GH-600 Prüfungsmaterialien, GH-600 Fragenkatalog

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

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

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Microsoft Developing in Agentic AI Systems GH-600 Prüfungsfragen mit Lösungen (Q77-Q82):

77. Frage
You have a repository that uses the GitHub Copilot coding agent and supports hooks stored under .github/hooks.
You need a Shell command to run automatically whenever an agent execution fails.
Which type of hook should you use?

A. errorOccurred
B. sessionEnd
C. postToolUse
D. agentStop

Antwort: A

Begründung:
The errorOccurred hook is designed to respond to failures during an agent session. It can invoke a shell command when an agent execution encounters an error, enabling automated capture of diagnostic information, cleanup actions, or controlled escalation.
A postToolUse hook runs after individual tool calls, whether or not the overall agent execution subsequently fails. It is suitable for logging or post-processing tool activity but does not specifically target an execution failure. A session-end event indicates that a session has ended; it does not necessarily represent an error condition. agentStop is not the defined hook required by the scenario.
Failure hooks should be used carefully. Their commands should collect only the required diagnostics and must avoid exposing secrets in logs. If the failure itself results from a prohibited operation, the hook should preserve the reason and relevant metadata so that a human can distinguish policy denial from a technical execution failure.
Study-guide topics: lifecycle hooks, failure handling, diagnostic collection, and accountability.

78. Frage
You have a GitHub Enterprise repository.
An agent opens pull requests to the main branch.
You need to ensure that changes to .github/workflows/* and /infra/* require approval from designated reviewers before merge.
What should you configure?

A. a branch protection rule and a CODEOWNERS file
B. a ruleset and an agents.md file
C. a ruleset and a .copilotignore file
D. a branch protection rule and copilot-instructions.md

Antwort: A

Begründung:
The correct solution combines a branch protection rule with a CODEOWNERS file. CODEOWNERS allows the repository to associate specific paths with designated users or teams. For example, entries can assign security or platform reviewers to .github/workflows/* and /infra/*. When a pull request modifies those paths, GitHub automatically identifies the corresponding code owners.
The enforcement mechanism comes from branch protection on main. Configure the protection rule to Require a pull request before merging and enable Require review from Code Owners. GitHub then blocks the merge until an applicable code owner approves the affected files. This converts CODEOWNERS from simple reviewer routing into an enforceable merge-control boundary.
agents.md and copilot-instructions.md provide behavioral guidance to AI agents; they do not enforce reviewer authorization. .copilotignore likewise does not establish mandatory merge approval. Although GitHub rulesets can also implement review controls, none of the ruleset choices provides the required CODEOWNERS pairing.
Study Guide Reference Topics: Implement Guardrails and Accountability; protected branches; required human review; CODEOWNERS; repository governance; approval gates.

79. Frage
You assigned an issue to the Copilot coding agent, and it opened a pull request. You want to inspect exactly what code changes were made before merging. Which CLI slash command lets you view the change set directly in the terminal?

A. /context
B. /plan
C. /compact
D. /diff

Antwort: D

Begründung:
The correct command is /diff. GitHub Copilot CLI provides /diff specifically for reviewing code changes directly from the terminal. GitHub's current CLI command reference defines /diff as a command that reviews changes in the current directory and can automatically switch to a branch comparison when the working tree is clean. This makes it the appropriate command when validating an agent-generated change set before accepting or merging the work.
The other commands serve different purposes. /context displays context-window utilization, including token allocation and remaining context capacity; it does not show code modifications. /compact summarizes conversation history to reclaim context-window space during long-running sessions. /plan creates an implementation plan before coding and restricts normal project-file modifications while the plan is being prepared.
Using /diff supports an essential human-in-the-loop control in agentic software development: generated code should be inspected before it crosses a merge or deployment boundary. Reviewing the diff exposes additions, deletions, and modifications so that unintended or unsafe changes can be identified before integration.
Study Guide Reference Topics: Implement Tool Use and Environment Interaction; agent-generated code review; human-in-the-loop validation; controlled execution and change inspection.

80. Frage
You have a GitHub Enterprise organization that has Copilot memory enabled.
You create a new repository.
What are two ways that memories will be deleted from the repository? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. when pull requests are merged
B. manually by repository members
C. automatically after 28 days
D. when the code that created the memory is deleted
E. when the repository is archived

Antwort: B,C

Begründung:
GitHub Copilot repository memories can be removed manually and through automatic retention cleanup .
Repository owners or administrators can review repository-level facts under the repository ' s Copilot Memory settings and delete stored facts that are obsolete, misleading, or no longer appropriate. This makes A a valid deletion mechanism.
D is also correct. GitHub applies an inactivity-based retention policy: a stored repository fact or user preference that goes unused for 28 days is automatically deleted . The 28-day period can restart when Copilot successfully validates and uses that memory, so this should be understood as an unused-memory expiration period rather than an unconditional deletion exactly 28 days after creation.
Deleting the source code referenced by a memory does not directly delete that memory. Repository-level facts retain citations and are validated against the current branch before use; if the supporting information is no longer valid, Copilot does not use the fact. Likewise, archiving a repository or merging a pull request is not documented as an automatic memory-deletion event.
Study Guide Reference Topics: Manage Memory, State, and Execution; repository memory lifecycle; memory validation; memory retention and deletion; persistent agent knowledge.

81. Frage
You have a GitHub repository that uses the GitHub Copilot coding agent.
Your company restricts GitHub Actions secrets.
Developers need the Copilot coding agent to call an internal dependency-scanning API during its run. The API requires an access token.
You need to ensure that the Copilot coding agent can use the token during execution without accessing the repository's Actions secrets and variables. The solution must prevent exposing the token in plaintext.
What should you do?

A. Add the token as an Actions repository secret.
B. Store the token in the agent configuration file.
C. Add the token as a secret in the Copilot environment.
D. Store the token in a repository custom instructions file.

Antwort: C

Begründung:
You should add the token as an Agent secret specifically designed for the GitHub Copilot cloud agent environment.
Repository administrators can configure dedicated Agents secrets to provide the Copilot coding agent with secure access to external resources and APIs. This allows the agent to consume the token natively during its sandboxed background execution without touching standard GitHub Actions repository secrets or variables.
Reference:
https://docs.github.com/en/rest/copilot/copilot-coding-agent-management

82. Frage
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