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An internationally recognized Microsoft certification is a capacity standard employers understand in any country. BraindumpsPrep lays the groundwork for passing Microsoft Developing in Agentic AI Systems with 110 verified GH-600 practice questions.
| Certification Vendor: | Microsoft / GitHub |
|---|---|
| Exam Name: | Developing in Agentic AI Systems |
| Exam Number: | GH-600 |
| Exam Format: | Interactive items, Proctored exam, Multiple-choice questions, Scenario-based questions |
| Real Exam Qty: | Approximately 40–60 |
| Certificate Validity Period: | 1 year |
| Exam Duration: | 120 minutes |
| Related Certifications: | GitHub Certified: Agentic AI Developer |
| Exam Price: | $165 USD |
| Passing Score: | 700/1000 |
| Available Languages: | English |
| 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: Implement guardrails and accountability | 10–15% | - Implement guardrails and human-in-the-loop workflows
|
| Topic 2: Manage memory, state, and execution | 10–15% | - Persist agent state and manage context drift
|
| Topic 3: Perform evaluation, error analysis, and tuning | 15–20% | - Analyze agent failures and identify root causes
|
| Topic 4: Orchestrate multi-agent coordination | 15–20% | - Configure observability for multi-agent behavior by using logs, artifacts, and operational signals
|
| Topic 5: Prepare agent architecture and SDLC processes | 15–20% | - Configure observability and control for autonomous agents
|
| Topic 6: Implement tool use and environment interaction | 20–25% | - Configure MCP servers
|
Microsoft Developing in Agentic AI Systems is an official Microsoft / GitHub exam, registered under exam code GH-600. Passing it earns the GitHub Administrator certification at the Intermediate level. It also links to GitHub Certified: Agentic AI Developer. As an internationally recognized capacity standard, this credential speaks for your ability wherever your career takes you.
The Microsoft Developing in Agentic AI Systems syllabus comprises 6 official domains. The heaviest include Orchestrate multi-agent coordination (15–20%), Manage memory, state, and execution (10–15%), and Prepare agent architecture and SDLC processes (15–20%). The complete outline is above on this page; a short, efficient study plan starts with knowing exactly where the marks live.
The Microsoft Developing in Agentic AI Systems exam packs Approximately 40–60 questions into 120 minutes. Short-term preparation works best when every session is realistic, so run timed simulations in the BraindumpsPrep engine, keep your flag-and-return rhythm tight, and let repeated full-length runs build the pace the clock demands.
Microsoft Developing in Agentic AI Systems requires 700/1000 to pass, with an official registration fee of $165 USD. Retakes bill the full $165 USD again, so speed should never mean gambling. Compress your preparation with the BraindumpsPrep practice tests, and book once your scores clear the requirement reliably.
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.
Policies change, so verify the current requirements before registering on the official exam page.
Yes to both. BraindumpsPrep provides a free demo of the Microsoft Developing in Agentic AI Systems questions so you can verify the quality first, and after purchase the newest practice material is free for one year from the date of your order. When that year ends, extending the update service costs 50% of the regular price.
Your purchase carries a 100% money-back guarantee with defined conditions. Take the Microsoft Developing in Agentic AI Systems exam within 60 days of purchase; if you fail, you may claim a full refund, provided the exam matches your product. Attempts within 3 days of purchase are ineligible, as are downloaded-but-unused products, free materials, and expired orders; the candidate name must match the payer name. Submit a scanned enrollment slip and the official Score Report PDF within 2 days of the exam, and claims are processed within 7 days. Alternatively, exchange for two other exam products of equal value, free, with the update service on your original purchase retained.
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You have a GitHub repository that runs an agentic software development lifecycle workflow by using GitHub Actions. The workflow uses the following three executors implemented as scripts: spec_analyzer, risk_reviewer, and plan_merger.
You need to coordinate multiple specialized agents so that analysis and risk review run in parallel and then a final executor merges the outputs into a single plan. The orchestration pattern must fan out one request to multiple executors and then fan in the results to a final executor.
How should you complete the workflow definition? To answer, drag the appropriate values to the correct targets.
Correct Answer:

Explanation:
Target 1 # E. needs
Target 2 # A. [spec_analyzer, risk_reviewer]
Target 3 # B. concurrency
The relevant completed section is:
plan_merger:
runs-on: ubuntu-latest
needs: [spec_analyzer, risk_reviewer]
steps:
- name: Merge executor outputs into a single plan
run: ./executors/plan_merger.sh
- name: Publish merged plan
run: echo " publish plan artifact "
concurrency:
group: multiagent-${{ github.ref }}
The plan_merger job must use needs: [spec_analyzer, risk_reviewer] . GitHub Actions jobs run in parallel by default when no dependency exists. Therefore, spec_analyzer and risk_reviewer can execute concurrently, implementing the fan-out phase. The needs array then establishes the fan-in dependency: plan_merger does not start until both upstream jobs complete successfully. GitHub explicitly defines jobs. < job_id > .needs as the mechanism for declaring jobs that must complete before another job runs.
The final blank is concurrency , because the following line already defines group: multiagent-${{ github.ref
}}. GitHub ' s concurrency key groups workflow jobs or runs under a shared key so that overlapping executions for the same reference can be controlled. This is separate from the parallelism of the two specialized analyzer jobs.
This architecture directly implements a multi-agent orchestration pattern: independent specialized executors operate concurrently, their outputs converge at a deterministic coordination point, and execution boundaries are managed explicitly. The GH-600 study guide specifically requires candidates to apply orchestration patterns, configure parallel agent execution, and detect or prevent conflicts among concurrently operating agents.
Study Guide Reference Topics: Orchestrate Multi-Agent Coordination; fan-out/fan-in orchestration; parallel execution; dependency management; agent isolation and conflict control.
You have a GitHub repository that uses the GitHub Copilot CLI to run autonomous tasks.
You need to validate each generated command before it runs. Any commands that attempt to modify paths outside the repository must be blocked.
How should you complete the YAML? 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
Intended selection in the supplied question
event
pre-exec
cli.hooks.config
copilot/hooks.yaml
Verification correction: These are the intended selections within the supplied YAML. Current official Copilot documentation specifies JSON hook configuration and the preToolUse event; the supplied YAML is not a verified current Copilot CLI configuration.
Within the question's own schema, pre-exec is the logical event because validation must occur before a command can modify the filesystem. A post-exec event would run after the potentially prohibited action. The configuration path must reference the hook file identified in the exhibit, .copilot/hooks.yaml, rather than a workflow file under .github/workflows.
However, official Copilot documentation defines repository hook files under .github/hooks/*.json and uses the preToolUse lifecycle event. A blocking hook can return a structured permission decision of " deny " . The supplied YAML names and configuration structure therefore should not be copied into a current deployment as documented syntax.
The depicted command-string filter also does not establish complete filesystem confinement. Detecting obvious absolute paths and parent traversal cannot reliably resolve symbolic links, variable expansion, or the behavior of invoked programs. Enforcing the requirement that all external modifications be blocked requires a genuine execution boundary alongside command validation.
The question tests the correct preventive-control concept but presents an implementation that needs correction. Relevant curriculum topics are blocking prohibited actions, enforcing least privilege, and constraining execution contexts.
Reference: GitHub Copilot hooks reference .
You have a GitHub Copilot coding agent named CodeAgent. The .agent.md file of CodeAgent contains the following YAML frontmatter.
name: CodeAgent
description: Performs repository analysis and code review tasks.
tools: [ ' edit ' , ' execute ' , ' read ' , ' search ' ]
You need to issue a GitHub Copilot CLI command that preserves execution velocity for read-only tasks by eliminating approval prompts for low-risk tools. The solution must ensure that high-risk tools that can make changes remain available but still require explicit user approval before running.
Which command should you run?
Correct Answer: C 🗳️
Explanation: Only visible for BraindumpsPrep members. You can sign-up / login (it's free).
You need to make changes to repo1 to support the planned changes for the agents.
What should you modify?
Correct Answer: A 🗳️
Explanation: Only visible for BraindumpsPrep members. You can sign-up / login (it's free).
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?
Correct Answer: B 🗳️
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