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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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. Generate evaluation signals by using automated scanning tools 2. Align evaluation criteria with development intent 3. Specify expected outcomes and operational constraints for agent tasks 4. Identify qualitative and quantitative evaluation signals to evaluate agents
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. Perform post-hoc analysis of multi-agent behavior 3. Document key decisions, handoffs, and outcomes across agents - 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 - 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
Topic 3: Prepare agent architecture and SDLC processes	15-20%	- Define boundaries between planning, reasoning, and action 1. Configure an agent to output a structured plan 2. Prevent agent action until the agent checked and approved 3. Validate agent plans 4. Configure agent planning to be distinct from agent execution - 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. 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
Topic 4: Implement tool use and environment interaction	20-25%	- Select and configure agent tools 1. Configure agent tools 2. Configure agent tool permissions 3. Identify required tools - Configure MCP servers 1. Configure MCP allow lists 2. Add an MCP server as a tool to an agent 3. Configure a GitHub remote MCP server 4. Configure the MCP registries - Integrate agents within development environments 1. Enable an agent to perform autonomous actions, including creating branches and pull requests 2. Configure an agent to be invoked in a CI workflow 3. Evaluate the execution context for an agent 4. Configure an agent to use branch-based scope 5. Configure an agent to handle environment-specific constraints 6. Configure an agent's scope to a specific repository - Operate agents with safe execution paths and robust error handling 1. Implement error handling 2. Implement traceability and accountability for agent actions 3. Implement escalation paths 4. Implement retries 5. Implement rollbacks
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. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes 2. Block actions that violate defined security, compliance, or Responsible AI policies 3. Scope permissions and execution contexts to enforce least-privilege access 4. Identify the subset of actions that require human judgment 5. Preserve execution velocity by minimizing approvals that do not materially reduce risk
Topic 6: Manage memory, state, and execution	10-15%	- Ensure continuity of agent memory and state across tools and environments 1. Prevent stale context 2. Prevent conflicting context 3. Share agent state - 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 - 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

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Microsoft Developing in Agentic AI Systems Sample Questions (Q72-Q77):

NEW QUESTION # 72
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 # 73
You have a GitHub Copilot coding agent that has completed a pull request for a security fix in your repository.
Before merging, you need to evaluate the quality of the agent's work by using both automated evaluation signals and human review.
You review the session log and the pull request.
What are two automated evaluation signals generated by the coding agent's built-in scanning tools? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. CodeQL findings that identify security vulnerabilities in the generated code
B. linting errors from the repository's CI pipeline configured in GitHub Actions
C. the median time to merge metric reported on the Copilot usage metrics dashboard
D. the detection of hardcoded secrets, such as API keys and tokens
E. comments from Copilot code review that suggest improvements to code patterns

Answer: A,D

Explanation:
The two automated evaluation signals generated by the coding agent's built-in scanning tools are:
CodeQL findings that identify security vulnerabilities in the generated code.
The detection of hardcoded secrets, such as API keys and tokens.
Reference:
https://itacademy.com.ua/en/articles/2026-06-11/security-validation-third-party-coding-agents-github/

NEW QUESTION # 74
You have a GitHub repository that uses GitHub Copilot code review on pull requests.
You plan to add repository-wide code review guidance that will apply to all files.
You need Copilot code review to consistently apply the guidance during pull request reviews.
What should you do?

A. Modify the .github/pull_request_template.md file.
B. Create a .github/copilot-instructions.md file.
C. Update the instructions in the .github/agents/*.agent.md files.
D. Create a .github/instructions/copilot.instructions.md file.

Answer: B

Explanation:
The repository-wide Copilot instruction file is .github/copilot-instructions.md. GitHub explicitly identifies this file as the location for review guidance that should apply throughout the codebase. It can describe coding standards, security expectations, error-handling requirements, and other review criteria that should be considered across pull requests.
A pull request template primarily structures the description supplied when a pull request is created. It is not the designated repository-wide Copilot instruction mechanism. Files beneath .github/instructions support instructions with defined applicability, commonly using path patterns. The filename in option C alone does not establish repository-wide scope. Custom agent profiles define the behavior of particular agents and do not replace the standard configuration for Copilot code review.
The instructions should state concrete, reviewable requirements rather than vague pReference. For example, a rule about checking authorization at a defined service boundary is more actionable than a general instruction to "ensure security." Repository settings must also allow custom instructions to be used for code review.
This configuration improves consistency while human reviewers remain responsible for evaluating the resulting findings.
Relevant curriculum topics are tuning instructions, defining evaluation criteria, and aligning automated review with development intent.
Reference:

NEW QUESTION # 75
You have a GitHub Enterprise Cloud repository that uses the GitHub Copilot coding agent.
Engineers assign issues to Copilot, and Copilot creates draft pull requests. The engineers start tasks either by assigning issues on github.com or by using GitHub Copilot Chat in an IDE. Reviewers request updates by leaving pull request comments.
You discover that sometimes, Copilot uses outdated requirements after a reviewer posts an updated instruction in a pull request comment, and, in several cases, Copilot fails to resume work from the comment.
You need to ensure that iteration requests are applied to the correct pull request session and are processed consistently.
What should you do?

A. Ensure that the reviewer has write access to the repository. Instruct the reviewer to mention @copilot in the pull request comments.
B. Ensure that the agent has write access to the repository. Instruct the reviewer to mention @github in the task comments.
C. Start a new task from Copilot Chat in the IDE. Instruct the reviewer to mention @github in the task comments.
D. Ensure that the agent has write access to the repository. Instruct the reviewer to unassign and reassign the original issue to Copilot.

Answer: A

Explanation:
The reviewer must have write access and explicitly mention @copilot in the relevant pull request comment. These requirements establish both authorization and routing. GitHub documents that Copilot responds to comments from users with write access, allowing an authorized reviewer to request further changes on the pull request being evaluated.
A normal comment can communicate information to other reviewers without becoming an instruction to the agent. The explicit mention distinguishes an actionable iteration request from surrounding discussion. The comment should clearly state the updated requirement and identify any earlier direction that it replaces.
Option A confuses the IDE's GitHub participant with the pull-request iteration mechanism. Option C restarts work through the issue assignment path rather than directly delivering the update to the existing pull request. Option D creates a separate task, introducing unnecessary context separation and potentially duplicating work.
After a valid request is accepted, the reviewer can check for the acknowledgment reaction and a new work event in the pull request timeline. These provide evidence that the instruction reached the agent.
Relevant curriculum topics are execution continuity, correct context routing, and preventing stale requirements during iterative work.
Reference:

NEW QUESTION # 76
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.
While upgrading App1, the agent identifies 47 issues, including a security vulnerability, and 46 API incompatibilities across different projects.
Which two actions are unsafe to delegate to the agent and require human involvement? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. Validate whether the tasks.md file exists.
B. Generate the assessment.md file.
C. Validate the assessment.md file for accuracy.
D. Approve all Git commits.
E. Review the plan.md file for dependencies.

Answer: C,D

Explanation:
The two actions that are unsafe to delegate to the agent and require human involvement are Approve all Git commits and Validate the assessment.md file for accuracy.
Validate the assessment.md file for accuracy: The agent generated this file based on its own scan. A human expert must cross-check its findings to catch false positives, false negatives, and misclassified security vulnerabilities.
Approve all Git commits: Automated agents can introduce unintended code changes, logic flaws, or broken builds. A human must review and approve commits to maintain code quality and prevent security regressions.
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 # 77
......

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