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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Manage memory, state, and execution	10-15%	- Ensure continuity of agent memory and state across tools and environments 1. Prevent conflicting context 2. Prevent stale context 3. Share agent state - 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 - Persist agent state and manage context drift 1. Resume agent work without repeating steps or diverging from prior decisions 2. Capture task progress and decisions as durable artifacts 3. Detect and correct drift during extended agent execution
Topic 2: 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. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes 2. Identify the subset of actions that require human judgment 3. Preserve execution velocity by minimizing approvals that do not materially reduce risk 4. Block actions that violate defined security, compliance, or Responsible AI policies 5. Scope permissions and execution contexts to enforce least-privilege access
Topic 3: Implement tool use and environment interaction	20-25%	- Integrate agents within development environments 1. Evaluate the execution context for an agent 2. Configure an agent to be invoked in a CI workflow 3. Configure an agent to handle environment-specific constraints 4. Configure an agent to use branch-based scope 5. Enable an agent to perform autonomous actions, including creating branches and pull requests 6. Configure an agent's scope to a specific repository - 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 the MCP registries - Operate agents with safe execution paths and robust error handling 1. Implement error handling 2. Implement rollbacks 3. Implement retries 4. Implement traceability and accountability for agent actions 5. Implement escalation paths - Select and configure agent tools 1. Identify required tools 2. Configure agent tool permissions 3. Configure agent tools
Topic 4: 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. Align evaluation criteria with development intent 4. Specify expected outcomes and operational constraints for agent tasks - Tune agent behavior based on evaluation results 1. Refine tool usage and tool access 2. Revise instructions, workflows, or constraints 3. Refine memory usage - 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
Topic 5: 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. Configure human intervention for autonomous agents without slowing delivery 3. Plan and implement the degree of agent autonomy, including guardrails - 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. 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
Topic 6: Orchestrate multi-agent coordination	15-20%	- Detect and respond to multi-agent failures and degraded behavior 1. Respond to degraded behavior or coordination across agents 2. Identify failed, partial, or stalled agent executions 3. Implement multi-agent recovery patterns, including rollback and human-in-the-loop - 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 - 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. Document key decisions, handoffs, and outcomes across agents 3. Perform post-hoc analysis of multi-agent behavior - Operate and manage multi-agent workflows 1. Detect and resolve agent conflicts, including overlapping code changes, duplicated effort, and contradictory outputs 2. Configure agent isolation for parallel execution 3. Apply an orchestration pattern to coordinate multiple agents

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Microsoft Developing in Agentic AI Systems Sample Questions (Q20-Q25):

NEW QUESTION # 20
Drag and Drop Question
You have a GitHub repository that has a GitHub Actions workflow. The workflow runs an AI agent.
You need to ensure that the default GITHUB_TOKEN permissions are read-only, and write access is granted to only the job that performs repository write operations. The workflow must be able to create and approve pull requests only when explicitly enabled.
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:
How should you complete the workflow?
Box 1: contents: read
Set Global Read-Only Permissions
To enforce the principle of least privilege, the top-level permissions block must strip all default write capabilities from the GITHUB_TOKEN. Setting contents: read allows the workflow to fetch the code but prevents any accidental repository modifications.
Box 2: contents: write
Grant Job-Level Write Access
Only the specific job responsible for modifying the repository should receive write access. The update_artifacts job requires permission to push code modifications back to the repository.
Box 3: pull-requests: write
To successfully create and approve pull requests, you must explicitly enable this capability in your GitHub repository settings:
Reference:
https://docs.github.com/en/organizations/managing-organization-settings/disabling-or-limiting-github-actions-for-your-organization

NEW QUESTION # 21
You are analyzing the following agent logs.
2026-03-19 21:00:59.905 [info] ccreq:6343bff1.copilotmd | success | gpt-5.3-codex | 10385ms | [panel/editAgent]
2026-03-19 21:01:09.644 [info] ccreq:4612e90d.copilotmd | success | gpt-5.3-codex | 9806ms | [panel/editAgent]
2026-03-19 21:01:09.856 [info] [ToolCallingLoop] Stop hook result: shouldContinue=false, reasons=undefined
2026-03-19 21:01:30.816 [info] ccreq:9dcd0468.copilotmd | success | gpt-5.3-codex | 5489ms | [panel/editAgent]
2026-03-19 21:01:31.475 [error] Error from tool mcp_microsoftdocs_microsoft_docs_search with args
{"query":"VS Code custom agent name from frontmatter and filename behavior"}:
Cannot read properties of undefined (reading 'invoke'):
TypeError: Cannot read properties of undefined (reading 'invoke')
You need to classify the error in a report for your company's CTO.
How should you classify the error?

A. Context issue
B. Tool misuse
C. Reasoning error
D. Network issue

Answer: B

Explanation:
C is the closest available classification because the explicit failure occurs during a tool invocation. The log identifies the Microsoft documentation search tool and records a JavaScript TypeError involving an undefined object's invoke property. The preceding successful model requests do not establish successful tool execution; model generation and external tool invocation are separate operations.
For an operational report, the more precise description is tool invocation or integration failure. The evidence does not prove that the agent selected an inappropriate tool or supplied invalid arguments. An undefined invocation object can also result from an extension defect, stale tool registration, or another runtime integration problem. Therefore, "tool misuse" should be understood as the question's broad tool-related category rather than a proven agent-behavior defect.
A network classification would require supporting evidence such as connection failure, DNS errors, or an HTTP response. A reasoning or context classification would require evidence that the agent's interpretation or supplied information caused the failure.
The next diagnostic step is to inspect the MCP server's output and registration state.
Relevant curriculum topics are classifying failure causes and distinguishing model behavior from tool and environment failures.
Reference:

NEW QUESTION # 22
You have a GitHub repository that uses the GitHub Copilot coding agent to resolve issues in an ephemeral GitHub Actions environment.
Builds fail unless an environment variable named NPM_TOKEN is available, because the repository depends on a private package registry.
A workflow named .github/workflows/copilot-setup-steps.yml was added but was merged into a non-default branch and does NOT run when new agent sessions start.
You need to ensure that for every new agent session, the setup steps run, and the private registry token is available as NPM_TOKEN, without hardcoding secrets in the repository.
What should you do?

A. Create an organization-level Actions secret named NPM_TOKEN and reference it from .github/copilot-instructions.md.
B. Add the token directly under env: npm_token: in the workflow file.
C. Disable the agent's integrated firewall and rename the workflow.
D. Move .github/workflows/copilot-setup-steps.yml to the repository's default branch and store the token as a GitHub Actions environment secret named NPM_TOKEN in the Copilot environment.

Answer: D

Explanation:
The Copilot setup workflow must be present on the repository's default branch to be used for new coding-agent sessions. Moving .github/workflows/copilot-setup-steps.yml there ensures the setup procedure is discoverable for every newly created agent environment.
The package-registry credential should be stored as an environment secret in the Copilot environment and exposed using the required variable name, NPM_TOKEN. This permits package installation without committing the token into workflow YAML, instructions, source code, or pull requests.
Disabling network controls does not resolve missing credentials and expands risk unnecessarily. Repository instructions can describe setup expectations but cannot safely substitute for an injected runtime secret. Adding the raw token to YAML permanently exposes it to repository history and anyone with repository access.
Study-guide topics: ephemeral environments, setup workflows, environment secrets, and secure dependency access.

NEW QUESTION # 23
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. .vscode/settings.json
B. .vscode/mcp.json
C. .github/agents/*.agent.md
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 # 24
Your organization wants to ensure the Copilot coding agent cannot push directly to main, and all its changes must go through a pull request review process like any other contributor. What enforces this?

A. /context
B. Standard branch protection rules on the main branch
C. copilot-instructions.md
D. .copilotignore

Answer: B

Explanation:
Branch protection rules apply uniformly regardless of whether the actor is a human or an agent; requiring pull requests and disallowing direct pushes to main ensures the coding agent's changes are always reviewed.

NEW QUESTION # 25
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