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| Certification Vendor: | Microsoft / GitHub |
|---|---|
| Exam Name: | Developing in Agentic AI Systems |
| Exam Number: | GH-600 |
| Certificate Validity Period: | 1 year |
| Available Languages: | English |
| Exam Price: | $165 USD |
| Real Exam Qty: | Approximately 40–60 |
| Exam Duration: | 120 minutes |
| Exam Format: | Multiple-choice questions, Proctored exam, Interactive items, Scenario-based questions |
| Passing Score: | 700/1000 |
| Related Certifications: | GitHub Certified: Agentic AI Developer |
| Sample Questions: | ![]() |
| Exam Way: | Proctored exam delivered through Pearson VUE; online and test-center delivery options are available. |
| Pre Condition: | No formal prerequisite certification is specified. Candidates should have experience with the software development lifecycle (SDLC), GitHub workflows and controls, code quality, security and review practices, and coding agents such as GitHub Copilot, MCP servers, custom instructions, custom agents, tools, and Copilot setup steps. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/agentic-ai-developer/ |
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Manage memory, state, and execution | 10–15% | - Implement agent memory strategies
|
| Topic 2: Orchestrate multi-agent coordination | 15–20% | - Manage the lifecycle of agents within multi-agent workflows
|
| Topic 3: Prepare agent architecture and SDLC processes | 15–20% | - Configure observability and control for autonomous agents
|
| Topic 4: Implement guardrails and accountability | 10–15% | - Define autonomy levels
|
| Topic 5: Implement tool use and environment interaction | 20–25% | - Operate agents with safe execution paths and robust error handling
|
| Topic 6: Perform evaluation, error analysis, and tuning | 15–20% | - Define success criteria and evaluation signals for agent tasks
|
Whichever matches your habits — all three are built around the same Microsoft Developing in Agentic AI Systems content. The PDF version suits paper readers: print it out, mark it up, and share pages with a study partner. The PC test engine suits computer-based learners: it simulates the real exam scene, lets you set a time limit like the live GH-600 exam, flags your mistakes, and reminds you to re-practice them daily. The online APP includes every software function and runs on Windows, Mac, Android, and iOS. Choose the one you will actually use every day.
The GH-600 exam contains Approximately 40–60 questions to be completed in 120 minutes minutes. That pace is exactly what the PC test engine's timed mode trains — set the same limit in practice, and the real clock stops being a surprise.
To pass the GH-600 exam you need 700/1000, and registration costs $165 USD. Set against that fee, thorough preparation is the smaller expense by far — and the one most likely to protect the larger one.
The official outline divides the GH-600 exam into weighted domains, including:
The Microsoft Developing in Agentic AI Systems practice questions at ValidBraindumps follow these same objectives, whichever of the three study formats you use.
Yes. We understand that many candidates prefer not to advertise how they prepare. ValidBraindumps runs a strict information protection system: your personal details and your GH-600 exam purchase are kept secret and safe, and never disclosed to any third party. You can order the Microsoft Developing in Agentic AI Systems materials with complete peace of mind.
No formal prerequisite certification is specified. Candidates should have experience with the software development lifecycle (SDLC), GitHub workflows and controls, code quality, security and review practices, and coding agents such as GitHub Copilot, MCP servers, custom instructions, custom agents, tools, and Copilot setup steps.
The GH-600 exam is the official assessment behind the Microsoft Developing in Agentic AI Systems certification from Microsoft. Many IT professionals pursue it as a step toward leadership roles, because the credential verifies practical command of the published objectives. Solid preparation — not luck — is what carries candidates through it.
You have a GitHub Copilot Enterprise subscription. Developers use Microsoft Visual Studio Code and GitHub Copilot.
You have the following Model Context Protocol (MCP) configuration in Visual Studio Code:
{
" servers " : {
" mcp1 " : {
" command " : " npx " ,
" args " : [ " -y " , " @microsoft/mcp-server-test " ]
},
" mcp2 " : {
" command " : " uvx " ,
" args " : [ " mcp-server-test " , " --db-path " , " pubs.db " ]
}
}
}
For each statement, select Yes if the statement is true. Otherwise, select No.
Correct Answer:

Explanation:
Statement
Answer
mcp1 is a Python-based MCP server.
No
mcp2 is a Python-based MCP server.
Yes
mcp1 and mcp2 are configured to use a specific package version.
No
The npx command runs packages through the Node.js ecosystem, so mcp1 is not configured as a Python- based MCP server. Its package name is supplied without an explicit version suffix.
The uvx command runs Python tools and packages through the uv ecosystem. Therefore, mcp2 is configured as a Python-based MCP server. Its --db-path argument supplies server-specific configuration; it does not pin a package release.
Neither server specifies a version, such as @[email protected] for an npm package or an equivalent version constraint for a Python package. Without a pinned version, the resolved package may change over time, which can affect reproducibility and behavior.
Study-guide topics: MCP server configuration, Node.js tooling, Python tooling, and dependency version control.
You have a GitHub Enterprise repository.
An agent opens pull requests to the main branch.
You need to ensure that changes to .github/workflows/* and /infra/* require approval from designated reviewers before merge.
What should you configure?
Correct Answer: C 🗳️
Explanation: Only visible for ValidBraindumps members. You can sign-up / login (it's free).
You have a GitHub repository that uses the GitHub Copilot coding agent.
The agent is assigned to a long-running issue and has already opened a draft pull request linked to the issue.
The pull request timeline shows Copilot started work and the pull request description shows periodic status updates.
After 70 minutes, the pull request stops receiving new commits, and the agent session log indicates that the session has timed out. In a pull request comment thread, the agent then proposes changes that no longer match the latest repository guidance.
You need to resume execution in a way that reestablishes the correct context and produces new commits in the existing draft pull request.
What should you do?
Correct Answer: B 🗳️
Explanation: Only visible for ValidBraindumps members. You can sign-up / login (it's free).
You have multiple GitHub Copilot coding agents that run tasks concurrently.
You live stream the session log output and see the following.
* agent execution (ID 987654321)
Run ./scripts/agent-run.sh
--------------------------------
[agent] Starting task: Clean up infra configs
[agent] Executing step: Remove unused files
rm -rf /infra
Error: Command blocked by policy
Reason: destructive_operation_detected
[agent] Escalating to human review...
X Run agent task
X Process completed with exit code 1
X agent-execution failed
What is a possible cause of the error?
Correct Answer: D 🗳️
Explanation: Only visible for ValidBraindumps members. You can sign-up / login (it's free).
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.
Correct Answer:

Explanation:
Target
Correct selection
Checkout ref
${{ github.sha }}
File checked by test -f
github/agents/rollout-bot.agent.md
Profile passed to the deployment script
github/agents/rollout-bot.agent.md
The workflow must deploy an agent profile from a specific repository snapshot. Setting the checkout reference to ${{ github.sha }} identifies the commit associated with the workflow event, rather than following a branch reference that might move as new commits arrive. The checkout action supports selecting a commit SHA through its ref input.
Both subsequent operations must use .github/agents/rollout-bot.agent.md. The existence check confirms that the expected profile is present in the checked-out revision, and the deployment script receives that same version-controlled file. Using a different path would break the relationship between the validated profile and the deployed profile.
Rollback requires one additional operational distinction: reverting a change creates a new commit containing the restored content. The deployment must run against that revert commit. Re-running the original failed workflow run retains its original SHA and does not automatically deploy the later revert.
This approach keeps agent behavior, repository history, and deployment provenance aligned, making the profile's effective version identifiable and recoverable.
Relevant curriculum topics are durable state, controlled rollback, and continuity of agent configuration across executions.
References: actions/checkout , Re-running workflows and jobs .
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