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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Perform evaluation, error analysis, and tuning | 15–20% | - Define metrics and quality standards for outputs - Diagnose failures, hallucinations, and unexpected behavior - Optimize prompts, tools, and behavior through iteration - Test, validate, and compare agent results |
| Topic 2: Prepare agent architecture and SDLC processes | 15–20% | - Design agent autonomy and decision boundaries - Plan agent deployment, monitoring, and maintenance - Define agent purpose, scope, and success criteria - Integrate agents into software development lifecycle |
| Topic 3: Manage memory, state, and execution | 10–15% | - Choose memory types: short-term, long-term, external - Implement memory cleanup and expiration rules - Handle execution flow, retries, and interruptions - Scope and persist agent state correctly |
| Topic 4: Implement guardrails and accountability | 10–15% | - Add validation, review, and approval gates - Enforce least privilege and security boundaries - Log actions, decisions, and changes for audit - Ensure compliance, safety, and responsible use |
| Topic 5: 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 |
| Topic 6: Orchestrate multi-agent coordination | 15–20% | - Design workflows for multiple agents - Prevent conflicts and manage shared resources - Monitor and troubleshoot multi-agent execution - Define communication and handoff protocols |
1. You are troubleshooting why a Copilot coding agent pull request keeps failing CI checks after every attempted fix. What is the most effective first step?
A) Switch the agent to --allow-all mode
B) Increase MCP server rate limits
C) Enable Copilot memory
D) Add clear, reproducible steps and expected behavior to the issue
2. 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?
A) .copilotignore
B) copilot-instructions.md
C) copilot-setup-steps.yml
D) agents.md
3. 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 CODEOWNERS entry
C) A .copilotignore rule
D) An MCP server
4. You want a reusable, shareable set of instructions that defines a specific persona/workflow (e.g.,
"security reviewer") that developers can invoke on demand in Copilot Chat, rather than instructions applied globally to every session. What should you create?
A) .copilotignore
B) copilot-instructions.md
C) A prompt file (.prompt.md)
D) agents.md
5. 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?
A) Use dependency caching to store trace.json and plan.md.
B) Store trace.json and plan.md on a network share and have later jobs retrieve them from the share.
C) Upload trace.json and plan.md as workflow artifacts in agent-run, and download the files inside the jobs.
D) Commit trace.json and plan.md back to the repository from agent-run.
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: C | Question # 3 Answer: D | Question # 4 Answer: C | Question # 5 Answer: C |
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