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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Developing in Agentic AI Systems |
| Exam Number: | GH-600 |
| Real Exam Qty: | 40–60 |
| Exam Format: | Performance-based/lab tasks, Multiple select, Scenario-based, Multiple choice |
| Exam Duration: | 120–150 |
| Available Languages: | English |
| Related Certifications: | Microsoft Certified: Azure Developer Associate Microsoft Certified: Azure AI Engineer Associate |
| Certificate Validity Period: | 2 years |
| Exam Price: | USD 100 |
| Passing Score: | 700 |
| Recommended Training: | Semantic Kernel Documentation Azure AI Foundry Documentation GH-600 Learning Paths on Microsoft Learn |
| Exam Registration: | Microsoft GH-600 Exam Registration Pearson VUE Registration |
| Sample Questions: | ![]() |
| Exam Way: | Online proctored or Pearson VUE test center |
| Pre Condition: | Recommended: AZ-204 (Azure Developer Associate) or AI-102 (Azure AI Engineer Associate); proficiency in Python or C# and Azure development experience |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/certifications/exams/gh-600 |
| Section | Weight | Objectives |
|---|---|---|
| Test, deploy, and monitor agentic AI systems | 20% | - Deploy and monitor agents at scale
|
| Design agentic AI solutions | 25% | - Define requirements for agentic systems
|
| Integrate tools, data, and services | 25% | - Incorporate external tools and APIs
|
| Implement agents and multi-agent systems | 30% | - Build agents with Azure AI tools and frameworks
|
Three versions of the same study materials: a printable, expert-prepared PDF with free demo download and instant access; a Desktop Test Engine for Windows with customizable simulation and timed modes that works offline; and an Online Test Engine for any browser on Windows, Mac, Android, and iOS with test history and performance review. All 110 practice questions are identical across versions. Every purchase includes 365 days of free updates, a 50% renewal discount afterward, and unlimited computer installations.
The Microsoft Developing in Agentic AI Systems exam contains 40–60 to complete within 120–150. Timed simulation in the BraindumpsPrep Desktop Test Engine teaches that rhythm before exam day does.
The official Microsoft Developing in Agentic AI Systems outline defines 4 domains. The leading three are Implement agents and multi-agent systems (30%), Design agentic AI solutions (25%), and Integrate tools, data, and services (25%). The full list is in the topics section above, and BraindumpsPrep's 110 practice questions cover each one.
Recommended: AZ-204 (Azure Developer Associate) or AI-102 (Azure AI Engineer Associate); proficiency in Python or C# and Azure development experience Since requirements change over time, confirm the latest on the official Microsoft exam page before scheduling.
The Microsoft Developing in Agentic AI Systems exam requires 700 to pass, and registration costs USD 100. A failed attempt means paying the same fee again — a readiness check with the BraindumpsPrep engines before booking is the cheaper option.
The GH-600 exam is a Microsoft certification exam validating the Microsoft Developing in Agentic AI Systems syllabus shown above. It belongs to these credential paths: Microsoft Certified: Azure AI Engineer Associate, Microsoft Certified: Azure Developer Associate. Candidates in 2026 start with BraindumpsPrep's 110 practice questions, available as a printable PDF, a Windows Desktop Test Engine, and an Online Test Engine — three versions of the same materials.
Microsoft recommends these training resources for the Microsoft Developing in Agentic AI Systems exam:
Follow the training with steady self-testing — the 110 practice questions from BraindumpsPrep help you apply theory and fill any gaps the training leaves.
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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?
Correct Answer: D 🗳️
Explanation: Only visible for BraindumpsPrep members. You can sign-up / login (it's free).
While using agent mode in VS Code, you want Copilot to run a specific test suite as a validation step after making changes, without manually invoking the terminal each time. What feature enables this?
Correct Answer: A 🗳️
Explanation: Only visible for BraindumpsPrep members. You can sign-up / login (it's free).
You have a GitHub repository that uses GitHub Copilot Chat in Microsoft Visual Studio Code. Custom agents are stored in the repository under version control.
Your team uses a multi-agent workflow where a planner agent produces an implementation plan that is then handed off to an implementation agent to make changes.
Recent prompts cause the planner agent to start editing files and running commands before the plan is approved.
You need to configure the planner agent to meet the following requirements:
* Use only read-only tools.
* Hand off to the implementation agent only after the plan is approved.
How should you configure the agent? To answer, select the appropriate options in the answer area.
Correct Answer:

Explanation:
tools: [ ' search ' , ' read ' , ' fetch ' ]
send: false
The completed configuration is:
name: planner-agent
description: Planning
tools: [ ' search ' , ' read ' , ' fetch ' ]
handoffs:
- label: Start Implementation
agent: implementer
prompt: Now implement the plan outlined above.
send: false
The planner must be restricted to read-only capabilities, so [ ' search ' , ' read ' , ' fetch ' ] is the correct tool set. search locates repository information, read inspects file contents, and fetch retrieves supporting information without modifying source files. The other available tool combinations contain write-capable operations such as edit, commit, push, merge, or publish, which would violate the requirement that the planner must not alter the repository before approval. VS Code custom agents explicitly support scoping tools so that planning agents can be constrained to research and analysis capabilities.
The send property must be false. A handoff appears as an interactive transition after the planner produces its response. With send: false, selecting the handoff switches to the implementation agent and pre-fills the implementation prompt, but the prompt is not automatically submitted. This provides the necessary human approval gate: the plan can be reviewed before implementation begins. By contrast, send: true immediately submits the handoff prompt after the transition.
Study Guide Reference Topics: agent role separation, least-privilege tool assignment, planning versus execution, multi-agent handoffs, and human approval gates.
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.
HOTSPOT
name: implementation-planner
description: Creates detailed implementation plans and technical specifications in markdown format tools: [
< Dropdown 1 > ,
< Dropdown 2 > ,
' microsoftdocs/mcp/docs_search ' ,
' microsoftdocs/mcp/docs_fetch '
]
The accompanying image includes empty dropdown controls and recreated practice alternatives.
Correct 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 .
You are architecting an agentic AI system and need the agent ' s tool-calling behavior to be constrained so it can only call a specific allow-listed set of MCP tools, never arbitrary ones. What should you configure?
Correct Answer: B 🗳️
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