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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Orchestrate multi-agent coordination	15-20%	- 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 - 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 - 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. Document key decisions, handoffs, and outcomes across agents 3. Perform post-hoc analysis of multi-agent behavior - 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
Topic 2: Implement guardrails and accountability	10-15%	- Define autonomy levels 1. Assign autonomy levels to maximize delivery speed while remaining compliant with organizational security and Responsible AI standards 2. Classify agent actions by operational, security, and compliance risk to right-size human interventions - Implement guardrails and human-in-the-loop workflows 1. Identify the subset of actions that require human judgment 2. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes 3. Preserve execution velocity by minimizing approvals that do not materially reduce risk 4. Scope permissions and execution contexts to enforce least-privilege access 5. Block actions that violate defined security, compliance, or Responsible AI policies
Topic 3: 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. Specify expected outcomes and operational constraints for agent tasks 2. Generate evaluation signals by using automated scanning tools 3. Identify qualitative and quantitative evaluation signals to evaluate agents 4. Align evaluation criteria with development intent - 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 4: Manage memory, state, and execution	10-15%	- 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 - Ensure continuity of agent memory and state across tools and environments 1. Share agent state 2. Prevent conflicting context 3. Prevent stale context - 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
Topic 5: Implement tool use and environment interaction	20-25%	- Select and configure agent tools 1. Identify required tools 2. Configure agent tools 3. Configure agent tool permissions - Configure MCP servers 1. Configure the MCP registries 2. Configure a GitHub remote MCP server 3. Add an MCP server as a tool to an agent 4. Configure MCP allow lists - Integrate agents within development environments 1. Evaluate the execution context for an agent 2. Configure an agent to use branch-based scope 3. Enable an agent to perform autonomous actions, including creating branches and pull requests 4. Configure an agent to handle environment-specific constraints 5. Configure an agent's scope to a specific repository 6. Configure an agent to be invoked in a CI workflow - Operate agents with safe execution paths and robust error handling 1. Implement error handling 2. Implement traceability and accountability for agent actions 3. Implement rollbacks 4. Implement escalation paths 5. Implement retries
Topic 6: Prepare agent architecture and SDLC processes	15-20%	- Define boundaries between planning, reasoning, and action 1. Validate agent plans 2. Configure an agent to output a structured plan 3. Prevent agent action until the agent checked and approved 4. Configure agent planning to be distinct from agent execution - Integrate agents into the software development lifecycle (SDLC) 1. Identify steps for agents to perform 2. Identify and mitigate common anti-patterns in agents 3. Define inputs, outputs, and success criteria for agents - Configure observability and control for autonomous agents 1. Configure agents to produce inspectable artifacts within standard development tooling 2. Configure human intervention for autonomous agents without slowing delivery 3. Plan and implement the degree of agent autonomy, including guardrails

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Microsoft Developing in Agentic AI Systems Sample Questions (Q12-Q17):

NEW QUESTION # 12
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 # 13
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

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

Answer:

Explanation:

Explanation:
Scenario:
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.
Box 1: Yes
Box 2: Yes
Box 3: Yes
-[Related to another question in this scenario]
The correct action to take is to delete the line with tools: ['read','cearch','edit'] from the agent configuration.
Enabling All Tools: In GitHub Copilot Agent configuration specifications, omitting the tools key entirely or deleting it allows the agent to automatically inherit and utilize all available tools in the runtime environment. Explicitly hardcoding a restricted array limits its capabilities.
Targeted Instructions: Modifying the repository's configuration for the agent ensures that the specific product-api guidelines apply strictly to that custom agent without bleeding into general Copilot Chat or standard Copilot code reviews.
Reference:
https://docs.github.com/en/copilot/how-tos/copilot-sdk/features/mcp

NEW QUESTION # 14
Hotspot Question
You have a GitHub repository that contains the following custom agent files:
- A file named planner.agent.md that includes YAML frontmatter with a
handoffs entry that has label: Start Implementation, agent:
implementer, and prompt: Now implement the plan outlined above
- A file named implementer.agent.md that is in the same directory as
planner.agent.md and includes YAML frontmatter that has name:
IMPLEMENTER
You add a third agent file named review.agent.md.review.agent.md includes YAML frontmatter that has name: code-review.
You make the following changes to planner.agent.md:
- Update the existing handoff to include send: true and model: GPT-5.2
(copilot).
- Add a second handoff that has label: Run Review, agent: code-review,
and prompt: Review the code changes made in the previous step.
No other agent files are modified.
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: Yes
GitHub Copilot Extensions and agent frameworks route handoffs by matching the agent identifier in the YAML frontmatter to the filename or the name property defined within that repository.
Box 2: Yes
The failure happens because the agent target pointer in the handoff configuration does not match the actual target agent's declaration or file name due to a mismatch introduced by your changes.
Box 3: Yes
Yes, selecting "Start Implementation" will switch the chat to the implementer agent and automatically submit the prompt using the specified model.
The updates made to your GitHub agent repository configure a seamless workflow transition.
Here is how your new settings process that action:
Trigger action: Clicking Start Implementation activates the specific handoff block you modified.
Agent switch: The agent: implementer property targets the file with name: IMPLEMENTER, routing the conversation there.Automated submission: Setting send: true forces the interface to submit the text immediately instead of leaving it in the text box.
Model override: The model: GPT-5.2 (copilot) line forces the system to process that automated submission using that specific model version.
Reference:
https://arinco.com.au/blog/github-copilot-multi-repo-instructions/
https://aminrj.com/posts/building-a-multi-agents-coding-workflow/

NEW QUESTION # 15
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 resolve the issue of the agents generating conflicting output. The solution must meet the implementation guidelines.
What should you do?

A. Configure tools: ['read', 'search'] in both agent profiles to prevent either agent from writing files.
B. Configure a concurrency group on both agent workflows so that only one workflow runs at a time.
C. Add shared/config.yaml to a CODEOWNERS file that requires SG_Review approval before any changes can be merged.
D. Configure each agent to work on a separate branch and add a required status check that detects file-level overlap before either pull request can be merged.

Answer: B

Explanation:
Scenario: Both agent1 and agent2 modified shared/config.yaml in a parallel test run, generating conflicting outputs.
To avoid race conditions and conflicting outputs when multiple Copilot agents modify the same shared/config.yaml file in parallel, the best practice is to configure a concurrency group on both agent workflows so that only one workflow runs at a time.
Workflow Concurrency
Using a concurrency group locks the shared resource. If one Copilot agent is already processing and modifying shared/config.yaml, the second agent's workflow will either queue or be canceled, preventing concurrent writes and eliminating file overwrites.
Incorrect:
Branch isolation is useful for developer edits, but in automated parallel agent operations, it still leads to frequent and painful merge conflicts if both agents make asynchronous structural updates to the same config file.
Reference:
https://docs.github.com/en/actions/how-tos/write-workflows/choose-when-workflows-run/control-workflow-concurrency

NEW QUESTION # 16
You have a GitHub repository that uses the GitHub Copilot coding agent to resolve issues and create draft pull requests.
You assign an issue to Copilot. Copilot creates a draft pull request. The pull request timeline shows Copilot started work, followed by status updates. The most recent status update is 55 minutes old.
You discover that the agent is no longer making progress.
You need to ensure that the work resumes without redoing the completed steps or changing the previously chosen approach.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 17
......

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