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| Section | Weight | Objectives |
|---|---|---|
| Perform evaluation, error analysis, and tuning | 15-20% | - Define success criteria and evaluation signals for agent tasks
|
| Implement guardrails and accountability | 10-15% | - Implement guardrails and human-in-the-loop workflows
|
| Orchestrate multi-agent coordination | 15-20% | - Operate and manage multi-agent workflows
|
| Prepare agent architecture and SDLC processes | 15-20% | - Define boundaries between planning, reasoning, and action
|
| Implement tool use and environment interaction | 20-25% | - Select and configure agent tools
|
| Manage memory, state, and execution | 10-15% | - Implement agent memory strategies
|
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NEW QUESTION # 55
You have a repository on github.com that uses the GitHub Copilot coding agent.
You also use the GitHub Copilot CLI locally to reproduce failures and continue the same work from your terminal.
You need to verify the current token usage.
Which Copilot CLI slash command should you run?
Answer: D
Explanation:
The correct answer is /context when the requirement is to verify the current token usage of the active context window. GitHub documents /context as the command that displays the active model and the number of tokens currently in use relative to the model's total context capacity. It also provides a breakdown across the system prompt, custom instructions, built-in tools, MCP tools, conversation messages, free space, and reserved buffer.
This is distinct from /usage. /usage reports broader session statistics, including AI Credits consumed, session duration, lines edited, and per-model token totals. Therefore, /usage is appropriate when reviewing cumulative session consumption, but /context is the more precise answer to a question asking for the current token usage of the working context.
/compact reduces context pressure by summarizing earlier conversation history; it does not merely report the current token state. /diff reviews code changes and is unrelated to context or token accounting.
Study Guide Reference Topics: Manage Memory, State, and Execution; context-window monitoring; token-budget management; maintaining effective long-running agent sessions.
NEW QUESTION # 56
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.
You need to make changes to repo1 to support the planned changes for the agents.
What should you modify?
Answer: C
Explanation:
Scenario, 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.
To centrally configure the Microsoft Learn Model Context Protocol (MCP) server for all developers and agents within a shared repository, you should modify the .vscode/mcp.json file.
Central Repository Configuration: In GitHub Copilot and Visual Studio Code, placing an mcp.json file inside the workspace root's .vscode/ directory ensures that the defined MCP servers are automatically loaded and shared with any developer or Copilot agent who opens that specific project repository.
Tool Exposure: This configuration file maps the external tools provided by the Microsoft Learn Docs MCP server (such as searching and fetching official documentation) directly into the Copilot agentic workflow.
Reference:
https://docs.github.com/en/copilot/how-tos/copilot-on-github/customize-copilot/configure-mcp-servers
NEW QUESTION # 57
You need to implement the security requirements for agent1.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
NEW QUESTION # 58
You have a GitHub repository that runs an agentic software development lifecycle workflow by using GitHub Actions. The workflow uses the following three executors implemented as scripts: spec_analyzer, risk_reviewer, and plan_merger.
You need to coordinate multiple specialized agents so that analysis and risk review run in parallel and then a final executor merges the outputs into a single plan. The orchestration pattern must fan out one request to multiple executors and then fan in the results to a final executor.
How should you complete the workflow definition? To answer, drag the appropriate values to the correct targets.
Answer:
Explanation:
Explanation:
Target 1 # E. needs
Target 2 # A. [spec_analyzer, risk_reviewer]
Target 3 # B. concurrency
The relevant completed section is:
plan_merger:
runs-on: ubuntu-latest
needs: [spec_analyzer, risk_reviewer]
steps:
- name: Merge executor outputs into a single plan
run: ./executors/plan_merger.sh
- name: Publish merged plan
run: echo " publish plan artifact "
concurrency:
group: multiagent-${{ github.ref }}
The plan_merger job must use needs: [spec_analyzer, risk_reviewer] . GitHub Actions jobs run in parallel by default when no dependency exists. Therefore, spec_analyzer and risk_reviewer can execute concurrently, implementing the fan-out phase. The needs array then establishes the fan-in dependency: plan_merger does not start until both upstream jobs complete successfully. GitHub explicitly defines jobs. < job_id > .needs as the mechanism for declaring jobs that must complete before another job runs.
The final blank is concurrency , because the following line already defines group: multiagent-${{ github.ref
}}. GitHub ' s concurrency key groups workflow jobs or runs under a shared key so that overlapping executions for the same reference can be controlled. This is separate from the parallelism of the two specialized analyzer jobs.
This architecture directly implements a multi-agent orchestration pattern: independent specialized executors operate concurrently, their outputs converge at a deterministic coordination point, and execution boundaries are managed explicitly. The GH-600 study guide specifically requires candidates to apply orchestration patterns, configure parallel agent execution, and detect or prevent conflicts among concurrently operating agents.
Study Guide Reference Topics: Orchestrate Multi-Agent Coordination; fan-out/fan-in orchestration; parallel execution; dependency management; agent isolation and conflict control.
NEW QUESTION # 59
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 # 60
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