GH-600 Exam Papers - Certification GH-600 Test Answers

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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Prepare agent architecture and SDLC processes	15–20%	- 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 - Configure observability and control for autonomous agents 1. Plan and implement the degree of agent autonomy, including guardrails 2. Configure human intervention for autonomous agents without slowing delivery 3. Configure agents to produce inspectable artifacts within standard development tooling - Define boundaries between planning, reasoning, and action 1. Configure agent planning to be distinct from agent execution 2. Configure an agent to output a structured plan 3. Prevent agent action until the agent checks and approves 4. Validate agent plans
Topic 2: Manage memory, state, and execution	10–15%	- Implement agent memory strategies 1. Define memory expiration, pruning, and reset rules 2. Choose between short-term, long-term, and external memory 3. Scope agent memory to task-relevant information - 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. Prevent stale context 2. Share agent state 3. Prevent conflicting context
Topic 3: Implement guardrails and accountability	10–15%	- Implement guardrails and human-in-the-loop workflows 1. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes 2. Identify the subset of actions that require human judgment 3. Block actions that violate defined security, compliance, or Responsible AI policies 4. Scope permissions and execution contexts to enforce least-privilege access 5. Preserve execution velocity by minimizing approvals that do not materially reduce risk - 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
Topic 4: 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 - 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. Evaluate the execution context for an agent 4. Configure an agent to handle environment-specific constraints 5. Configure an agent to be invoked in a CI workflow 6. Configure an agent's scope to a specific repository - Operate agents with safe execution paths and robust error handling 1. Implement retries 2. Implement traceability and accountability for agent actions 3. Implement escalation paths 4. Implement rollbacks 5. Implement error handling - Configure MCP servers 1. Configure a GitHub remote MCP server 2. Add an MCP server as a tool to an agent 3. Configure MCP allow lists 4. Configure MCP registries
Topic 5: 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 - Define success criteria and evaluation signals for agent tasks 1. Generate evaluation signals by using automated scanning tools 2. Align evaluation criteria with development intent 3. Specify expected outcomes and operational constraints for agent tasks 4. Identify qualitative and quantitative evaluation signals to evaluate agents - Tune agent behavior based on evaluation results 1. Revise instructions, workflows, or constraints 2. Refine memory usage 3. Refine tool usage and tool access
Topic 6: Orchestrate multi-agent coordination	15–20%	- 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 - 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. Perform post-hoc analysis of multi-agent behavior 3. Configure multi-agent workflows to produce artifacts suitable for review and audit

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Microsoft Developing in Agentic AI Systems Sample Questions (Q17-Q22):

NEW QUESTION # 17
You have a GitHub repository that uses GitHub Actions to validate pull requests opened by the GitHub Copilot coding agent. The workflow runs unit tests and a linter on pull request triggers, and Copilot opens draft pull requests on dedicated branches while iterating by using commits.
You discover that when multiple Copilot sessions push updates to the same pull request branch in quick succession, multiple workflow runs execute concurrently.
You need to enable parallel workflow executions across different pull request branches.
How should you configure workflow-level concurrency? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 18
While using agent mode in VS Code, you want Copilot to run a specific test suite as a validation step after making changes, without manually invoking the terminal each time. What feature enables this?

A. .copilotignore
B. MCP server integration
C. Tool/terminal invocation permissions in agent mode
D. CODEOWNERS

Answer: C

Explanation:
Agent mode can be granted permission to invoke terminal commands (like running a test suite) as part of its autonomous workflow, allowing it to self-validate changes rather than requiring the developer to run tests manually each time.

NEW QUESTION # 19
You have a repository on github.com that uses the GitHub Copilot coding agent.
You also use the GitHub Copilot CLI locally to reproduce failures and continue the same work from your terminal.
You need to verify the current token usage.
Which Copilot CLI slash command should you run?

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

Answer: B

Explanation:
To verify your current token usage in the GitHub Copilot CLI, you should run the /context slash command.
When you execute /context during your interactive terminal session, it visualizes your context window breakdown. It helps you track token consumption by explicitly mapping out:
The active AI model being used.
The number of used tokens versus total capacity.
Token distribution among the System Prompt, System/MCP Tools, and active Message History.
Remaining Free Space and reserved response buffers.
Reference:
https://docs.github.com/en/copilot/how-tos/copilot-cli/use-copilot-cli/overview

NEW QUESTION # 20
You have a GitHub repository that has a GitHub Actions workflow. The workflow runs an AI agent that deploys a containerized service.
You need to update the workflow so that only one production deployment can run at a time, and any currently running production deployments are cancelled when a new run starts.
How should you complete the workflow? To answer, drag the appropriate values to the correct targets.

Answer:

Explanation:

NEW QUESTION # 21
You have a GitHub Enterprise organization that uses GitHub Copilot.
You discover that GitHub Copilot Chat responses in Microsoft Visual Studio Code are influenced by earlier, unrelated troubleshooting prompts from the same conversation.
You need to ensure that the Copilot Chat conversation context is limited to information relevant to the current work item. The solution must minimize effort.
What should you do? To answer, select the appropriate options in the answer area.

Answer:

Explanation:

NEW QUESTION # 22
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