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

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Microsoft Developing in Agentic AI Systems Sample Questions (Q75-Q80):

NEW QUESTION # 75
Case Study 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 repo1that 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 repo1use 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.
Before App1 is upgraded, you need to verify each individual upgrade step and whether all tests have passed.
Which file should you use?

A. plan.md
B. assessment.md
C. agent.md
D. tasks.md

Answer: B

Explanation:
To analyze a legacy application using GitHub Copilot modernization agents before executing a .NET 10 upgrade, you must use the assessment.md file Scenario:
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.
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.
Reference:
https://learn.microsoft.com/en-us/dotnet/core/porting/github-copilot-app-modernization/overview

NEW QUESTION # 76
A team assigns an issue to the GitHub Copilot coding agent by using the following one-line description: Fix the login bug.
Copilot creates a pull request, but the pull request is missing changes and has an incorrect scope.
How should you resolve the issue?

A. Allocate more resources to copilot-setup-steps.yml.
B. Update the Model Context Protocol (MCP) server rate limits.
C. Enable Copilot memory.
D. Add a clear description of the problem to the issue.

Answer: D

Explanation:
The primary failure is insufficient task definition. "Fix the login bug" does not identify the observed behavior, expected behavior, reproduction conditions, affected component, or completion criteria. The agent must infer these details, creating a substantial risk of incomplete changes or work outside the intended scope. A clear issue description supplies the information needed to construct and implement an appropriate plan.
For this scenario, the description should identify how the login failure occurs, which authentication path is affected, what successful behavior looks like, and which existing behavior must remain intact. Relevant error messages, reproduction steps, and expected tests make the task independently verifiable. These details turn an ambiguous request into a bounded engineering assignment.
Enabling memory does not supply missing requirements reliably. MCP rate limits govern service interaction rather than task clarity. Additional setup resources address environmental capacity or dependency preparation, not uncertainty about what must change.
The corrective action therefore belongs at task intake, where developers define the agent's inputs and success conditions. The relevant curriculum topics are defining agent inputs, outputs, and success criteria and mitigating poorly scoped task assignments.
Reference:

NEW QUESTION # 77
You have a GitHub repository that uses GitHub Actions for CI on pull requests. The repository contains a Node.js app.
You have a GitHub Copilot coding agent that opens pull requests for backlog items, and your company requires automated checks for agent-generated changes.
You plan to standardize success criteria so that pull requests created by agents only succeed when unit tests pass and CodeQL analysis completes.
You need to configure a GitHub Actions workflow that runs on pull requests, executes unit tests, and performs CodeQL analysis.
How should you complete the workflow? To answer, drag the appropriate values to the correct targets.

Answer:

Explanation:

NEW QUESTION # 78
You need to provide access to the API key of MCP1. The solution must meet the security requirements.
What should you do?

A. In product-api, add the API key as a GitHub Actions encrypted secret and reference the secret by using
${{ secrets.KEY }} in the workflow YAML of agent1.
B. In the product-api repository settings, add the API key directly to the .mcp/server.json file by using a plaintext apiKey field.
C. Store the API key as a GitHub Codespaces user secret scoped to product-api.
D. Store the API key as a secret in the Copilot environment of product-api by using a name prefix of COPILOT_MCP_, and then reference the variable name in the mcp.json configuration.

Answer: D

Explanation:
The API key should be stored as a secret in the Copilot environment and referenced by name from the MCP configuration. This keeps the secret value out of repository files, agent prompts, pull requests, and ordinary workflow output while allowing the MCP server to receive the required credential at runtime.
A plaintext key in a configuration file is unacceptable because repository history, forks, logs, and code review systems can expose it. A Codespaces user secret is scoped to a user development environment and does not provide the intended runtime secret for the Copilot agent environment. A GitHub Actions secret is useful for workflow expressions, but the scenario requires the key to be available to the MCP server configuration itself.
The prefixed environment-secret approach creates a clear boundary: the configuration references an identifier, while the secret value is resolved only in the authorized execution environment. This supports rotation without changing tracked configuration content.
Study-guide topics: MCP authentication, secrets management, secure environment configuration, and credential isolation.

NEW QUESTION # 79
You need to make changes to repo1 to support the planned changes for the agents.
What should you modify?

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

Answer: D

Explanation:
Repository custom agent profiles belong in .github/agents/ and use Markdown files with agent configuration and instructions. When the intended changes concern the agents' roles, available tools, or operating guidance, these profiles are the appropriate implementation point.
An agent profile provides a durable definition that can be shared with other repository contributors. Keeping that definition under version control makes changes reviewable and enables the team to associate a particular agent configuration with the code revision used during evaluation. This is useful when diagnosing why an agent's behavior changed after its instructions or capabilities were modified.
Editor settings and MCP connection settings address different concerns. They can affect the environment in which an agent operates, but they do not replace the agent's own profile. Changing a server connection is appropriate for transport or authentication requirements; changing an agent profile is appropriate for agent behavior and capability selection.
The source selects C. Its applicability depends on the omitted planned changes actually concerning custom agent configuration.
Study-guide topics: agent profiles, configuration management, and SDLC traceability. Reference: GitHub-Creating custom agents.

NEW QUESTION # 80
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