GitHub Copilot Fundamentals Part 2 of 2
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Table of Content:
- Question 1: What are the main learning objectives for the GitHub MCP Server module?
- Question 2: What is the purpose of customizing Copilot with your own review guidelines?
- Question 3: What are Premium Request Units (PRUs)?
- Question 4: ow does Copilot help in code reviews and pull requests?
- Question 5: List five key features of Copilot in code reviews.
- Question 6: How does Copilot contribute to security during reviews?
- Question 7: What role do PRUs play in unlocking advanced capabilities?
- Question 8: What are five ways Copilot helps developers during code reviews?
- Question 9: How can Copilot help in multi-language projects?
- Question 10: How does GitHub Copilot help resolve code review challenges?
- Question 11: Why are code reviews essential, and what common challenges do developers face during reviews?
- Question 12: What are best practices for using MCP and Agent Mode together?
- Question 13: What are the prerequisites before using GitHub MCP Server?
- Question 14: What problem does GitHub MCP Server solve?
- Question 15: What is the Model Context Protocol (MCP)?
- Question 16: Describe the three common ways MCP clients connect to servers.
- Question 17: Why should developers use the GitHub MCP Server instead of local setups?
- Question 18: What can developers do with the GitHub MCP Server in action?
- Question 19: How do you set up GitHub MCP Server in Visual Studio Code using OAuth?
- Question 20: What is the benefit of using OAuth for MCP setup?
- Question 21: How do you request a Copilot review on GitHub.com?
- Question 22: How does Copilot’s review differ from a human approval?
- Question 23: How does GitHub Copilot help with existing projects?
- Question 24: What is “prompt engineering” in GitHub Copilot?
- Question 25: How does the quality of the prompt affect Copilot’s output?
- Question 26: Give an example of a vague and an effective prompt.
- Question 27: What are some best practices when using GitHub Copilot?
- Question 28: What should you do if Copilot’s suggestions aren’t accurate?
- Question 29: How does Copilot use your open files?
- Question 30: How can you provide broader context to Copilot Chat?
- Question 31: How do you accept or cycle through suggestions in Copilot?
- Question 32: How can Copilot recognize a prompt or instruction?
- Question 33: Give an example of how to use a comment as a prompt in Copilot.
- Question 34: How long does Copilot take to review a pull request?
- Question 35: What is the purpose of .github/copilot-instructions.md?
- Question 36: What strategies can help optimize PRU usage?
- Question 37: How does GitHub Copilot generate code suggestions?
- Question 38: What is GitHub Codespaces?
- Question 39: What problem does Copilot aim to solve for developers?
- Question 40: Why do developers need GitHub Copilot?
- Question 41: How does GitHub Copilot work inside an IDE?
- Question 42: How can you provide context to GitHub Copilot?
- Question 43: Which platforms currently support the GitHub MCP Server?
- Question 44: What is GitHub Copilot Agent Mode?
- Question 45: How does Agent Mode assist in setting up a new microservice?
- Question 46: What does the developer focus on after Agent Mode sets up a microservice?
- Question 47: What are the advanced reasoning capabilities of Agent Mode?
- Question 48: What is premium reasoning in Agent Mode?
- Question 49: How does Agent Mode use project context to enhance accuracy?
- Question 50: Example of context-aware deployment?
- Question 51: What is iterative improvement and self-healing in Agent Mode?
- Question 52: Example of self-healing process?
- Question 53: What is a PRU in GitHub Copilot billing?
- Question 54: What is the “Draft–Review–Accept” workflow in Agent Mode?
- Question 55: What is an example of a multi-step task that Agent Mode can handle?
- Question 56: How is Agent Mode different from traditional AI-powered code completion tools?
- Question 57: What are the key tasks Agent Mode can perform autonomously?
- Question 58: What are the four main interaction modes of GitHub Copilot?
- Question 59: What is one of the most powerful aspects of Agent Mode?
- Question 60: How does Agent Mode process a given task?
- Question 61: What are the benefits of using Agent Mode in development workflows?
- Question 62: Example Task: How does Agent Mode create a new REST API endpoint?
- Question 63: How does Agent Mode handle multi-step tasks?
- Question 64: How does Agent Mode ensure user control and oversight?
- Question 65: Example of user oversight in action?
- Question 66: What are the main risks associated with Copilot coding agent and their mitigations?
- Question 67: What are known limitations of the Copilot coding agent?
- Question 68: How can the agent be extended using the Model Context Protocol (MCP)?
- Question 69: What are best practices when using MCP servers?
- Question 70: How can you validate the Copilot agent’s output before merging?
- Question 71: What are best practices for responsible use?
- Question 72: How does Copilot prevent privilege escalation and data leaks?
- Question 73: What is the GitHub MCP Server?
- Question 74: How does Copilot coding agent maintain security?
- Question 75: What are PRUs used for?
- Question 76: What resources does Copilot coding agent consume?
- Question 77: What are some limitations of Agent Mode?
- Question 78: How can developers maximize Agent Mode’s effectiveness?
- Question 79: What does GitHub Copilot Agent Mode ultimately represent?
- Question 80: What is GitHub Copilot coding agent?
- Question 81: How is Copilot coding agent different from IDE-based assistants?
- Question 82: Which GitHub plans support the Copilot coding agent?
- Question 83: What types of repositories support the Copilot coding agent?
- Question 84: How can users delegate work to the Copilot coding agent?
- Question 85: What is the purpose of the GitHub MCP Server?