestrate multi-agent collaboration
|
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NEW QUESTION # 32
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 # 33
After App1 is upgraded to meet the technical requirements, you need to validate the output.
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:
NEW QUESTION # 34
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 intended distinction is between delegating analytical work and allowing the agent to provide the final approval of its own work. Approving all Git commits would remove independent review of changes that include security-sensitive remediation and compatibility modifications across multiple projects.
Human validation of assessment.md is similarly important because the assessment determines the scope and direction of the upgrade. Incorrectly classified dependencies, missed projects, or an incomplete vulnerability analysis can propagate into the implementation plan. Microsoft's guidance explicitly recommends reviewing the assessment for missing projects, problematic dependencies, and application-specific concerns before proceeding.
Generating the assessment and checking whether a task file exists are appropriate automated activities. An agent can also inspect the plan's dependency relationships and flag potential ordering problems. Such assistance does not transfer final accountability for accepting the plan.
The wording should not be interpreted as prohibiting automated assessment validation. Agents can run valuable consistency checks; the unsafe delegation is treating those checks as sufficient independent approval of their own conclusions.
Study-guide topics: human oversight, independent validation, and approval boundaries. Reference: Microsoft Learn-Best practices for GitHub Copilot upgrade.
NEW QUESTION # 35
You have a GitHub Actions workflow that runs GitHub Copilot-driven integration tests across multiple jobs.
You need to persist the agent memory/state so that it remains available across the jobs.
What should you do?
Answer: C
Explanation:
Workflow artifacts provide an explicit mechanism for transferring serialized agent state between jobs. The producing job writes the required state to files and uploads those files as an artifact. Subsequent jobs download the artifact and load the state before continuing the integration-test workflow.
This is necessary because jobs do not automatically share process memory or a common workspace. They may execute on different runners, and even jobs assigned to similar runner types must not depend on incidental machine reuse. Uploading the state establishes a deliberate persistence boundary rather than relying on temporary execution infrastructure.
Committing state after every job introduces repository mutations merely to transport workflow data. Environment variables are also unsuitable as a general cross-job state store: setting a variable in one job does not automatically expose it in another, and complex state is better represented in structured files.
The application must serialize the information needed for continuation, such as completed steps, relevant decisions, and test progress. Uploading an arbitrary directory does not automatically restore a running agent process.
Relevant curriculum topics are durable state, cross-environment continuity, and resuming work without repeating completed steps.
Reference:
NEW QUESTION # 36
You have a repository that uses the GitHub Copilot coding agent and supports hooks stored under .github/hooks.
You need a Shell command to run automatically whenever an agent execution fails.
Which type of hook should you use?
Answer: C
Explanation:
The errorOccurred hook is designed to respond to failures during an agent session. It can invoke a shell command when an agent execution encounters an error, enabling automated capture of diagnostic information, cleanup actions, or controlled escalation.
A postToolUse hook runs after individual tool calls, whether or not the overall agent execution subsequently fails. It is suitable for logging or post-processing tool activity but does not specifically target an execution failure. A session-end event indicates that a session has ended; it does not necessarily represent an error condition. agentStop is not the defined hook required by the scenario.
Failure hooks should be used carefully. Their commands should collect only the required diagnostics and must avoid exposing secrets in logs. If the failure itself results from a prohibited operation, the hook should preserve the reason and relevant metadata so that a human can distinguish policy denial from a technical execution failure.
Study-guide topics: lifecycle hooks, failure handling, diagnostic collection, and accountability.
NEW QUESTION # 37
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