Exam: GH-600
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GH-600 Developing in Agentic AI Systems Exam
The GH-600 Developing in Agentic AI Systems exam is associated with the GitHub Certified: Agentic AI Developer credential. It focuses on the practical skills required to design, implement, operate, evaluate, and govern AI agents within software development workflows using GitHub and related agent technologies.
Candidates should understand how AI agents interact with development environments, use tools, work with Model Context Protocol (MCP) servers, manage memory and state, evaluate agent performance, coordinate multiple agents, and apply security and governance controls.
The official study guide identifies six major areas: agent architecture and SDLC processes, tool use and environment interaction, memory/state/execution, evaluation and tuning, multi-agent coordination, and guardrails/accountability.
GH-600 Exam Topics Covered
1. Prepare Agent Architecture and SDLC Processes — 15–20%
Topics include:
Integrating agents into the software development lifecycle
Identifying appropriate tasks for AI agents
Agent inputs, outputs, and success criteria
Common agent anti-patterns
Planning versus reasoning versus action
Structured agent plans
Plan validation
Human approval before autonomous actions
Agent observability
Agent autonomy levels
Guardrails for autonomous agents
Inspectable development artifacts
Human intervention and oversight
2. Implement Tool Use and Environment Interaction — 20–25%
This is the largest weighted domain in the official skills outline.
Important topics include:
Selecting appropriate agent tools
Configuring agent tools
Tool permissions
Least-privilege access
MCP servers
GitHub remote MCP server
MCP registries
MCP allow lists
Agent execution environments
Repository-specific agent scope
Branch-based scope
CI workflow integration
Autonomous branch creation
Pull request creation
Environment-specific restrictions
Safe execution paths
Error handling
3. Manage Memory, State, and Execution — 10–15%
Candidates should understand:
Agent memory
Short-term and long-term context
Agent state
Context management
Persistent information
External memory
Durable artifacts
Context drift
Execution state
Managing agent continuity
Maintaining reliable agent behavior
4. Perform Evaluation, Error Analysis, and Tuning — 15–20%
Key areas include:
Defining agent success criteria
Evaluating agent outputs
Measuring agent performance
Error analysis
Root-cause analysis
Automated evaluation
Code scanning
Reviewing generated artifacts
Identifying failure patterns
Agent tuning
Improving reliability
Testing agent behavior
5. Orchestrate Multi-Agent Coordination — 15–20%
Study areas include:
Multi-agent architectures
Agent roles
Agent-to-agent coordination
Task delegation
Agent communication
Sequential and parallel execution
Agent isolation
Conflict resolution
Recovery strategies
Coordinating specialized agents
Managing dependencies between agents
6. Implement Guardrails and Accountability — 10–15%
Important concepts include:
Agent autonomy controls
Human-in-the-loop workflows
Least-privilege permissions
Security boundaries
Approval mechanisms
Repository controls
Branch protection
Auditability
Accountability
Monitoring autonomous actions
Safe agent execution
Responsible AI practices
These six areas correspond to the official GH-600 skills measured by Microsoft/GitHub.
GH-600 Exam Preparation
Successful preparation should combine conceptual study with practical experience. Microsoft recommends hands-on training and provides learning paths covering agentic AI foundations, agent architecture and SDLC integration, and tooling, MCP, and agent execution environments.
CertKingdom GH-600 preparation resources can be positioned around:
GH-600 practice questions
GH-600 study material
GH-600 mock tests
GH-600 exam preparation
Agentic AI practice
GitHub Copilot agent training
MCP practice scenarios
Agent architecture questions
Multi-agent orchestration practice
Guardrails and security scenarios
Evaluation and tuning exercises
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Prepare for GH-600 with CertKingdom's exam-focused study material, practice questions, mock tests, and training resources. Build your understanding of agent architecture, GitHub Copilot agents, MCP, memory, evaluation, multi-agent orchestration, and agent security before taking the Developing in Agentic AI Systems exam.
GH-600 Exam Preparation: Prepare for Developing in Agentic AI Systems with practice questions, study material, mock tests, and agentic AI training.
Topic 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 repo1
that 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 repo1 use 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.
Questions and Answers PDF 3/156
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.
Question: 1
HOTSPOT
You need to implement agent2 to meet the technical requirements.
How should you complete the YAML configuration? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
name: implementation-planner
description: Creates detailed implementation plans and technical specifications in markdown format tools: [
'microsoftdocs/mcp/docs_search',
'microsoftdocs/mcp/docs_fetch'
]
The accompanying image includes empty dropdown controls and recreated practice alternatives.
Answer:
Explanation:
Target Selection
Dropdown 1 'search'
Dropdown 2 'read'
The visible configuration describes an implementation planner. Providing search and read supports
the information-gathering work needed to produce a technically grounded plan: locating relevant
code and inspecting its contents. The listed documentation tools supplement repository context with
external technical reference material.
This tool selection also establishes a practical boundary between planning and implementation. A
planner can explain proposed changes and produce Markdown in its response without needing
permission to modify repository files. Granting editing or command-execution capabilities would
expand its authority beyond the visible planning role.
The distinction between generating Markdown and saving a Markdown file is important. The
selected tools support producing a plan in conversation. If the requirements instead demanded that
the planner write that plan directly into the repository, an appropriate file-writing capability or a
separate persistence step would be necessary.
Tool names must also resolve to capabilities available in the configured environment. Merely listing a
documentation tool does not install or authenticate its MCP server.
Study-guide topics: tool selection, least-privilege planning, and environment capabilities. Reference:
VS Code—Use tools with agents.
Question: 2
Before App1 is upgraded, you need to verify each individual upgrade step and whether all tests have passed.
Which file should you use?
A.
B.
C.
D.
Answer: D
Explanation:
The tasks.md file is the execution-tracking document for the upgrade scenario. It records individual
tasks, their validation criteria, and their completion status. Microsoft’s upgrade walkthrough
specifically directs users to review this file when checking the status of every upgrade step.
A task can include several concrete verification activities, such as restoring dependencies, building
the solution, correcting compilation errors, running the test suite, and rerunning tests after fixes.
Reviewing these entries reveals whether implementation has merely been attempted or has satisfied
the defined checks.
The assessment identifies upgrade issues and scope; it does not serve as the live record of completed
work. A plan describes intended actions and ordering, which also differs from evidence that
execution and testing succeeded.
The phrase “before App1 is upgraded” requires temporal care. Before execution, tasks.md identifies
the steps and checks that must be performed. It cannot prove that future tests have already passed.
Completion and test results become available as the agent executes and updates the tasks.
Study-guide topics: execution validation, task status, and acceptance criteria. Reference: Microsoft
Learn—Execute and verify a Copilot upgrade.
===============
Question: 3
You need to make changes to repo1 to support the planned changes for the agents.
What should you modify?
A.
B. .vscode/settings.json
C. .github/agents/*.agent.md
D. .vscode/mcp.json
Answer: C
Explanation:
Repository custom agent profiles belong in .github/agents/ and use Markdown files with agent
configuration and instructions. When the intended changes concern the agents’ roles, available
tools, or operating guidance, these profiles are the appropriate implementation point.
An agent profile provides a durable definition that can be shared with other repository contributors.
Keeping that definition under version control makes changes reviewable and enables the team to
associate a particular agent configuration with the code revision used during evaluation. This is
useful when diagnosing why an agent’s behavior changed after its instructions or capabilities were modified.
Editor settings and MCP connection settings address different concerns. They can affect the
environment in which an agent operates, but they do not replace the agent’s own profile. Changing a
server connection is appropriate for transport or authentication requirements; changing an agent
profile is appropriate for agent behavior and capability selection.
The source selects C. Its applicability depends on the omitted planned changes actually concerning
custom agent configuration.
Study-guide topics: agent profiles, configuration management, and SDLC traceability. Reference:
GitHub—Creating custom agents.
===============
Question: 4
You need to troubleshoot the issue reported by Dev1.
What should you review?
A. The GitHub Actions usage metrics of repo1.
B. The agent session log in the Agents panel.
C. The GitHub Actions runner log for the session job.
D. The GITHUB_TOKEN permissions block in the agent1 workflow.
Answer: B
Explanation:
The agent session log is the appropriate starting point when the reported problem concerns an
agent’s interaction, execution sequence, or tool activity within the development environment. It
provides information closer to the failing behavior than repository-level workflow usage statistics.
A useful investigation establishes what the agent was asked to do, which operations it attempted,
what responses those operations returned, and where progress stopped or diverged from the
intended result. Session diagnostics can help distinguish an instruction problem from an unavailable
Questions and Answers PDF 8/156
tool, a rejected operation, or an execution error. VS Code also provides detailed chat debugging
facilities for inspecting interactions and diagnosing failures.
GitHub Actions usage metrics primarily describe workflow consumption. Runner logs are relevant
when evidence points to a hosted workflow or runner problem. The token permissions block
becomes relevant when the failure concerns authorization to a GitHub resource. None should be
assumed to be the root cause before examining the reported execution.
The PDF selects B; the underlying Dev1 incident description is not included, so this selection assumes
a session-level agent issue.
Study-guide topics: diagnostic evidence, tool-call inspection, and failure localization. Reference: VS
Code—Debug chat interactions.
Question: 5
You need to enable Copilot memory to support the planned changes.
What should you configure?
A. The personal Copilot settings of each developer
B. The Copilot settings of each repository
C. The agent profile of agent1
D. The Copilot settings of the organization
Answer: D
Explanation:
Copilot memory is an organization-level capability. Configuring it through the organization’s Copilot
settings establishes the governance boundary, availability, and policy controls consistently for the
developers and repositories covered by that organization.
Personal settings are unsuitable because they create inconsistent behavior between developers and
do not provide centralized administration. A repository-level configuration can supply repository
instructions and scoped context, but it does not replace the organization-level control that enables
and governs the memory capability. An individual agent profile defines role behavior, tool access,
Questions and Answers PDF 9/156
prompts, and delegation settings; it is not the tenant-level location for enabling Copilot memory.
Organization configuration is particularly important when agents must retain approved context,
recurring pReference, and workflow guidance across sessions without each developer configuring
behavior independently. Central governance also allows administrators to maintain predictable
controls over which organizational information is available to Copilot.
Study-guide topics: organizational governance, persistent context, and centralized agent configuration.
Question: 6
While upgrading App1, the agent identifies 47 issues, including a security vulnerability, and 46 API
incompatibilities across different projects.
Which two actions are unsafe to delegate to the agent and require human involvement? Each correct
answer presents a complete solution.
NOTE: Each correct selection is worth one point.
A. Generate the assessment.md file.
B. Approve all Git commits.
C. Validate whether the tasks.md file exists.
D. Validate the assessment.md file for accuracy.
E. Review the plan.md file for dependencies.
Answer: B, D
Explanation:
The intended distinction is between delegating analytical work and allowing the agent to provide the
final approval of its own work. Approving all Git commits would remove independent review of
changes that include security-sensitive remediation and compatibility modifications across multiple projects.
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GH-600 Developing in Agentic AI Systems Exam
1. What is the GH-600 exam?
GH-600 is Developing in Agentic AI Systems, associated with the GitHub Certified: Agentic AI Developer credential. The exam focuses on developing, operating, evaluating, and governing AI agents in software-development workflows.
2. What are the GH-600 exam topics?
The exam covers six domains: agent architecture and SDLC, tool use and environments, memory/state/execution, evaluation and tuning, multi-agent coordination, and guardrails/accountability.
3. Which GH-600 domain has the highest weighting?
Implement Tool Use and Environment Interaction has the highest weighting at 20–25%.
4. Is MCP important for GH-600?
Yes. MCP server configuration is specifically included under the tool-use and environment-interaction domain, including remote GitHub MCP servers, registries, and allow lists.
5. Does GH-600 cover GitHub Copilot agents?
Yes. The official audience profile expects candidates to have experience with coding agents such as GitHub Copilot, MCP servers, custom instructions, custom agents, tools, and Copilot setup steps.
6. Does GH-600 cover agent memory?
Yes. Managing memory, state, and execution is one of the six official exam domains.
7. Does GH-600 cover multi-agent systems?
Yes. Orchestrate Multi-Agent Coordination represents 15–20% of the exam objectives.
8. Are guardrails included in GH-600?
Yes. Implementing guardrails and accountability represents 10–15% of the official skills measured.
9. Does the exam include practical scenarios?
Candidates should prepare for scenario-based application of the objectives rather than relying solely on memorization. Community study resources also describe scenario/case-study and interactive question formats.
10. How long is the GH-600 exam?
Microsoft currently states that candidates have 120 minutes to complete the assessment.
11. What should I study first for GH-600?
Start with the official six domains, giving particular attention to tool use, environment interaction, MCP, agent architecture, evaluation, and multi-agent coordination.
12. Do I need hands-on experience?
Hands-on practice is strongly recommended. Microsoft's study guide recommends training and practical experience before taking the exam.
13. What should I know about agent permissions?
You should understand how to select tools, configure permissions, establish appropriate agent scope, and use safe execution paths. Least-privilege access is particularly important for secure agent operation.
14. What is the best way to prepare for GH-600?
Use the official Microsoft/GitHub learning resources together with hands-on practice, structured notes, and original practice questions. Microsoft provides two learning paths specifically for Developing in Agentic AI Systems.
15. Can GH-600 practice questions replace studying?
No. Practice questions should reinforce your understanding rather than replace learning. For GH-600, candidates should understand why an agent architecture, tool permission, MCP configuration, evaluation method, or guardrail is appropriate in a given scenario.
Get ready for GH-600 Developing in Agentic AI Systems with CertKingdom practice tests covering agent architecture, MCP, memory, evaluation, orchestration, and guardrails.
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