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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Manage memory, state, and execution	10–15%	- 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 - 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 - Ensure continuity of agent memory and state across tools and environments 1. Prevent conflicting context 2. Share agent state 3. Prevent stale context
Topic 2: Prepare agent architecture and SDLC processes	15–20%	- Define boundaries between planning, reasoning, and action 1. Validate agent plans 2. Configure agent planning to be distinct from agent execution 3. Configure an agent to output a structured plan 4. Prevent agent action until the agent checks and approves - Integrate agents into the software development lifecycle (SDLC) 1. Identify and mitigate common anti-patterns in agents 2. Identify steps for agents to perform 3. Define inputs, outputs, and success criteria for agents - Configure observability and control for autonomous agents 1. Configure human intervention for autonomous agents without slowing delivery 2. Configure agents to produce inspectable artifacts within standard development tooling 3. Plan and implement the degree of agent autonomy, including guardrails
Topic 3: Perform evaluation, error analysis, and tuning	15–20%	- 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 - Tune agent behavior based on evaluation results 1. Revise instructions, workflows, or constraints 2. Refine memory usage 3. Refine tool usage and tool access - Define success criteria and evaluation signals for agent tasks 1. Align evaluation criteria with development intent 2. Identify qualitative and quantitative evaluation signals to evaluate agents 3. Specify expected outcomes and operational constraints for agent tasks 4. Generate evaluation signals by using automated scanning tools
Topic 4: Orchestrate multi-agent coordination	15–20%	- Detect and respond to multi-agent failures and degraded behavior 1. Implement multi-agent recovery patterns, including rollback and human-in-the-loop 2. Respond to degraded behavior or coordination across agents 3. Identify failed, partial, or stalled agent executions - Manage the lifecycle of agents within multi-agent workflows 1. Retire agents while preserving auditability and workflow continuity 2. Update, reconfigure, or replace agents without disrupting active workflows 3. Add agents to existing multi-agent workflows - 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 - 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
Topic 5: Implement tool use and environment interaction	20–25%	- Configure MCP servers 1. Configure MCP registries 2. Add an MCP server as a tool to an agent 3. Configure a GitHub remote MCP server 4. Configure MCP allow lists - Integrate agents within development environments 1. Evaluate the execution context for an agent 2. Enable an agent to perform autonomous actions, including creating branches and pull requests 3. Configure an agent to use branch-based scope 4. Configure an agent to handle environment-specific constraints 5. Configure an agent's scope to a specific repository 6. Configure an agent to be invoked in a CI workflow - Operate agents with safe execution paths and robust error handling 1. Implement escalation paths 2. Implement traceability and accountability for agent actions 3. Implement error handling 4. Implement rollbacks 5. Implement retries - Select and configure agent tools 1. Configure agent tool permissions 2. Configure agent tools 3. Identify required tools
Topic 6: Implement guardrails and accountability	10–15%	- Implement guardrails and human-in-the-loop workflows 1. Preserve execution velocity by minimizing approvals that do not materially reduce risk 2. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes 3. Scope permissions and execution contexts to enforce least-privilege access 4. Identify the subset of actions that require human judgment 5. Block actions that violate defined security, compliance, or Responsible AI policies - Define autonomy levels 1. Classify agent actions by operational, security, and compliance risk to right-size human interventions 2. Assign autonomy levels to maximize delivery speed while remaining compliant with organizational security and Responsible AI standards

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The Developing in Agentic AI Systems (GH-600) certification is a valuable credential that every Microsoft professional should earn it. The Microsoft GH-600 certification exam offers a great opportunity for beginners and experienced professionals to demonstrate their expertise. With the Developing in Agentic AI Systems (GH-600) certification exam everyone can upgrade their skills and knowledge. There are other several benefits that the GH-600 Exam holders can achieve after the success of the Developing in Agentic AI Systems (GH-600) certification exam. However, you should keep in mind to pass the Microsoft GH-600 certification exam is not an easy task. It is a challenging job.

Microsoft Developing in Agentic AI Systems Sample Questions (Q58-Q63):

NEW QUESTION # 58
You have a GitHub repository.
Developers use the GitHub Copilot CLI and repository-scoped hooks under .github/hooks/*.json.
You need to allow the Copilot CLI to automatically run low-risk Bash commands. The solution must prevent the autonomous execution of high-risk commands, such as sudo, rm -rf, and curl ... | bash.
What should you do?

A. Configure a userPromptSubmitted hook that logs prompts and exits nonzero.
B. Add .github/hooks/agent/ to .gitignore.
C. Configure a sessionStart hook that prints a policy banner.
D. Configure a preToolUse hook that returns permissionDecision: "deny".

Answer: D

Explanation:
A preToolUse hook evaluates the proposed command before the shell tool executes it. The hook can inspect command arguments and return permissionDecision: "deny" when a pattern indicates a high-risk operation, while allowing low-risk commands to continue automatically.
This is the correct enforcement point because the requirement is preventive. A policy banner only informs the user or agent; it cannot block execution. Logging submitted prompts provides audit information but does not evaluate the actual shell command. Ignoring the hooks directory simply removes the repository-scoped enforcement mechanism.
The hook should use precise matching rules. For example, it can deny sudo, destructive recursive deletion, and piping untrusted remote content into a shell, while allowing commands such as git status, package metadata inspection, and test execution. Rules should avoid broad string matching that blocks harmless commands merely because they contain similar text.
Study-guide topics: pre-execution guardrails, shell-command approval, policy enforcement, and autonomous tool safety. Reference: GitHub Copilot-Hooks reference.

NEW QUESTION # 59
You have a GitHub repository that stores custom GitHub Copilot agents in the .github/agents/ folder.
You have an agent named lead-dev.agent.md that must invoke a specialist subagent named security-reviewer.
Evaluation results show that lead-dev attempts to invoke security-reviewer, but the subagent call fails to run.
You need to ensure that lead-dev can invoke security-reviewer.
How should you complete the YAML frontmatter of the agent profile? To answer, drag the appropriate values to the correct targets.

Answer:

Explanation:

Explanation:
Target
Value
tools
[ ' execute ' ]
agents
[ ' security-reviewer ' ]
The agents property identifies the custom agent profile that lead-dev is permitted to invoke. Adding security- reviewer establishes the delegated specialist relationship required by the workflow.
The source configuration uses the execute tool for the lead agent's subagent invocation capability. Without the appropriate invocation capability, adding the target agent name alone does not permit the orchestration step to run.
The user-invocable: false value can remain. It prevents direct selection of lead-dev in the user interface while still allowing it to operate as part of an orchestrated workflow. This is useful for internal coordinator roles that should be reached only through a parent workflow.
Read, search, or edit tools do not identify the target specialist. They may be needed for other lead-dev tasks, but they do not solve the failed subagent invocation.
Study-guide topics: subagent invocation, custom agent frontmatter, delegation permissions, and orchestrated security review.

NEW QUESTION # 60
You have a custom agent profile file named test-agent.agent.md that contains the following YAML frontmatter:
---
name: test-agent
description: Custom agent description
tools: ['tool-a', 'tool-b']
---
In the same repository, you have an MCP configuration file named mcp.json.
You need to ensure that the GitHub Model Context Protocol (MCP) server is available to test the agent. The solution must allow only the Copilot toolset.
What should you do?

A. Replace line 04 with tools: ['tool-a', 'tool-b', 'github/*'].
B. Replace line 04 with tools: ['tool-a', 'tool-b', 'github/copilot'].
C. Replace "servers": with "mcp-servers": in mcp.json.
D. Replace line 04 with tools: ['copilot/*'].

Answer: D

Explanation:
The copilot/* toolset grants the agent access to the Copilot-provided tools while avoiding unrelated tool groups. This directly meets the requirement to allow only the Copilot toolset for the test agent.
Keeping tool-a and tool-b would expand access beyond the stated requirement. Using github/* would expose the broader GitHub tool group rather than limiting the profile to the Copilot toolset. The MCP configuration retains its server structure; changing the server property name does not configure which tools the agent may use.
Toolsets are an important control surface for specialized agents. They let an architect define a narrow capability set for testing, planning, review, or implementation roles. The selected toolset should still be backed by an available and authorized MCP configuration; naming a toolset does not bypass server authentication or policy restrictions.
Study-guide topics: MCP toolsets, custom agent capabilities, and least-privilege tool access.

NEW QUESTION # 61
You need to resolve the issue of the agents generating conflicting output. The solution must meet the implementation guidelines.
What should you do?

A. Configure each agent to work on a separate branch and add a required status check that detects file-level overlap before either pull request can be merged.
B. Configure tools: ['read', 'search'] in both agent profiles to prevent either agent from writing files.
C. Add shared/config.yaml to a CODEOWNERS file that requires SG_Review approval before any changes can be merged.
D. Configure a concurrency group on both agent workflows so that only one workflow runs at a time.

Answer: A

Explanation:
Separate branches isolate each agent's proposed changes and allow the team to inspect them independently. Adding a required status check for file-level overlap detects conflicting modifications before either pull request is merged, preventing one agent's work from silently overwriting another's changes.
A read-only tool configuration prevents both agents from making required changes, so it avoids conflict by eliminating implementation capability rather than controlling it. CODEOWNERS approval adds review accountability for a specific file but does not automatically detect overlap between independent agent outputs. A single concurrency group serializes entire workflows and reduces throughput even where the agents work on unrelated files.
The selected solution maintains parallel development while creating a merge-time control for the actual risk: overlapping file changes. It also produces reviewable evidence of the overlap check in pull request status results.
Branch isolation does not eliminate semantic conflicts, such as incompatible API assumptions. Teams should therefore combine overlap detection with normal pull request review and integration testing.
Study-guide topics: parallel agent coordination, branch isolation, merge controls, and conflict detection.

NEW QUESTION # 62
You need to troubleshoot the issue reported by Dev1.
What should you review?

A. The GitHub Actions usage metrics of repo1.
B. The GITHUB_TOKEN permissions block in the agent1 workflow.
C. The agent session log in the Agents panel.
D. The GitHub Actions runner log for the session job.

Answer: C

NEW QUESTION # 63
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