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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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. Prevent agent action until the agent checked and approved 4. Configure an agent to output a structured plan - Configure observability and control for autonomous agents 1. Plan and implement the degree of agent autonomy, including guardrails 2. Configure agents to produce inspectable artifacts within standard development tooling 3. Configure human intervention for autonomous agents without slowing delivery - Integrate agents into the software development lifecycle (SDLC) 1. Identify and mitigate common anti-patterns in agents 2. Define inputs, outputs, and success criteria for agents 3. Identify steps for agents to perform
Topic 2: Orchestrate multi-agent coordination	15-20%	- Operate and manage multi-agent workflows 1. Apply an orchestration pattern to coordinate multiple agents 2. Configure agent isolation for parallel execution 3. Detect and resolve agent conflicts, including overlapping code changes, duplicated effort, and contradictory outputs - 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 - Manage the lifecycle of agents within multi-agent workflows 1. Add agents to existing multi-agent workflows 2. Update, reconfigure, or replace agents without disrupting active workflows 3. Retire agents while preserving auditability and workflow continuity - Detect and respond to multi-agent failures and degraded behavior 1. Implement multi-agent recovery patterns, including rollback and human-in-the-loop 2. Identify failed, partial, or stalled agent executions 3. Respond to degraded behavior or coordination across agents
Topic 3: Implement tool use and environment interaction	20-25%	- Configure MCP servers 1. Configure a GitHub remote MCP server 2. Configure the MCP registries 3. Add an MCP server as a tool to an agent 4. Configure MCP allow lists - Select and configure agent tools 1. Configure agent tools 2. Identify required tools 3. Configure agent tool permissions - Integrate agents within development environments 1. Configure an agent's scope to a specific repository 2. Configure an agent to handle environment-specific constraints 3. Configure an agent to use branch-based scope 4. Evaluate the execution context for an agent 5. Enable an agent to perform autonomous actions, including creating branches and pull requests 6. Configure an agent to be invoked in a CI workflow - Operate agents with safe execution paths and robust error handling 1. Implement retries 2. Implement escalation paths 3. Implement error handling 4. Implement rollbacks 5. Implement traceability and accountability for agent actions
Topic 4: 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. Scope agent memory to task-relevant information 2. Define memory expiration, pruning, and reset rules 3. Choose between short-term, long-term, and external memory - Ensure continuity of agent memory and state across tools and environments 1. Share agent state 2. Prevent stale context 3. Prevent conflicting context
Topic 5: Implement guardrails and accountability	10-15%	- 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 - Implement guardrails and human-in-the-loop workflows 1. Scope permissions and execution contexts to enforce least-privilege access 2. Identify the subset of actions that require human judgment 3. Block actions that violate defined security, compliance, or Responsible AI policies 4. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes 5. Preserve execution velocity by minimizing approvals that do not materially reduce risk
Topic 6: Perform evaluation, error analysis, and tuning	15-20%	- Define success criteria and evaluation signals for agent tasks 1. Specify expected outcomes and operational constraints for agent tasks 2. Align evaluation criteria with development intent 3. Generate evaluation signals by using automated scanning tools 4. Identify qualitative and quantitative evaluation signals to evaluate agents - Analyze agent failures and identify root causes 1. Classify root causes, including reasoning errors, tool misuse, and context or environment issues 2. Identify failures by using logs, plans, traces, outputs, and workflow artifacts - Tune agent behavior based on evaluation results 1. Revise instructions, workflows, or constraints 2. Refine memory usage 3. Refine tool usage and tool access

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Microsoft Developing in Agentic AI Systems Sample Questions (Q38-Q43):

NEW QUESTION # 38
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.

Answer:

Explanation:

NEW QUESTION # 39
You need to troubleshoot the issue reported by Dev1.
What should you review?

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

Answer: A

NEW QUESTION # 40
You have a GitHub repository that uses the GitHub Copilot coding agent to resolve issues and create draft pull requests.
You assign an issue to Copilot. Copilot creates a draft pull request. The pull request timeline shows Copilot started work, followed by status updates. The most recent status update is 55 minutes old.
You discover that the agent is no longer making progress.
You need to ensure that the work resumes without redoing the completed steps or changing the previously chosen approach.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 41
You need to make changes to repo1 to support the planned changes for the developers.
What should you modify?

A. .vscode/mcp.json
B. <project>/.mcp/server.json
C. .vscode/settings.json
D. .github/agents/*.agent.md

Answer: D

Explanation:
The selected location is appropriate when the planned developer changes concern repository-defined custom agents. Files under .github/agents/ define reusable agent roles that can be stored and reviewed alongside the application code. Their .agent.md format combines YAML configuration with Markdown instructions describing the agent's responsibilities and expected behavior.
This arrangement allows the team to maintain specialized planning, implementation, and review roles consistently. Changes to an agent's instructions or configuration can pass through the repository's normal pull request process, providing a record of how the development workflow evolves.
The alternative locations serve different configuration purposes. .vscode/mcp.json configures MCP server connections for the workspace. .vscode/settings.json contains editor settings. Neither is the primary location for defining a repository's custom agent profile.
The question's planned changes are not reproduced in its supplied statement. Therefore, B is the source-selected answer under the custom-agent interpretation; a requirement specifically about MCP connectivity would require different configuration.
Study-guide topics: custom agent architecture, repository-scoped configuration, and version-controlled development workflows. Reference: VS Code-Custom agent file locations and structure.
Topic 2, GitHub Environment
Existing Environment
GitHub Environment
The GitHub environment contains the following:
Three repositories named product-api, billing-service, and infra-terraform.
Branch protection on the main branch in all repositories that requires at least one pull request review before merging GitHub Actions runners used across all workflows A GitHub team named SG_Dev that contains developers A GitHub team named SG_Review that contains senior engineers and a security team A .github/copilot-instructions.md file that includes general coding conventions for all features Agent environment The product-api repository uses a GitHub Copilot coding agent named agent1 that has the following configurations:
No custom agent profile is defined.
A Model Context Protocol (MCP) server named MCP1 is deployed to https://mcp.litwareinc.internal and provides access to internal ticketing and deployment APIs. MCP1 requires an API key for authentication.
A second Copilot coding agent named agent2 handles changes in infra-terraform and runs in parallel with agent1 when both agents have open assigned issues.
Copilot memory is NOT enabled for the organization.
Problem Statements
Litware identifies the following issues:
During two recent sessions, agent1 accessed files in billing-service, which is outside the agent's intended scope. agent1 makes code changes immediately after receiving a task.
A developer named Ben, who is on the SG_Dev team, reports that agent1 completed a session with a successful status and opened a pull request, but the pull request contains no file changes. Other developers report this intermittently as well.
Both agent1 and agent2 modified shared/config.yaml in a parallel test run, generating conflicting outputs. agent1 consistently uses raw try-catch blocks for error handling, which violates the defined implementation guidelines of SG_Dev.
Requirements
Planned Changes
Litware plans to make the following changes:
Ensure that agent1 can access all the tools in the environment.
Provide product-api with specific instructions to agent1 without affecting Copilot Chat or Copilot code review.
Configure MCP1 as a tool for agent1 by modifying the product-api repository MCP configuration.
Ensure that Copilot retains details that it has learned and uses that knowledge for future work. This must be applied to all licensed members of the organization.
Implementation guidelines
The development team at Litware identifies the following implementation guidelines:
Agent workflows must be able to run in parallel.
Application error handling must use the repository ErrorHandler class. agent1 and agent2 must run on isolated branches during parallel execution. File-level conflicts must be detected before merges, and both agents must be able to run concurrently.
Security Requirements
Litware identifies the following security requirements:
Only the members of SG_Review must be able to approve agent1 plan outputs.
All API keys must be stored and accessed securely.
The developers must NOT be able to self-approve.
Agent configuration

NEW QUESTION # 42
You have a GitHub Copilot Enterprise subscription.
Developers use Microsoft Visual Studio Code.
You need to configure an array of subagents that can be invoked by an agent.
What should you configure in the YAML frontmatter for the agent?

A. mcp-servers
B. target
C. handoffs
D. agents
E. tools

Answer: D

Explanation:
The agents frontmatter property defines the custom agents that the current agent can invoke as subagents. It establishes the delegation boundary for an orchestrator or other coordinating agent.
The tools property grants operational capabilities such as file access, terminal commands, MCP tools, or the agent invocation tool itself. It does not identify the specific custom-agent profiles available for delegation. The mcp-servers property configures external MCP servers; it does not define subagent membership.
Handoffs are user-facing transitions from one custom agent to another. They are useful for moving a conversation from planning to implementation but are not the configuration mechanism for an agent's available subagent array. The target field identifies an execution target or context and is unrelated to delegation membership.
For a reliable orchestration design, include only the required specialized agents in the agents list and ensure the parent agent has the appropriate agent-invocation capability.
Study-guide topics: subagent lists, delegation boundaries, orchestrator design, and multi-agent coordination.

NEW QUESTION # 43
......

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