GH-600 Prüfungsfragen Prüfungsvorbereitungen, GH-600 Fragen und Antworten, Developing in Agentic AI Systems

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Microsoft GH-600 Exam Syllabus Topics:

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

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Microsoft Developing in Agentic AI Systems GH-600 Prüfungsfragen mit Lösungen (Q21-Q26):

21. Frage
Hotspot Question
You have the following agent logs.

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Antwort:

Begründung:

Explanation:
Box 1: Yes
Yes, the agent did respond with messages.
Markdown Message: The log ccreq:XXX.copilotmd | markdown message 0 returned: finish reason: [stop] indicates that a text-based markdown response was successfully generated and completed.
Language Model Output: The log ccreq:XXX.copilotmd shows the core language model (gpt-4o- mini) completed its request, delivering the main content of the message.
Box 2: Yes
Based on the log entries provided, yes, the agent can edit files in the repository.
Evidence from the LogsTargeted Component: The final three log entries explicitly target the component [panel/editAgent].
Model Selection: For these specific tasks, the system switches from standard conversational models (gpt-4o-mini) to a specialized code-generation model: gpt-5.3-codex.
Processing Time: These operations take significantly longer than standard chat generation, which is characteristic of complex code-parsing and file-modification tasks.
Box 3: Yes
Based on the log entries provided, yes, the agent analyzed files in the repository.
Action Type: The final three entries explicitly call the [panel/editAgent] capability.
Model Used: These actions utilized the gpt-5.3-codex model, which is specialized for codebases.
Operation Duration: The execution times were significantly longer.
Implied Task: Code-generation and editing agents require repository file analysis to perform edits.
Reference:
https://learn.microsoft.com/en-us/troubleshoot/power-platform/copilot-studio/authoring/error-codes

22. Frage
You have a GitHub Enterprise Cloud repository that uses the GitHub Copilot coding agent to implement changes by creating draft pull requests in a firewalled GitHub Actions environment.
Repository administrators add a Model Context Protocol (MCP) server configuration so that the agent can query an external system while it executes issues. The MCP server requires an API key, and the key must be provided to the MCP server as an environment variable when the server starts.
You create an environment secret named copilot_mcp_api_key that contains the API key.
You need to configure the repository to ensure that the MCP server receives the API key at runtime. The solution must ensure that only the intended secret is available to the MCP configuration.
What is the best option to use to achieve the goal?

A. The JSON configuration
B. A default environment variable
C. An environment variable mapping
D. An environment variable
E. An Actions repository secret

Antwort: C

Begründung:
An environment variable mapping connects the defined environment secret to the exact environment variable required by the MCP server at startup. This allows the MCP server to receive the API key without embedding the secret's value in repository configuration.
A default environment variable is not sufficiently targeted because it can expose data more broadly than necessary. An Actions repository secret is a secret storage mechanism, but the scenario already uses an environment secret and requires controlled injection into the MCP server process. The JSON configuration should reference the mapping or variable name; it must not contain the raw key value.
The mapping provides the least-privilege boundary: only the intended secret is made available, under the expected variable name, to the MCP process that needs it. It also supports credential rotation because the stored secret can be replaced without modifying committed MCP configuration.
Study-guide topics: MCP credential injection, environment secrets, least privilege, and secure tool startup.

23. Frage
You have multiple GitHub Copilot coding agents that run tasks concurrently.
You are monitoring the agents from the terminal by using the GitHub CLI.
An agent appears stalled.
You need to live stream the session log output.
What should you do?

A. Run gh agent-task view and specify the --log and --jq parameters.
B. Run gh agent-task view and specify the --log and --follow parameters.
C. Run gh agent-task list and specify the --web parameter.
D. Run gh agent-task list and specify the --status and --jq parameters.

Antwort: B

Begründung:
To live stream the session log output of a background agent, you must run the gh agent-task view
<task-id> --log --follow command.
Command Syntax
gh agent-task view <task-id> --log --follow
Parameter Breakdown
<task-id>: The unique identifier of the stalled agent task. If you do not know the ID, run gh agent- task list first to look it up.
--log: Instructs the GitHub CLI to pull the specific execution and reasoning logs rather than just a summary of the task state.
--follow: Tells the CLI to keep the connection open and live stream new log events to your terminal in real-time as they happen.
Reference:
https://docs.github.com/en/copilot/how-tos/copilot-on-github/use-copilot-agents/manage-and-track-agents

24. Frage
You need to provide access to the API key of MCP1. The solution must meet the security requirements.
What should you do?

A. Store the API key as a secret in the Copilot environment of product-api by using a name prefix of COPILOT_MCP_, and then reference the variable name in the mcp.json configuration.
B. In product-api, add the API key as a GitHub Actions encrypted secret and reference the secret by using
${{ secrets.KEY }} in the workflow YAML of agent1.
C. In the product-api repository settings, add the API key directly to the .mcp/server.json file by using a plaintext apiKey field.
D. Store the API key as a GitHub Codespaces user secret scoped to product-api.

Antwort: A

Begründung:
The API key should be stored as a secret in the Copilot environment and referenced by name from the MCP configuration. This keeps the secret value out of repository files, agent prompts, pull requests, and ordinary workflow output while allowing the MCP server to receive the required credential at runtime.
A plaintext key in a configuration file is unacceptable because repository history, forks, logs, and code review systems can expose it. A Codespaces user secret is scoped to a user development environment and does not provide the intended runtime secret for the Copilot agent environment. A GitHub Actions secret is useful for workflow expressions, but the scenario requires the key to be available to the MCP server configuration itself.
The prefixed environment-secret approach creates a clear boundary: the configuration references an identifier, while the secret value is resolved only in the authorized execution environment. This supports rotation without changing tracked configuration content.
Study-guide topics: MCP authentication, secrets management, secure environment configuration, and credential isolation.

25. Frage
In Microsoft Visual Studio Code, you use GitHub Copilot agent mode for a repository. The GitHub Model Context Protocol (MCP) server is enabled.
Your team maintains an internal orchestrator script that creates independent Copilot SDK sessions to process operational events from webhooks.
You discover that the agent sometimes mixes context between unrelated webhook deliveries. Additionally, in some cases, the agent starts running tools before a human reviews the proposed steps.
You need to ensure that the script meets the following business requirements:
Planning must be kept separate from executing.
Each plan must be captured without calling any tools.
Each webhook event must create a new independent session.
Once the plan is complete, tool calls must be enabled within the same event-specific session for follow-up.

Antwort:

Begründung:

26. Frage
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