Microsoft GH-600 Questions & Answers - in .pdf
- Total Q&A: 111
- Update: Oct 02, 2026
- Price: $59.99
- Vendor: Microsoft
- Exam Code: GH-600
- Exam Name: Developing in Agentic AI Systems
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Microsoft GH-600 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|
| Topic 1: Orchestrate multi-agent coordination | 15–20% | - Configure observability for multi-agent behavior by using logs, artifacts, and operational signals
- 1. Configure multi-agent workflows to produce artifacts suitable for review and audit
- 2. Perform post-hoc analysis of multi-agent behavior
- 3. Document key decisions, handoffs, and outcomes across 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
- 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
- Operate and manage multi-agent workflows
- 1. Apply an orchestration pattern to coordinate multiple agents
- 2. Detect and resolve agent conflicts, including overlapping code changes, duplicated effort, and contradictory outputs
- 3. Configure agent isolation for parallel execution
|
| Topic 2: Implement tool use and environment interaction | 20–25% | - Configure MCP servers
- 1. Configure MCP allow lists
- 2. Configure MCP registries
- 3. Configure a GitHub remote MCP server
- 4. Add an MCP server as a tool to an agent
- Integrate agents within development environments
- 1. Evaluate the execution context for an agent
- 2. Enable an agent to perform autonomous actions, including creating branches and pull requests
- 3. Configure an agent to use branch-based scope
- 4. Configure an agent's scope to a specific repository
- 5. Configure an agent to be invoked in a CI workflow
- 6. Configure an agent to handle environment-specific constraints
- Operate agents with safe execution paths and robust error handling
- 1. Implement traceability and accountability for agent actions
- 2. Implement escalation paths
- 3. Implement rollbacks
- 4. Implement error handling
- 5. Implement retries
- Select and configure agent tools
- 1. Configure agent tool permissions
- 2. Configure agent tools
- 3. Identify required tools
|
| Topic 3: Perform evaluation, error analysis, and tuning | 15–20% | - Define success criteria and evaluation signals for agent tasks
- 1. Identify qualitative and quantitative evaluation signals to evaluate agents
- 2. Generate evaluation signals by using automated scanning tools
- 3. Specify expected outcomes and operational constraints for agent tasks
- 4. Align evaluation criteria with development intent
- Tune agent behavior based on evaluation results
- 1. Refine tool usage and tool access
- 2. Refine memory usage
- 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
|
| Topic 4: 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. Prevent agent action until the agent checks and approves
- 4. Configure an agent to output a structured plan
- 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 agents to produce inspectable artifacts within standard development tooling
- 2. Configure human intervention for autonomous agents without slowing delivery
- 3. Plan and implement the degree of agent autonomy, including guardrails
|
| Topic 5: Manage memory, state, and execution | 10–15% | - Ensure continuity of agent memory and state across tools and environments
- 1. Prevent stale context
- 2. Prevent conflicting context
- 3. Share agent state
- Implement agent memory strategies
- 1. Define memory expiration, pruning, and reset rules
- 2. Scope agent memory to task-relevant information
- 3. Choose between short-term, long-term, and external memory
- Persist agent state and manage context drift
- 1. Capture task progress and decisions as durable artifacts
- 2. Detect and correct drift during extended agent execution
- 3. Resume agent work without repeating steps or diverging from prior decisions
|
| Topic 6: 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. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes
- 3. Identify the subset of actions that require human judgment
- 4. Scope permissions and execution contexts to enforce least-privilege access
- 5. Block actions that violate defined security, compliance, or Responsible AI policies
|
Microsoft Developing in Agentic AI Systems Sample Questions:
Question #1
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.
Question #2
You have a GitHub Enterprise repository that runs an autonomous agent by using a GitHub Actions workflow. The workflow has the following jobs:
agent-run that generates trace.json and plan.md
review that waits for human approval before continuing
deploy that uses the outputs from agent-run
You need to make the files inspectable in the GitHub Actions UI and ensure that the files are available to the review and deploy jobs.
What should you do in the workflow?
A. Upload trace.json and plan.md as workflow artifacts in agent-run, and download the files inside the jobs.
B. Use dependency caching to store trace.json and plan.md.
C. Store trace.json and plan.md on a network share and have later jobs retrieve them from the share.
D. Commit trace.json and plan.md back to the repository from agent-run.
Question #3
You use the GitHub Copilot CLI in a developer terminal.
You plan to use Copilot CLI programmatically to summarize recent commits.
You need to ensure that Copilot performs a low-risk, read-only action without granting Copilot broad write or execution capability.
Which Copilot CLI configuration should you use?
A. copilot -p "<prompt>" --allow-tool 'write'
B. copilot -p "<prompt>" --allow-all-tools
C. copilot -p "<prompt>" --deny-tool 'shell(git)'
D. copilot -p "<prompt>" --allow-tool 'shell(git)'
Question #4
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. An environment variable
B. An Actions repository secret
C. An environment variable mapping
D. The JSON configuration
E. A default environment variable
Question #5
You have a GitHub Actions workflow that runs two jobs named agent-plan and agent-implement on pull requests.
The agent-plan job generates an implementation plan that downstream jobs must consume, but the job currently writes the plan only to the console.
You need to capture the implementation plan as a step output and configure the workflow so that downstream jobs can reference the output.
How should you configure the workflow? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Solutions:
Question #1
Correct Answer: Only visible for members | Question #2
Correct Answer: A | Question #3
Correct Answer: D | Question #4
Correct Answer: C | Question #5
Correct Answer: Only visible for members |
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