GH-600 Übungsfragen: Developing in Agentic AI Systems & GH-600 Dateien Prüfungsunterlagen

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Microsoft GH-600 Exam Syllabus Topics:

SectionWeightObjectives
Implement agents and multi-agent systems30%- Orchestrate multi-agent collaboration
  • 1. Define communication protocols between agents
    • 2. Implement workflows and coordination strategies
      • 3. Manage agent handoffs and task distribution
        - Build agents with Azure AI tools and frameworks
        • 1. Develop using Semantic Kernel and Azure AI Foundry
          • 2. Integrate models and prompts
            • 3. Implement agent logic and reasoning
              Integrate tools, data, and services25%- Connect data sources and knowledge bases
              • 1. Ensure data security and access control
                • 2. Integrate vector databases and search
                  • 3. Implement retrieval-augmented generation (RAG)
                    - Incorporate external tools and APIs
                    • 1. Implement function calling and service integration
                      • 2. Design and register tool definitions
                        • 3. Handle authentication and error resilience
                          Test, deploy, and monitor agentic AI systems20%- Validate agent performance and safety
                          • 1. Apply guardrails and content safety
                            • 2. Test reasoning accuracy and consistency
                              • 3. Evaluate quality metrics and iterate
                                - Deploy and monitor agents at scale
                                • 1. Implement logging, telemetry, and observability
                                  • 2. Deploy to Azure AI and cloud environments
                                    • 3. Optimize cost, latency, and throughput
                                      Design agentic AI solutions25%- Design agent architecture
                                      • 1. Design memory and state management
                                        • 2. Select agent patterns and topologies
                                          • 3. Plan tool integration and orchestration
                                            - Define requirements for agentic systems
                                            • 1. Define functional and non-functional requirements
                                              • 2. Identify use cases and scenarios
                                                • 3. Plan for responsible AI and governance

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                                                  Microsoft GH-600 Buch & GH-600 Antworten

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                                                  Microsoft Developing in Agentic AI Systems GH-600 Prüfungsfragen mit Lösungen (Q31-Q36):

                                                  31. Frage
                                                  Drag and Drop Question
                                                  Your team uses a remote GitHub Model Context Protocol (MCP) server for workflows in the software development life cycle (SDLC).
                                                  You need to commit a workspace-scoped MCP configuration to ensure that GitHub Copilot can connect to the GitHub-hosted MCP endpoint and authenticate by using a GitHub personal access token (PAT).
                                                  How should you complete the mcp.json configuration file? 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.

                                                  Antwort:

                                                  Begründung:

                                                  Explanation:
                                                  Box 1: http
                                                  Type (http): The Model Context Protocol (MCP) configuration requires specifying the transport layer type. Since you are communicating via an API endpoint URL (https://...), the transport type is http (or in some implementations, standard variants like sse or streamable-http).
                                                  Box 2: Bearer
                                                  Authorization (Bearer ): GitHub's API endpoints authenticate Personal Access Tokens using the standard HTTP Bearer token schema. Note that a space is required between the word Bearer and the token variable itself.
                                                  Reference:
                                                  https://docs.github.com/en/copilot/how-tos/copilot-on-github/customize-copilot/configure-mcp-servers


                                                  32. Frage
                                                  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?

                                                  Antwort: A

                                                  Begründung:
                                                  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.


                                                  33. Frage
                                                  You are troubleshooting why a Copilot coding agent pull request keeps failing CI checks after every attempted fix. What is the most effective first step?

                                                  Antwort: D

                                                  Begründung:
                                                  Agents rely entirely on the context provided in the issue. Vague issues lead to incorrect fixes.
                                                  Providing reproduction steps, error logs, and expected behavior gives the agent the information it needs to correctly diagnose and resolve the root cause.


                                                  34. Frage
                                                  You have a GitHub repository that stores custom GitHub Copilot agents in the .github/agents/ folder.
                                                  You have an agent named lead-dev.agent.md that must invoke a specialist subagent named security-reviewer.
                                                  Evaluation results show that lead-dev attempts to invoke security-reviewer, but the subagent call fails to run.
                                                  You need to ensure that lead-dev can invoke security-reviewer.
                                                  How should you complete the YAML frontmatter of the agent profile? To answer, drag the appropriate values to the correct targets.

                                                  Antwort:

                                                  Begründung:


                                                  35. Frage
                                                  You have a GitHub Copilot coding agent named CodeAgent. The .agent.md file of CodeAgent contains the following YAML frontmatter.
                                                  name: CodeAgent
                                                  description: Performs repository analysis and code review tasks.
                                                  tools: ['edit', 'execute', 'read', 'search']
                                                  You need to issue a GitHub Copilot CLI command that preserves execution velocity for read-only tasks by eliminating approval prompts for low-risk tools. The solution must ensure that high-risk tools that can make changes remain available but still require explicit user approval before running.
                                                  Which command should you run?

                                                  Antwort: D

                                                  Begründung:
                                                  Allowing only read and search removes approval friction for operations that inspect repository content without modifying it. The edit and execute tools remain available in the agent profile, but they continue to require explicit approval because they are not included in the command's approval bypass.
                                                  Using no option preserves the default approval behavior for all tools, including low-risk inspection tools, which does not meet the velocity requirement. Allowing all tools would remove the approval safeguard for file modifications and shell execution. Denying edit and execute prevents those tools from being used at all, rather than keeping them available under human control.
                                                  This design separates tool availability from automatic authorization. Read-only operations can proceed autonomously, while potentially impactful operations remain subject to a human decision at the point of use.
                                                  Study-guide topics: tool approval boundaries, least privilege, read-only execution, and controlled agent autonomy.


                                                  36. Frage
                                                  ......

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