GH-600 Übungstest: Developing in Agentic AI Systems & GH-600 Braindumps Prüfung

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Microsoft GH-600 Exam Syllabus Topics:

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

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

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Microsoft Developing in Agentic AI Systems GH-600 Prüfungsfragen mit Lösungen (Q69-Q74):

69. Frage
You have a GitHub Enterprise Cloud organization that uses a custom coding agent to run GitHub Actions workflows that create branches, open pull requests, and merge changes after required checks pass.
You need to log all agent-initiated actions and ensure that the logs are retained for two years.
What should you do?

A. Configure audit log streaming.
B. Enable Git events in the audit log.
C. Enable log forwarding.

Antwort: A

Begründung:
Audit log streaming is the correct choice because it exports audit events to an external destination where the organization can apply its required two-year retention policy. This captures agent-initiated activity as part of the organization's auditable GitHub events.
Standard audit-log retention in GitHub may not meet a long-term evidence requirement on its own. Streaming allows the organization to store and search the exported events in a SIEM, data lake, or logging platform with retention controls aligned to compliance policy.
Log forwarding is not the required GitHub audit mechanism in this scenario. Enabling Git events can increase event visibility, but it does not independently provide the requested long-term retention of all relevant agent-initiated actions.
The external destination should preserve actor details, timestamps, repository identifiers, action types, and correlation information needed to reconstruct agent activity during an investigation.
Study-guide topics: accountability, audit log streaming, retention, and agent activity traceability.

70. Frage
You have a GitHub repository that uses a GitHub Actions workflow to run an agent-driven change plan as part of a CI pipeline. The workflow generates an artifact named plan.json that includes a field named risk. risk has possible values of low, medium, or high.
You need to ensure that a human must confirm the execution of the workflow when risk is medium or high. The workflow must proceed automatically only when risk is low.
How should you complete the workflow? 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:

71. Frage
Hotspot Question
Your company uses GitHub Copilot custom agents in Microsoft Visual Studio Code.
The company also uses the Copilot coding agent on GitHub issues.
You have a file named .planner.agent.md that defines an agent named planner.planner has tools set to ['search', 'read', 'fetch']. There are explicit instructions NOT to write or modify any code. The file also defines a handoff labeled Start Implementation to an agent named implementer and sets send to false.
Developers report that after the planner agent produces a plan, implementation sometimes starts immediately in the same conversation, and code changes appear without an explicit agent switch.
When Copilot-created pull requests stall, maintainers review the pull request timeline and session logs. Several stalled sessions show outbound network commands blocked by a firewall, and the repositories do NOT contain a .github/copilot-instructions.md file.
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: No
The configuration of the planner agent alone does not explicitly or fully prevent code changes from being produced automatically in the same conversation.
Box 2: No
No, removing the search tool will not resolve the issue, because the planner agent is not the one executing the network requests or writing the code.
The underlying problem is that the handoff configuration has an logic error (send: false), which prevents a clean, explicit agent migration. Because of this, the implementer agent takes over implicitly within the same conversation session, executing background code changes and attempting blocked internet calls.
Box 3: No
No, adding a .github/copilot-instructions.md file demanding tests and linters will not mitigate the stalled pull requests.
The root cause of your stalling pull requests is an underlying network connectivity issue, not a procedural omission by the agent.
Reference:
https://docs.github.com/en/copilot/concepts/agents/cloud-agent/about-custom-agents

72. Frage
You have a GitHub repository that uses the GitHub Copilot coding agent.
You need to complete the hooks configuration to meet the following requirements:
Tool usage must be evaluated before execution.
Build results must be logged after execution.
Tool usage must be logged after execution.
How should you complete the hooks configuration? To answer, drag the appropriate values to the correct targets.

Antwort:

Begründung:

73. 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

74. Frage
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