GH-600 Prüfungsaufgaben - GH-600 Zertifizierungsprüfung

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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Implement tool use and environment interaction	20-25%	- Select and configure agent tools 1. Configure agent tool permissions 2. Configure agent tools 3. Identify required tools - Integrate agents within development environments 1. Evaluate the execution context for an agent 2. Configure an agent's scope to a specific repository 3. Configure an agent to handle environment-specific constraints 4. Enable an agent to perform autonomous actions, including creating branches and pull requests 5. Configure an agent to be invoked in a CI workflow 6. Configure an agent to use branch-based scope - Operate agents with safe execution paths and robust error handling 1. Implement error handling 2. Implement escalation paths 3. Implement rollbacks 4. Implement retries 5. Implement traceability and accountability for agent actions - Configure MCP servers 1. Configure the MCP registries 2. Configure MCP allow lists 3. Add an MCP server as a tool to an agent 4. Configure a GitHub remote MCP server
Topic 2: Prepare agent architecture and SDLC processes	15-20%	- Configure observability and control for autonomous agents 1. Plan and implement the degree of agent autonomy, including guardrails 2. Configure agents to produce inspectable artifacts within standard development tooling 3. Configure human intervention for autonomous agents without slowing delivery - Define boundaries between planning, reasoning, and action 1. Configure agent planning to be distinct from agent execution 2. Validate agent plans 3. Prevent agent action until the agent checked and approved 4. Configure an agent to output a structured plan - Integrate agents into the software development lifecycle (SDLC) 1. Identify and mitigate common anti-patterns in agents 2. Define inputs, outputs, and success criteria for agents 3. Identify steps for agents to perform
Topic 3: Implement guardrails and accountability	10-15%	- Define autonomy levels 1. Classify agent actions by operational, security, and compliance risk to right-size human interventions 2. Assign autonomy levels to maximize delivery speed while remaining compliant with organizational security and Responsible AI standards - Implement guardrails and human-in-the-loop workflows 1. Identify the subset of actions that require human judgment 2. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes 3. Scope permissions and execution contexts to enforce least-privilege access 4. Block actions that violate defined security, compliance, or Responsible AI policies 5. Preserve execution velocity by minimizing approvals that do not materially reduce risk
Topic 4: Manage memory, state, and execution	10-15%	- Ensure continuity of agent memory and state across tools and environments 1. Prevent stale context 2. Share agent state 3. Prevent conflicting context - Implement agent memory strategies 1. Scope agent memory to task-relevant information 2. Define memory expiration, pruning, and reset rules 3. Choose between short-term, long-term, and external memory - Persist agent state and manage context drift 1. Capture task progress and decisions as durable artifacts 2. Resume agent work without repeating steps or diverging from prior decisions 3. Detect and correct drift during extended agent execution
Topic 5: Perform evaluation, error analysis, and tuning	15-20%	- Define success criteria and evaluation signals for agent tasks 1. Identify qualitative and quantitative evaluation signals to evaluate agents 2. Generate evaluation signals by using automated scanning tools 3. Specify expected outcomes and operational constraints for agent tasks 4. Align evaluation criteria with development intent - Analyze agent failures and identify root causes 1. Identify failures by using logs, plans, traces, outputs, and workflow artifacts 2. Classify root causes, including reasoning errors, tool misuse, and context or environment issues - Tune agent behavior based on evaluation results 1. Refine tool usage and tool access 2. Revise instructions, workflows, or constraints 3. Refine memory usage
Topic 6: Orchestrate multi-agent coordination	15-20%	- Detect and respond to multi-agent failures and degraded behavior 1. Identify failed, partial, or stalled agent executions 2. Respond to degraded behavior or coordination across agents 3. Implement multi-agent recovery patterns, including rollback and human-in-the-loop - Configure observability for multi-agent behavior by using logs, artifacts, and operational signals 1. Document key decisions, handoffs, and outcomes across agents 2. Configure multi-agent workflows to produce artifacts suitable for review and audit 3. Perform post-hoc analysis of multi-agent behavior - Manage the lifecycle of agents within multi-agent workflows 1. Retire agents while preserving auditability and workflow continuity 2. Add agents to existing multi-agent workflows 3. Update, reconfigure, or replace agents without disrupting active workflows - Operate and manage multi-agent workflows 1. Apply an orchestration pattern to coordinate multiple agents 2. Detect and resolve agent conflicts, including overlapping code changes, duplicated effort, and contradictory outputs 3. Configure agent isolation for parallel execution

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GH-600 Zertifizierungsprüfung - GH-600 Zertifizierungsfragen

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Microsoft Developing in Agentic AI Systems GH-600 Prüfungsfragen mit Lösungen (Q92-Q97):

92. Frage
You need to troubleshoot the issue reported by Dev1.
What should you review?

A. The GitHub Actions runner log for the session job.
B. The agent session log in the Agents panel.
C. The GitHub Actions usage metrics of repo1.
D. The GITHUB_TOKEN permissions block in the agent1 workflow.

Antwort: B

Begründung:
The agent session log is the appropriate starting point when the reported problem concerns an agent's interaction, execution sequence, or tool activity within the development environment. It provides information closer to the failing behavior than repository-level workflow usage statistics.
A useful investigation establishes what the agent was asked to do, which operations it attempted, what responses those operations returned, and where progress stopped or diverged from the intended result. Session diagnostics can help distinguish an instruction problem from an unavailable tool, a rejected operation, or an execution error. VS Code also provides detailed chat debugging facilities for inspecting interactions and diagnosing failures.
GitHub Actions usage metrics primarily describe workflow consumption. Runner logs are relevant when evidence points to a hosted workflow or runner problem. The token permissions block becomes relevant when the failure concerns authorization to a GitHub resource. None should be assumed to be the root cause before examining the reported execution.
The PDF selects B; the underlying Dev1 incident description is not included, so this selection assumes a session-level agent issue.
Study-guide topics: diagnostic evidence, tool-call inspection, and failure localization. Reference: VS Code-Debug chat interactions.

93. Frage
You have a GitHub Enterprise Cloud repository that uses GitHub Actions for CI and requires pull requests for all changes.
You are planning a GitHub Actions workflow where a coding agent drafts implementation changes and tests from GitHub issues, and an automated review runs before human review.
You need the agent to draft changes from an assigned issue, open a pull request, and add an automated review to the pull request before requesting a human review.
What should you do? To answer, select the appropriate options in the answer area.

Antwort:

Begründung:

94. Frage
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.

A. Validate the assessment.md file for accuracy.
B. Review the plan.md file for dependencies.
C. Approve all Git commits.
D. Validate whether the tasks.md file exists.
E. Generate the assessment.md file.

Antwort: A,C

Begründung:
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

95. Frage
You have a GitHub repository that uses the GitHub Copilot coding agent to run agent plans for assigned issues.
You discover that some issues contain ambiguous implementation steps.
You need to reduce the likelihood that Copilot executes plans based on ambiguous implementation steps.
What should you do?

A. Add a copilot-setup-steps.yml file to preinstall dependencies in the agent environment.
B. Add comments to the issues after assigning the issues to the agent.
C. Mention @copilot in a pull request comment to request changes after the plan runs.
D. Add acceptance criteria and directions to the issues before assigning the issues to the agent.

Antwort: D

Begründung:
The most effective intervention occurs before the issue becomes an executable assignment. Acceptance criteria define the observable conditions that constitute success, while implementation directions establish boundaries and clarify how the work should proceed. Providing both before assignment reduces the need for the agent to infer missing requirements during planning.
Applied to this scenario, the issue should distinguish required outcomes from optional improvements and identify constraints that the implementation must respect. Where steps are ambiguous, the author should resolve the ambiguity or explicitly require clarification before execution. Reviewable criteria also give the agent and maintainers a shared basis for evaluating whether the resulting changes satisfy the original request.
Options A and D intervene after assignment or execution has begun. They may support later correction, but they do not prevent the initial plan from being based on unclear directions. Option C addresses dependency availability, which is a separate environmental concern.
The governing design principle is to make the task sufficiently definite before delegating action. Relevant curriculum topics include defining inputs and success criteria, validating agent plans, and separating preparation from execution.
Reference:

96. Frage
You have multiple GitHub Copilot coding agents that run tasks concurrently.
You live stream the session log output and see the following.

What is a possible cause of the error?

A. a postToolUse hook
B. a preToolUse hook
C. a GitHub app installation policy
D. a Copilot firewall policy

Antwort: B

Begründung:
In GitHub Copilot Extensions and Agent frameworks (such as the GitHub Copilot Chat architecture), preToolUse hooks are explicitly designed to intercept, evaluate, and validate a tool's arguments before the agent executes the underlying command or function. Because the agent attempted to execute rm -rf /infra and was immediately blocked with a specific destructive_operation_detected reason, a preToolUse lifecycle hook intercepted the function call, identified the risk, and rejected the execution before the system could actually run the command.
Reference:
https://docs.github.com/en/copilot/how-tos/copilot-on-github/use-copilot-agents/manage-and-track-agents

97. Frage
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