GH-600 Prüfung & GH-600 Zertifizierungsprüfung

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Microsoft GH-600 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|
| Topic 1: Perform evaluation, error analysis, and tuning | 15-20% | - 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
- 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. Align evaluation criteria with development intent
- 4. Specify expected outcomes and operational constraints for agent tasks
|
| Topic 2: Implement guardrails and accountability | 10-15% | - Implement guardrails and human-in-the-loop workflows
- 1. Identify the subset of actions that require human judgment
- 2. Block actions that violate defined security, compliance, or Responsible AI policies
- 3. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes
- 4. Preserve execution velocity by minimizing approvals that do not materially reduce risk
- 5. Scope permissions and execution contexts to enforce least-privilege access
- 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
|
| Topic 3: Prepare agent architecture and SDLC processes | 15-20% | - Define boundaries between planning, reasoning, and action
- 1. Configure agent planning to be distinct from agent execution
- 2. Validate agent plans
- 3. Configure an agent to output a structured plan
- 4. Prevent agent action until the agent checked and approved
- Integrate agents into the software development lifecycle (SDLC)
- 1. Identify and mitigate common anti-patterns in agents
- 2. Define inputs, outputs, and success criteria for agents
- 3. Identify steps for agents to perform
- Configure observability and control for autonomous agents
- 1. Configure human intervention for autonomous agents without slowing delivery
- 2. Plan and implement the degree of agent autonomy, including guardrails
- 3. Configure agents to produce inspectable artifacts within standard development tooling
|
| Topic 4: Orchestrate multi-agent coordination | 15-20% | - Configure observability for multi-agent behavior by using logs, artifacts, and operational signals
- 1. Document key decisions, handoffs, and outcomes across agents
- 2. Perform post-hoc analysis of multi-agent behavior
- 3. Configure multi-agent workflows to produce artifacts suitable for review and audit
- Manage the lifecycle of agents within multi-agent workflows
- 1. Retire agents while preserving auditability and workflow continuity
- 2. Add agents to existing multi-agent workflows
- 3. Update, reconfigure, or replace agents without disrupting active workflows
- Operate and manage multi-agent workflows
- 1. Configure agent isolation for parallel execution
- 2. Detect and resolve agent conflicts, including overlapping code changes, duplicated effort, and contradictory outputs
- 3. Apply an orchestration pattern to coordinate multiple agents
- Detect and respond to multi-agent failures and degraded behavior
- 1. Implement multi-agent recovery patterns, including rollback and human-in-the-loop
- 2. Identify failed, partial, or stalled agent executions
- 3. Respond to degraded behavior or coordination across agents
|
| Topic 5: Implement tool use and environment interaction | 20-25% | - Operate agents with safe execution paths and robust error handling
- 1. Implement retries
- 2. Implement rollbacks
- 3. Implement escalation paths
- 4. Implement traceability and accountability for agent actions
- 5. Implement error handling
- Integrate agents within development environments
- 1. Enable an agent to perform autonomous actions, including creating branches and pull requests
- 2. Configure an agent to use branch-based scope
- 3. Configure an agent to be invoked in a CI workflow
- 4. Evaluate the execution context for an agent
- 5. Configure an agent to handle environment-specific constraints
- 6. Configure an agent's scope to a specific repository
- Select and configure agent tools
- 1. Identify required tools
- 2. Configure agent tools
- 3. Configure agent tool permissions
- Configure MCP servers
- 1. Configure the MCP registries
- 2. Configure a GitHub remote MCP server
- 3. Configure MCP allow lists
- 4. Add an MCP server as a tool to an agent
|
| Topic 6: Manage memory, state, and execution | 10-15% | - 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
- Ensure continuity of agent memory and state across tools and environments
- 1. Share agent state
- 2. Prevent stale context
- 3. Prevent conflicting context
- Implement agent memory strategies
- 1. Choose between short-term, long-term, and external memory
- 2. Define memory expiration, pruning, and reset rules
- 3. Scope agent memory to task-relevant information
|
>> GH-600 Prüfung <<
GH-600 Zertifizierungsprüfung & GH-600 Prüfungsaufgaben
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Microsoft Developing in Agentic AI Systems GH-600 Prüfungsfragen mit Lösungen (Q10-Q15):
10. Frage
You need to resolve the issue of the agents generating conflicting output. The solution must meet the implementation guidelines.
What should you do?
- A. Add shared/config.yaml to a CODEOWNERS file that requires SG_Review approval before any changes can be merged.
- B. Configure a concurrency group on both agent workflows so that only one workflow runs at a time.
- C. Configure tools: ['read', 'search'] in both agent profiles to prevent either agent from writing files.
- D. Configure each agent to work on a separate branch and add a required status check that detects file-level overlap before either pull request can be merged.
Antwort: D
Begründung:
Separate branches isolate each agent's proposed changes and allow the team to inspect them independently. Adding a required status check for file-level overlap detects conflicting modifications before either pull request is merged, preventing one agent's work from silently overwriting another's changes.
A read-only tool configuration prevents both agents from making required changes, so it avoids conflict by eliminating implementation capability rather than controlling it. CODEOWNERS approval adds review accountability for a specific file but does not automatically detect overlap between independent agent outputs. A single concurrency group serializes entire workflows and reduces throughput even where the agents work on unrelated files.
The selected solution maintains parallel development while creating a merge-time control for the actual risk: overlapping file changes. It also produces reviewable evidence of the overlap check in pull request status results.
Branch isolation does not eliminate semantic conflicts, such as incompatible API assumptions. Teams should therefore combine overlap detection with normal pull request review and integration testing.
Study-guide topics: parallel agent coordination, branch isolation, merge controls, and conflict detection.
11. 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?
- A. Switch the agent to --allow-all mode
- B. Increase MCP server rate limits
- C. Enable Copilot memory
- D. Add clear, reproducible steps and expected behavior to the issue
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.
12. Frage
An agent working through the Copilot coding agent in GitHub Actions needs additional build dependencies installed before it can run tests. Where should you define this setup?
- A. agents.md
- B. .copilotignore
- C. copilot-instructions.md
- D. copilot-setup-steps.yml
Antwort: D
Begründung:
copilot-setup-steps.yml defines a custom setup workflow (e.g., installing dependencies, configuring environment variables) that runs before the coding agent starts its task, ensuring it has the environment it needs.
13. Frage
Your team wants Copilot's suggestions to reflect knowledge of internal library APIs that are not publicly documented and not present in the codebase being edited. What is the most appropriate solution?
- A. Enable plan mode
- B. Use .copilotignore to hide public APIs
- C. Connect an MCP server that exposes the internal documentation
- D. Add the internal docs as copilot-instructions.md
Antwort: C
Begründung:
For external or large sets of documentation not resident in the repo, an MCP server can expose a searchable knowledge source Copilot can query dynamically, rather than trying to cram everything into static instruction files.
14. Frage
You want the Copilot coding agent to scan a large codebase and propose a full, human- reviewable step-by-step plan before writing any code. What should you do first?
- A. Switch to plan mode
- B. Run /compact
- C. Enable --allow-all
- D. Configure an MCP server
Antwort: A
Begründung:
Plan mode lets the agent operate in a read-only analysis state, producing a structured Markdown plan of intended changes. This allows you to catch architectural issues before the agent starts editing files.
15. Frage
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