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Microsoft GH-600 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Perform evaluation, error analysis, and tuning15-20%- 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. Specify expected outcomes and operational constraints for agent tasks
                • 3. Identify qualitative and quantitative evaluation signals to evaluate agents
                  • 4. Generate evaluation signals by using automated scanning tools
                    Topic 2: Implement guardrails and accountability10-15%- Implement guardrails and human-in-the-loop workflows
                    • 1. Preserve execution velocity by minimizing approvals that do not materially reduce risk
                      • 2. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes
                        • 3. Block actions that violate defined security, compliance, or Responsible AI policies
                          • 4. Scope permissions and execution contexts to enforce least-privilege access
                            • 5. Identify the subset of actions that require human judgment
                              - 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
                                  Topic 3: Orchestrate multi-agent coordination15-20%- Operate and manage multi-agent workflows
                                  • 1. Detect and resolve agent conflicts, including overlapping code changes, duplicated effort, and contradictory outputs
                                    • 2. Configure agent isolation for parallel execution
                                      • 3. Apply an orchestration pattern to coordinate multiple 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
                                              - 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
                                                    - 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
                                                          Topic 4: Prepare agent architecture and SDLC processes15-20%- Integrate agents into the software development lifecycle (SDLC)
                                                          • 1. Identify steps for agents to perform
                                                            • 2. Define inputs, outputs, and success criteria for agents
                                                              • 3. Identify and mitigate common anti-patterns in 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
                                                                      - Define boundaries between planning, reasoning, and action
                                                                      • 1. Configure an agent to output a structured plan
                                                                        • 2. Validate agent plans
                                                                          • 3. Prevent agent action until the agent checked and approved
                                                                            • 4. Configure agent planning to be distinct from agent execution
                                                                              Topic 5: Manage memory, state, and execution10-15%- Ensure continuity of agent memory and state across tools and environments
                                                                              • 1. Prevent conflicting context
                                                                                • 2. Prevent stale context
                                                                                  • 3. Share agent state
                                                                                    - Persist agent state and manage context drift
                                                                                    • 1. Detect and correct drift during extended agent execution
                                                                                      • 2. Resume agent work without repeating steps or diverging from prior decisions
                                                                                        • 3. Capture task progress and decisions as durable artifacts
                                                                                          - 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
                                                                                                Topic 6: Implement tool use and environment interaction20-25%- Configure MCP servers
                                                                                                • 1. Add an MCP server as a tool to an agent
                                                                                                  • 2. Configure MCP allow lists
                                                                                                    • 3. Configure the MCP registries
                                                                                                      • 4. Configure a GitHub remote MCP server
                                                                                                        - Operate agents with safe execution paths and robust error handling
                                                                                                        • 1. Implement retries
                                                                                                          • 2. Implement escalation paths
                                                                                                            • 3. Implement error handling
                                                                                                              • 4. Implement traceability and accountability for agent actions
                                                                                                                • 5. Implement rollbacks
                                                                                                                  - Integrate agents within development environments
                                                                                                                  • 1. Configure an agent to be invoked in a CI workflow
                                                                                                                    • 2. Configure an agent to use branch-based scope
                                                                                                                      • 3. Evaluate the execution context for an agent
                                                                                                                        • 4. Enable an agent to perform autonomous actions, including creating branches and pull requests
                                                                                                                          • 5. Configure an agent's scope to a specific repository
                                                                                                                            • 6. Configure an agent to handle environment-specific constraints
                                                                                                                              - Select and configure agent tools
                                                                                                                              • 1. Identify required tools
                                                                                                                                • 2. Configure agent tool permissions
                                                                                                                                  • 3. Configure agent tools

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                                                                                                                                    Microsoft Developing in Agentic AI Systems GH-600 Prüfungsfragen mit Lösungen (Q71-Q76):

                                                                                                                                    71. Frage
                                                                                                                                    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.

                                                                                                                                    Antwort: B,E

                                                                                                                                    Begründung:
                                                                                                                                    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


                                                                                                                                    72. Frage
                                                                                                                                    You have a GitHub repository that uses the GitHub Copilot coding agent to resolve issues and create draft pull requests. The repository has a ruleset named ruleset1 that enforces the following:
                                                                                                                                    Signed commits
                                                                                                                                    Branch protections that require status checks to pass before merge
                                                                                                                                    The agent is blocked from operating in the repository because it fails to comply with the signed-commits rule.
                                                                                                                                    You need to ensure that the agent can create and push changes to copilot/ branches. The solution must enforce the signed-commits rule for human developers on protected branches.
                                                                                                                                    What should you do?

                                                                                                                                    Antwort: D

                                                                                                                                    Begründung:
                                                                                                                                    Adding Copilot as a bypass actor for the ruleset permits the coding agent to create and push its working changes without removing the signed-commit control for human developers. This is the narrowly scoped exception required by the scenario.
                                                                                                                                    Making Copilot a repository owner grants excessive privilege and is not needed to resolve the signing restriction. Granting general push permission does not override a ruleset that blocks unsigned commits. Removing the signed-commit requirement weakens protection for every actor governed by the ruleset, including human developers on protected branches.
                                                                                                                                    A bypass actor should be used deliberately and limited to the required automated identity and scope. The protected branch requirements, status checks, and human review processes should remain in place before agent-generated changes are merged.
                                                                                                                                    Study-guide topics: rulesets, bypass actors, branch protection, and least-privilege exceptions.


                                                                                                                                    73. Frage
                                                                                                                                    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?

                                                                                                                                    Antwort: D

                                                                                                                                    Begründung:
                                                                                                                                    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


                                                                                                                                    74. Frage
                                                                                                                                    You assigned an issue to the Copilot coding agent, and it opened a pull request. You want to inspect exactly what code changes were made before merging. Which CLI slash command lets you view the change set directly in the terminal?

                                                                                                                                    Antwort: B

                                                                                                                                    Begründung:
                                                                                                                                    The correct command is /diff. GitHub Copilot CLI provides /diff specifically for reviewing code changes directly from the terminal. GitHub's current CLI command reference defines /diff as a command that reviews changes in the current directory and can automatically switch to a branch comparison when the working tree is clean. This makes it the appropriate command when validating an agent-generated change set before accepting or merging the work.
                                                                                                                                    The other commands serve different purposes. /context displays context-window utilization, including token allocation and remaining context capacity; it does not show code modifications. /compact summarizes conversation history to reclaim context-window space during long-running sessions. /plan creates an implementation plan before coding and restricts normal project-file modifications while the plan is being prepared.
                                                                                                                                    Using /diff supports an essential human-in-the-loop control in agentic software development: generated code should be inspected before it crosses a merge or deployment boundary. Reviewing the diff exposes additions, deletions, and modifications so that unintended or unsafe changes can be identified before integration.
                                                                                                                                    Study Guide Reference Topics: Implement Tool Use and Environment Interaction; agent-generated code review; human-in-the-loop validation; controlled execution and change inspection.


                                                                                                                                    75. Frage
                                                                                                                                    You are running the GitHub Copilot CLI and want the agent to execute file edits and shell commands without prompting for confirmation on each individual action, during a trusted throwaway sandbox session. Which flag/command should you use?

                                                                                                                                    Antwort: B

                                                                                                                                    Begründung:
                                                                                                                                    The --allow-all (commonly nicknamed "YOLO mode") flag disables per-action confirmation prompts, letting the agent run autonomously. This should only be used in isolated, low-risk environments since it removes a key safety checkpoint.


                                                                                                                                    76. Frage
                                                                                                                                    ......

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