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Microsoft GH-600 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Implement tool use and environment interaction20-25%- Integrate agents within development environments
  • 1. Configure an agent to handle environment-specific constraints
    • 2. Configure an agent to use branch-based scope
      • 3. Evaluate the execution context for an agent
        • 4. Configure an agent to be invoked in a CI workflow
          • 5. Enable an agent to perform autonomous actions, including creating branches and pull requests
            • 6. Configure an agent's scope to a specific repository
              - Configure MCP servers
              • 1. Add an MCP server as a tool to an agent
                • 2. Configure the MCP registries
                  • 3. Configure MCP allow lists
                    • 4. Configure a GitHub remote MCP server
                      - Select and configure agent tools
                      • 1. Configure agent tool permissions
                        • 2. Configure agent tools
                          • 3. Identify required tools
                            - Operate agents with safe execution paths and robust error handling
                            • 1. Implement traceability and accountability for agent actions
                              • 2. Implement retries
                                • 3. Implement error handling
                                  • 4. Implement escalation paths
                                    • 5. Implement rollbacks
                                      Topic 2: Perform evaluation, error analysis, and tuning15-20%- Define success criteria and evaluation signals for agent tasks
                                      • 1. Generate evaluation signals by using automated scanning tools
                                        • 2. Specify expected outcomes and operational constraints for agent tasks
                                          • 3. Identify qualitative and quantitative evaluation signals to evaluate agents
                                            • 4. Align evaluation criteria with development intent
                                              - 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. Refine tool usage and tool access
                                                    • 2. Refine memory usage
                                                      • 3. Revise instructions, workflows, or constraints
                                                        Topic 3: Orchestrate multi-agent coordination15-20%- 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
                                                              - Manage the lifecycle of agents within multi-agent workflows
                                                              • 1. Update, reconfigure, or replace agents without disrupting active workflows
                                                                • 2. Retire agents while preserving auditability and workflow continuity
                                                                  • 3. Add agents to existing multi-agent workflows
                                                                    - 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. Implement multi-agent recovery patterns, including rollback and human-in-the-loop
                                                                              • 3. Respond to degraded behavior or coordination across agents
                                                                                Topic 4: Implement guardrails and accountability10-15%- 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. 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 5: Prepare agent architecture and SDLC processes15-20%- 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. Configure agent planning to be distinct from agent execution
                                                                                                    • 4. Validate agent plans
                                                                                                      - 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
                                                                                                            - 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: Manage memory, state, and execution10-15%- Persist agent state and manage context drift
                                                                                                                  • 1. Detect and correct drift during extended agent execution
                                                                                                                    • 2. Capture task progress and decisions as durable artifacts
                                                                                                                      • 3. Resume agent work without repeating steps or diverging from prior decisions
                                                                                                                        - Implement agent memory strategies
                                                                                                                        • 1. Choose between short-term, long-term, and external memory
                                                                                                                          • 2. Scope agent memory to task-relevant information
                                                                                                                            • 3. Define memory expiration, pruning, and reset rules
                                                                                                                              - Ensure continuity of agent memory and state across tools and environments
                                                                                                                              • 1. Prevent stale context
                                                                                                                                • 2. Prevent conflicting context
                                                                                                                                  • 3. Share agent state

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                                                                                                                                    Microsoft Developing in Agentic AI Systems Sample Questions (Q36-Q41):

                                                                                                                                    NEW QUESTION # 36
                                                                                                                                    Hotspot Question
                                                                                                                                    Your company uses Microsoft Visual Studio Code and GitHub Copilot Chat.
                                                                                                                                    You have a GitHub repository that uses main as the default branch. The repository contains a workspace custom agent stored at .github/agents/release-notes.agent.md.
                                                                                                                                    A developer switches to a branch named branch1 where the agent file does NOT exist. In the same session, the developer switches to a user profile named profile1.profile1 contains a custom agent file named release-notes.agent.md that has user-invokable set to false.
                                                                                                                                    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:

                                                                                                                                    Explanation:
                                                                                                                                    Box 1: No
                                                                                                                                    The agent will not appear in the agents dropdown with a display name of release-notes because the developer is currently on branch1 and profile1, which effectively hides it.
                                                                                                                                    Box 2: No
                                                                                                                                    The workspace version of the release-notes agent will not be available in the agents dropdown when the developer is on branch2.
                                                                                                                                    Box 3: No
                                                                                                                                    The user profile agent will not appear in the agents dropdown, nor will it override a workspace agent in this scenario.
                                                                                                                                    Reference:
                                                                                                                                    https://code.visualstudio.com/docs/agent-customization/custom-agents


                                                                                                                                    NEW QUESTION # 37
                                                                                                                                    An agent working through the Copilot coding agent in GitHub Actions needs additional build dependencies installed before it can run tests. Where should you define this setup?

                                                                                                                                    Answer: B

                                                                                                                                    Explanation:
                                                                                                                                    copilot-setup-steps.yml defines a custom setup workflow (e.g., installing dependencies, configuring environment variables) that runs before the coding agent starts its task, ensuring it has the environment it needs.


                                                                                                                                    NEW QUESTION # 38
                                                                                                                                    Case Study 2
                                                                                                                                    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

                                                                                                                                    You need to troubleshoot the issue reported by Ben.
                                                                                                                                    What should you review?

                                                                                                                                    Answer: B

                                                                                                                                    Explanation:
                                                                                                                                    Scenario: 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.
                                                                                                                                    To investigate a successful Copilot agent session that created an empty pull request, you should check the agent session log in the Agents panel.
                                                                                                                                    Internal behavior: The empty pull request means the agent completed its logical execution loop without generating or committing code modifications.
                                                                                                                                    Session context: The agent session log contains the specific LLM prompts, tool execution outputs, and file parsing steps that explain why the agent decided no changes were necessary.
                                                                                                                                    Reference:
                                                                                                                                    https://github.blog/ai-and-ml/github-copilot/whats-new-with-github-copilot-coding-agent/


                                                                                                                                    NEW QUESTION # 39
                                                                                                                                    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?

                                                                                                                                    Answer: A

                                                                                                                                    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 # 40
                                                                                                                                    You have a GitHub Enterprise repository that runs an autonomous agent by using a GitHub Actions workflow. The workflow has the following jobs:
                                                                                                                                    - agent-run that generates trace.json and plan.md
                                                                                                                                    - review that waits for human approval before continuing
                                                                                                                                    - deploy that uses the outputs from agent-run
                                                                                                                                    You need to make the files inspectable in the GitHub Actions UI and ensure that the files are available to the review and deploy jobs.
                                                                                                                                    What should you do in the workflow?

                                                                                                                                    Answer: B


                                                                                                                                    NEW QUESTION # 41
                                                                                                                                    ......

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