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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Implement tool use and environment interaction	20-25%	- Select and configure agent tools 1. Identify required tools 2. Configure agent tool permissions 3. Configure agent tools - Operate agents with safe execution paths and robust error handling 1. Implement rollbacks 2. Implement error handling 3. Implement escalation paths 4. Implement retries 5. Implement traceability and accountability for agent actions - Configure MCP servers 1. Configure MCP allow lists 2. Configure a GitHub remote MCP server 3. Add an MCP server as a tool to an agent 4. Configure the MCP registries - Integrate agents within development environments 1. Evaluate the execution context for an agent 2. Configure an agent's scope to a specific repository 3. Configure an agent to be invoked in a CI workflow 4. Enable an agent to perform autonomous actions, including creating branches and pull requests 5. Configure an agent to handle environment-specific constraints 6. Configure an agent to use branch-based scope
Topic 2: Manage memory, state, and execution	10-15%	- Persist agent state and manage context drift 1. Capture task progress and decisions as durable artifacts 2. Resume agent work without repeating steps or diverging from prior decisions 3. Detect and correct drift during extended agent execution - Ensure continuity of agent memory and state across tools and environments 1. Prevent stale context 2. Share agent state 3. Prevent conflicting context - Implement agent memory strategies 1. Scope agent memory to task-relevant information 2. Choose between short-term, long-term, and external memory 3. Define memory expiration, pruning, and reset rules
Topic 3: Prepare agent architecture and SDLC processes	15-20%	- Configure observability and control for autonomous agents 1. Plan and implement the degree of agent autonomy, including guardrails 2. Configure human intervention for autonomous agents without slowing delivery 3. Configure agents to produce inspectable artifacts within standard development tooling - Define boundaries between planning, reasoning, and action 1. Validate agent plans 2. Configure an agent to output a structured plan 3. Configure agent planning to be distinct from agent execution 4. Prevent agent action until the agent checked and approved - 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
Topic 4: Perform evaluation, error analysis, and tuning	15-20%	- 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. Refine memory usage 2. Refine tool usage and tool access 3. Revise instructions, workflows, or constraints - Define success criteria and evaluation signals for agent tasks 1. Align evaluation criteria with development intent 2. Generate evaluation signals by using automated scanning tools 3. Specify expected outcomes and operational constraints for agent tasks 4. Identify qualitative and quantitative evaluation signals to evaluate agents
Topic 5: Orchestrate multi-agent coordination	15-20%	- 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 - Configure observability for multi-agent behavior by using logs, artifacts, and operational signals 1. Document key decisions, handoffs, and outcomes across agents 2. Perform post-hoc analysis of multi-agent behavior 3. Configure multi-agent workflows to produce artifacts suitable for review and audit - Detect and respond to multi-agent failures and degraded behavior 1. Identify failed, partial, or stalled agent executions 2. Respond to degraded behavior or coordination across agents 3. Implement multi-agent recovery patterns, including rollback and human-in-the-loop - Operate and manage multi-agent workflows 1. Detect and resolve agent conflicts, including overlapping code changes, duplicated effort, and contradictory outputs 2. Apply an orchestration pattern to coordinate multiple agents 3. Configure agent isolation for parallel execution
Topic 6: Implement guardrails and accountability	10-15%	- Define autonomy levels 1. Assign autonomy levels to maximize delivery speed while remaining compliant with organizational security and Responsible AI standards 2. Classify agent actions by operational, security, and compliance risk to right-size human interventions - Implement guardrails and human-in-the-loop workflows 1. Block actions that violate defined security, compliance, or Responsible AI policies 2. Identify the subset of actions that require human judgment 3. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes 4. Scope permissions and execution contexts to enforce least-privilege access 5. Preserve execution velocity by minimizing approvals that do not materially reduce risk

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Microsoft Developing in Agentic AI Systems Sample Questions (Q65-Q70):

NEW QUESTION # 65
You need to enable Copilot memory to support the planned changes.
What should you configure?

A. The personal Copilot settings of each developer
B. The Copilot settings of each repository
C. The Copilot settings of the organization
D. The agent profile of agent1

Answer: C

NEW QUESTION # 66
You need Copilot's coding agent to interact with an external ticketing system (e.g., Jira) so it can pull issue details as part of its workflow. What should you configure?

A. A .copilotignore rule
B. An MCP server
C. A CODEOWNERS entry
D. A branch protection ruleset

Answer: B

Explanation:
Model Context Protocol (MCP) servers extend an agent's capabilities by connecting it to external tools and data sources, such as ticketing systems, databases, or internal APIs, in a standardized way.

NEW QUESTION # 67
You have a private GitHub repository that has Copilot memory enabled.
Several developers who have write access to the repository make changes across multiple branches, including creating some pull requests that are later closed without merging.
Your team needs to understand how GitHub Copilot ensures that only task-relevant, up-to-date information influences code suggestions, even when older memories exist.
How does Copilot manage memories?

A. Copilot automatically blocks memory creation from pull requests that are closed without merging, to prevent outdated information from being stored.
B. Copilot stores memories per user, ensuring that only the developer who created a memory can trigger the memory in future sessions.
C. Copilot validates each memory's citations against the current branch before using the memory, and ignores the memory if the referenced code no longer exists.
D. Copilot stores memories indefinitely until a repository administrator deletes them manually.

Answer: C

Explanation:
GitHub Copilot Memory relies on a mechanism called just-in-time verification to ensure older or unmerged code changes do not pollute its code suggestions.
Reference:
https://docs.github.com/en/copilot/concepts/agents/copilot-memory

NEW QUESTION # 68
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.
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.

Answer:

Explanation:

Topic 1, Contoso Ltd,
Overview
Contoso Ltd. is a software development company located in the United States.
Existing Environment
GitHub Environment
Contoso uses GitHub Enterprise and assigns GitHub Copilot Pro+ licenses to its developers. The developers use Microsoft Visual Studio Code as their IDE.
Contoso has a customer portal. The code for the portal is stored in a GitHub repository named repo1 that contains the following:
A custom agent named agent1 that includes instructions to review specs related to best practices A custom instruction file named validate-instructions.md that is used to validate tone of voice and applies to all .md and .txt files A custom instruction file named codereview.instructions.md that is used by the Copilot coding agent but is excluded for use by the Copilot code review repo1 has the following structure:
The front-end is stored in the /frontend folder.
The API logic is stored in the /api folder.
Contoso has a second repository named repo2 that contains a legacy .NET application named App1 built by using .NET 6. repo2 has a multi-agent workflow for modernization tasks.
Contoso enables the Model Context Protocol (MCP) registry and allows the Microsoft Learn MCP Server. Every developer must configure their own connection to the Learn MCP Server.
Problem Statements
The developers working in repo1 report that the Microsoft Learn documentation is NOT being retrieved when they attempt to validate a design by using agent1.
The testing team at Contoso identifies that the customer portal uses inconsistent UI styles, which leads to customer confusion and branding issues. The UI inconsistencies stem from variations in the folder structure.
Agent Logs
You have the following logs for the multi-agent workflow used in repo2.
Requirements
Planned Changes
Contoso plans to have all agents and developers in repo1 use the Microsoft Learn MCP to ensure that reviews are validated by using the appropriate documentation. This must be implemented centrally.
Contoso plans to leverage AI-powered coding agents to implement new portal features and pages.
Technical Requirements
App1 must be upgraded to .NET 10. A previous upgrade attempt was started by using the Copilot modernization agent, but the attempt was never finalized.
You plan to retry the upgrade. You must first analyze App1 by using AI, and then generate a report that contains breaking changes and deprecated patterns before retrying the upgrade.
All AI-generated code for UI styling must adhere to a predefined folder structure.
The architects at Contoso need help building implementation plans for repo1. The company wants to implement a new agent named agent2 to analyze the code base and the code requirements, and then respond with a detailed plan. The agent must NOT be able to edit files or run local commands.
The developers must be able to delegate work to the Copilot coding agent by assigning issues to the agent.

NEW QUESTION # 69
You are evaluating how agent1 will behave after you implement the planned changes.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 70
......

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