Get the Microsoft GH-600 Certification Exam to Boost Your Professional Career

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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Implement tool use and environment interaction	20–25%	- Select and configure agent tools 1. Configure agent tools 2. Configure agent tool permissions 3. Identify required tools - Operate agents with safe execution paths and robust error handling 1. Implement traceability and accountability for agent actions 2. Implement error handling 3. Implement escalation paths 4. Implement retries 5. Implement rollbacks - Configure MCP servers 1. Add an MCP server as a tool to an agent 2. Configure a GitHub remote MCP server 3. Configure MCP allow lists 4. Configure MCP registries - Integrate agents within development environments 1. Enable an agent to perform autonomous actions, including creating branches and pull requests 2. Evaluate the execution context for an agent 3. Configure an agent to use branch-based scope 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
Manage memory, state, and execution	10–15%	- Ensure continuity of agent memory and state across tools and environments 1. Share agent state 2. Prevent conflicting context 3. Prevent stale context - Persist agent state and manage context drift 1. Resume agent work without repeating steps or diverging from prior decisions 2. Detect and correct drift during extended agent execution 3. Capture task progress and decisions as durable artifacts - 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
Perform evaluation, error analysis, and tuning	15–20%	- Define success criteria and evaluation signals for agent tasks 1. Align evaluation criteria with development intent 2. Identify qualitative and quantitative evaluation signals to evaluate agents 3. Generate evaluation signals by using automated scanning tools 4. Specify expected outcomes and operational constraints for agent tasks - 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. Revise instructions, workflows, or constraints 2. Refine tool usage and tool access 3. Refine memory usage
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. Add agents to existing multi-agent workflows 2. Retire agents while preserving auditability and workflow continuity 3. Update, reconfigure, or replace agents without disrupting active workflows - 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. Respond to degraded behavior or coordination across agents 2. Implement multi-agent recovery patterns, including rollback and human-in-the-loop 3. Identify failed, partial, or stalled agent executions
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. Scope permissions and execution contexts to enforce least-privilege access 3. Identify the subset of actions that require human judgment 4. Block actions that violate defined security, compliance, or Responsible AI policies 5. Preserve execution velocity by minimizing approvals that do not materially reduce risk
Prepare agent architecture and SDLC processes	15–20%	- 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 - 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. Prevent agent action until the agent checks and approves 3. Configure an agent to output a structured plan 4. Configure agent planning to be distinct from agent execution

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Microsoft Developing in Agentic AI Systems Sample Questions (Q25-Q30):

NEW QUESTION # 25
You have a GitHub repository that contains an agent named Orchestrator.Orchestrator delegates work to the following specialized subagents:
- Planner reviews issues and creates a plan of action.
- Implementer writes code based on the plan of action.
- Reviewer reviews the code.
You create a new agent named Summarizer that produces a concise summary of the work performed by the other agents.
You need to ensure that Orchestrator can invoke Summarizer as part of its workflow.
What should you do?

A. In the YAML frontmatter of the Orchestrator agent, add Summarizer to the agents list.
B. In the YAML frontmatter of the Reviewer agent, add a handoffs entry that points to the Summarizer agent.
C. In the YAML frontmatter of the Reviewer agent, add Summarizer to the agents list.
D. In the YAML frontmatter of the Orchestrator agent, add Summarizer to the tools list.

Answer: A

Explanation:
To ensure that Orchestrator can invoke the new Summarizer agent as part of its workflow, you should take the following action: In the YAML frontmatter of the Orchestrator agent, add Summarizer to the agents list.
In GitHub Copilot custom agent configurations (defined in .agent.md files), the properties specified in the YAML frontmatter serve distinct purposes:
agents: This field acts as an explicit whitelist of other custom subagents that the parent agent is allowed to invoke and delegate work to. Adding Summarizer here registers it as an authorized subagent within the Orchestrator workflow.
Reference:
https://awesome-copilot.github.com/learning-hub/building-custom-agents/

NEW QUESTION # 26
Drag and Drop Question
You have a GitHub repository that uses a custom GitHub Copilot coding agent defined in the rollout-bot.agent.md file.
You need to update a workflow so that agent-profile changes can be rolled back by reverting a single commit and rerunning the workflow. The workflow must check out the exact commit being deployed and apply the agent profile from the repository at that commit.
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.

Answer:

Explanation:

Explanation:
Box 1: inputs.ref
The specific commit being targeted.
Box 2: agents/rollout-bot.agent.md
The profile file to validate
Box 3: ${{ github.sha }}
The exact commit ref that the checkout action fetched
Reference:
https://octopus.com/devops/github-actions/github-actions-workflows/

NEW QUESTION # 27
During an agentic session, you want to see exactly how many tokens are being consumed by the system prompt, tools, and message history before deciding whether to compact. Which command should you run?

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

Answer: D

Explanation:
/context displays a breakdown of the active context window, including tokens used by the system prompt, MCP/system tools, and message history, along with remaining free space.

NEW QUESTION # 28
Case Study 1 - Contoso, Ltd
Overview
Contoso Ltd. is a software development company located in the United States.
Existing Environment
GitHub Environment
Contoso uses GitHub Enterprise and assigns GitHub Copilot Pro+ licenses to its developers. The developers use Microsoft Visual Studio Code as their IDE.
Contoso has a customer portal. The code for the portal is stored in a GitHub repository named repo1that contains the following:
- A custom agent named agent1 that includes instructions to review specs related to best practices
- A custom instruction file named validate-instructions.md that is used to validate tone of voice and applies to all .md and .txt files
- A custom instruction file named codereview.instructions.md that is used by the Copilot coding agent but is excluded for use by the Copilot code review repo1 has the following structure:
- The front-end is stored in the /frontend folder.
- The API logic is stored in the /api folder.
Contoso has a second repository named repo2 that contains a legacy .NET application named App1 built by using .NET 6. repo2 has a multi-agent workflow for modernization tasks.
Contoso enables the Model Context Protocol (MCP) registry and allows the Microsoft Learn MCP Server. Every developer must configure their own connection to the Learn MCP Server.
Problem Statements
The developers working in repo1 report that the Microsoft Learn documentation is NOT being retrieved when they attempt to validate a design by using agent1.
The testing team at Contoso identifies that the customer portal uses inconsistent UI styles, which leads to customer confusion and branding issues. The UI inconsistencies stem from variations in the folder structure.
Agent Logs
You have the following logs for the multi-agent workflow used in repo2.

Requirements
Planned Changes
Contoso plans to have all agents and developers in repo1use the Microsoft Learn MCP to ensure that reviews are validated by using the appropriate documentation. This must be implemented centrally.
Contoso plans to leverage AI-powered coding agents to implement new portal features and pages.
Technical Requirements
App1 must be upgraded to .NET 10. A previous upgrade attempt was started by using the Copilot modernization agent, but the attempt was never finalized.
You plan to retry the upgrade. You must first analyze App1 by using AI, and then generate a report that contains breaking changes and deprecated patterns before retrying the upgrade.
All AI-generated code for UI styling must adhere to a predefined folder structure.
The architects at Contoso need help building implementation plans for repo1. The company wants to implement a new agent named agent2 to analyze the code base and the code requirements, and then respond with a detailed plan. The agent must NOT be able to edit files or run local commands.
The developers must be able to delegate work to the Copilot coding agent by assigning issues to the agent.
You need to make changes to repo1 to support the planned changes for the agents.
What should you modify?

A. .github/agents/*.agent.md
B. .vscode/mcp.json
C. .vscode/settings.json
D. <project>/.mcp/server.json

Answer: B

Explanation:
Scenario, Planned Changes: Contoso plans to have all agents and developers in repo1 use the Microsoft Learn MCP to ensure that reviews are validated by using the appropriate documentation. This must be implemented centrally.
To centrally configure the Microsoft Learn Model Context Protocol (MCP) server for all developers and agents within a shared repository, you should modify the .vscode/mcp.json file.
Central Repository Configuration: In GitHub Copilot and Visual Studio Code, placing an mcp.json file inside the workspace root's .vscode/ directory ensures that the defined MCP servers are automatically loaded and shared with any developer or Copilot agent who opens that specific project repository.
Tool Exposure: This configuration file maps the external tools provided by the Microsoft Learn Docs MCP server (such as searching and fetching official documentation) directly into the Copilot agentic workflow.
Reference:
https://docs.github.com/en/copilot/how-tos/copilot-on-github/customize-copilot/configure-mcp-servers

NEW QUESTION # 29
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.

Answer:

Explanation:

NEW QUESTION # 30
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