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| Section | Weight | Objectives |
|---|---|---|
| Manage memory, state, and execution | 10–15% | - Handle execution flow, retries, and interruptions - Scope and persist agent state correctly - Choose memory types: short-term, long-term, external - Implement memory cleanup and expiration rules |
| Prepare agent architecture and SDLC processes | 15–20% | - Define agent purpose, scope, and success criteria - Plan agent deployment, monitoring, and maintenance - Design agent autonomy and decision boundaries - Integrate agents into software development lifecycle |
| Perform evaluation, error analysis, and tuning | 15–20% | - Test, validate, and compare agent results - Diagnose failures, hallucinations, and unexpected behavior - Define metrics and quality standards for outputs - Optimize prompts, tools, and behavior through iteration |
| Orchestrate multi-agent coordination | 15–20% | - Design workflows for multiple agents - Monitor and troubleshoot multi-agent execution - Prevent conflicts and manage shared resources - Define communication and handoff protocols |
| Implement tool use and environment interaction | 20–25% | - Implement tools, custom actions, and MCP servers - Configure and extend GitHub Copilot agents - Manage permissions and environment access - Connect agents to codebase, APIs, and external systems |
| Implement guardrails and accountability | 10–15% | - Ensure compliance, safety, and responsible use - Enforce least privilege and security boundaries - Log actions, decisions, and changes for audit - Add validation, review, and approval gates |
1. You have a repository on github.com that uses the GitHub Copilot coding agent.
You also use the GitHub Copilot CLI locally to reproduce failures and continue the same work from your terminal.
You need to verify the current token usage.
Which Copilot CLI slash command should you run?
A) /compact
B) /usage
C) /diff
D) /context
2. You need Copilot's coding agent to interact with an external ticketing system (e.g., Jira) so it can pull issue details as part of its workflow. What should you configure?
A) A branch protection ruleset
B) A .copilotignore rule
C) An MCP server
D) A CODEOWNERS entry
3. You have a repository that uses the GitHub Copilot coding agent and supports hooks stored under .github/hooks.
You need a Shell command to run automatically whenever an agent execution fails.
Which type of hook should you use?
A) errorOccurred
B) sessionEnd
C) agentStop
D) postToolUse
4. Drag and Drop Question
Your team uses a remote GitHub Model Context Protocol (MCP) server for workflows in the software development life cycle (SDLC).
You need to commit a workspace-scoped MCP configuration to ensure that GitHub Copilot can connect to the GitHub-hosted MCP endpoint and authenticate by using a GitHub personal access token (PAT).
How should you complete the mcp.json configuration file? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
5. 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?
A) the GITHUB_TOKEN permissions block in the agent1 workflow
B) the GitHub Actions runner log for the session job
C) the agent session log in the Agents panel
D) the GitHub Actions usage metrics of repo1
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: C | Question # 3 Answer: A | Question # 4 Answer: Only visible for members | Question # 5 Answer: C |
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