GH-600 Test Dumps, GH-600 VCE Engine Ausbildung, GH-600 aktuelle Prüfung

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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Test, deploy, and monitor agentic AI systems	20%	- Deploy and monitor agents at scale 1. Implement logging, telemetry, and observability 2. Optimize cost, latency, and throughput 3. Deploy to Azure AI and cloud environments - Validate agent performance and safety 1. Apply guardrails and content safety 2. Test reasoning accuracy and consistency 3. Evaluate quality metrics and iterate
Topic 2: Integrate tools, data, and services	25%	- Incorporate external tools and APIs 1. Design and register tool definitions 2. Handle authentication and error resilience 3. Implement function calling and service integration - Connect data sources and knowledge bases 1. Integrate vector databases and search 2. Implement retrieval-augmented generation (RAG) 3. Ensure data security and access control
Topic 3: Design agentic AI solutions	25%	- Design agent architecture 1. Plan tool integration and orchestration 2. Design memory and state management 3. Select agent patterns and topologies - Define requirements for agentic systems 1. Define functional and non-functional requirements 2. Plan for responsible AI and governance 3. Identify use cases and scenarios
Topic 4: Implement agents and multi-agent systems	30%	- Build agents with Azure AI tools and frameworks 1. Integrate models and prompts 2. Develop using Semantic Kernel and Azure AI Foundry 3. Implement agent logic and reasoning - Orchestrate multi-agent collaboration 1. Define communication protocols between agents 2. Implement workflows and coordination strategies 3. Manage agent handoffs and task distribution

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Microsoft GH-600 Praxisprüfung, GH-600 Zertifizierungsantworten

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Microsoft Developing in Agentic AI Systems GH-600 Prüfungsfragen mit Lösungen (Q52-Q57):

52. Frage
You have a GitHub Copilot coding agent that has completed a pull request for a security fix in your repository.
Before merging, you need to evaluate the quality of the agent's work by using both automated evaluation signals and human review.
You review the session log and the pull request.
What are two automated evaluation signals generated by the coding agent's built-in scanning tools? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. the detection of hardcoded secrets, such as API keys and tokens
B. CodeQL findings that identify security vulnerabilities in the generated code
C. linting errors from the repository's CI pipeline configured in GitHub Actions
D. comments from Copilot code review that suggest improvements to code patterns
E. the median time to merge metric reported on the Copilot usage metrics dashboard

Antwort: A,B

Begründung:
The two automated evaluation signals generated by the coding agent's built-in scanning tools are:
CodeQL findings that identify security vulnerabilities in the generated code.
The detection of hardcoded secrets, such as API keys and tokens.
Reference:
https://itacademy.com.ua/en/articles/2026-06-11/security-validation-third-party-coding-agents-github/

53. Frage
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?

A. Replace "servers": with "mcp-servers": in mcp.json.
B. Replace line 04 with tools: ['tool-a', 'tool-b', 'github/copilot'].
C. Replace line 04 with tools: ['tool-a', 'tool-b', 'github/*'].
D. Replace line 04 with tools: ['copilot/*'].

Antwort: D

Begründung:
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.

54. Frage
You need finer control, selecting specific files and describing precise natural-language changes to apply, rather than letting the agent decide the full scope of changes. Which Copilot Chat mode should you use?

A. Agent mode
B. Plan mode
C. Edit mode
D. Ask mode

Antwort: C

Begründung:
Edit mode is designed for targeted, developer-directed changes: you select the files in scope and describe the specific modification, giving you more granular control than the fully autonomous agent mode.

55. Frage
You have a GitHub repository that uses GitHub Actions for CI on pull requests. The repository contains a Node.js app.
You have a GitHub Copilot coding agent that opens pull requests for backlog items, and your company requires automated checks for agent-generated changes.
You plan to standardize success criteria so that pull requests created by agents only succeed when unit tests pass and CodeQL analysis completes.
You need to configure a GitHub Actions workflow that runs on pull requests, executes unit tests, and performs CodeQL analysis.
How should you complete the workflow? To answer, drag the appropriate values to the correct targets.

Antwort:

Begründung:

56. Frage
You have a GitHub repository that uses the GitHub Copilot coding agent to resolve issues in an ephemeral GitHub Actions environment.
Builds fail unless an environment variable named NPM_TOKEN is available, because the repository depends on a private package registry.
A workflow named .github/workflows/copilot-setup-steps.yml was added but was merged into a non-default branch and does NOT run when new agent sessions start.
You need to ensure that for every new agent session, the setup steps run, and the private registry token is available as NPM_TOKEN, without hardcoding secrets in the repository.
What should you do?

A. Disable the agent's integrated firewall and rename the workflow.
B. Move .github/workflows/copilot-setup-steps.yml to the repository's default branch and store the token as a GitHub Actions environment secret named NPM_TOKEN in the Copilot environment.
C. Create an organization-level Actions secret named NPM_TOKEN and reference it from .github/copilot-instructions.md.
D. Add the token directly under env: npm_token: in the workflow file.

Antwort: B

Begründung:
The Copilot setup workflow must be present on the repository's default branch to be used for new coding-agent sessions. Moving .github/workflows/copilot-setup-steps.yml there ensures the setup procedure is discoverable for every newly created agent environment.
The package-registry credential should be stored as an environment secret in the Copilot environment and exposed using the required variable name, NPM_TOKEN. This permits package installation without committing the token into workflow YAML, instructions, source code, or pull requests.
Disabling network controls does not resolve missing credentials and expands risk unnecessarily. Repository instructions can describe setup expectations but cannot safely substitute for an injected runtime secret. Adding the raw token to YAML permanently exposes it to repository history and anyone with repository access.
Study-guide topics: ephemeral environments, setup workflows, environment secrets, and secure dependency access.

57. Frage
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