This repository serves as a comprehensive study resource for preparing for the GitHub Copilot Certification. It includes module breakdowns, resource links, tips, and preparation strategies.
The GitHub Copilot Certification assesses your knowledge and practical ability to effectively use GitHub Copilot to enhance software development productivity. It includes scenario-based questions testing Copilot’s usage in real-world programming workflows.
- Exam Type: Multiple Choice Questions (MCQ), scenario-based
- Difficulty Level: Beginner to Intermediate (Developer focused)
- Duration: 60 minutes
- Passing Score: 70%
| Domain | Weightage |
|---|---|
| 1. Responsible AI | 7% |
| 2. GitHub Copilot Plans and Features | 31% |
| 3. How GitHub Copilot Works and Handles Data | 15% |
| 4. Prompt Crafting and Prompt Engineering | 9% |
| 5. Developer Use Cases for AI | 14% |
| 6. Testing with GitHub Copilot | 9% |
| 7. Privacy Fundamentals and Context Exclusions | 15% |
- 🧭 GitHub Copilot Certification Portal
- 📄 GitHub Copilot Documentation
- 💡 GitHub Copilot Labs
- ✍️ GitHub Copilot for Docs
- GitHub Copilot Fundamentals - Understand the AI Pair Programmer
- Accelerate App Development by Using GitHub Copilot
GitHub Copilot works with the following IDEs:
- Visual Studio Code
- Visual Studio
- JetBrains IDEs (e.g., IntelliJ, PyCharm, WebStorm)
- Neovim (with community plugins)
Prompt engineering in GitHub Copilot is based on the 4S principles:
- Specific – Be clear about what you want.
- Structured – Use comments and code structure.
- Step-by-step – Guide Copilot through small steps.
- Starter code – Give a strong starting point.
✅ This section will grow over time with practice questions and answer breakdowns.
- What types of suggestions does Copilot provide?
- How to control Copilot’s suggestions in VSCode?
- Which practices help improve suggestion accuracy?
- What are the limitations and ethical guidelines of Copilot?
- Understand Copilot’s strengths and weaknesses.
- Practice writing structured prompts.
- Explore Copilot in multiple IDEs to understand behavior.
- Review ethical concerns, especially around code suggestion origin.
- Practice using Copilot Labs and experiment with test generation.
This domain covers the principles and best practices of using AI tools—such as GitHub Copilot—responsibly and ethically in software development.
- Describe the risks associated with using AI
- Explain the limitations of using generative AI tools
(e.g. depth of the source data for the model, bias in the data, etc.) - Explain the need to validate the output of AI tools
- Identify how to operate a responsible AI
- Identify the potential harms of generative AI
(e.g. bias, insecure code, fairness, privacy, transparency) - Explain how to mitigate the occurrence of potential harms
- Explain ethical AI
📌 These objectives build a foundational understanding of how to use GitHub Copilot in a secure, ethical, and socially responsible manner.
This domain is the largest portion of the exam and covers the different GitHub Copilot offerings, their features, and how to configure and use them across various environments (IDE, CLI, GitHub.com). It also addresses how Copilot functions in enterprise settings and how to manage subscriptions and security policies.
- Identify the different GitHub Copilot plans
- Understand differences between:
- Copilot Individual
- Copilot Business
- Copilot Enterprise
- Copilot Business for non-GitHub Enterprise
- Understand Copilot usage for non-GitHub customers
- Define GitHub Copilot and Copilot Chat in the IDE
- Describe various ways to trigger Copilot:
- Inline suggestions
- Copilot Chat
- Multiple suggestions
- Exception handling
- CLI
- Identify features available in Copilot Individual
- Compare features between Copilot Individual and Copilot Business:
- Data exclusion
- IP indemnity
- Billing and license management
- Demonstrate:
- File exclusions
- Organization-wide policy management
- Audit log search for Copilot Business
- Managing Copilot Business subscriptions via REST API
- Identify key features and best use cases of GitHub Copilot Chat
- Understand:
- How to improve performance with Copilot Chat
- Its limitations
- Options to use code suggestions
- Feedback and slash command features
- Identify features of GitHub Copilot Enterprise
- Explain:
- Benefits of using Copilot Chat on GitHub.com
- Copilot-generated pull request summaries
- Configure and manage Knowledge Bases:
- Code snippets, best practices, design patterns
- Search and indexing
- Describe the use of custom models for Copilot Enterprise
- Installation steps for Copilot CLI
- Common Copilot CLI commands
- CLI configuration settings
✅ Mastery of this domain will ensure that you understand all GitHub Copilot offerings and can confidently use and manage them across personal, team, and enterprise contexts.
This domain focuses on how GitHub Copilot gathers, processes, and uses contextual information to generate code suggestions. It covers the internal data flow, prompt construction, and architecture of the suggestion engine, including post-processing and limitations.
- Describe the data pipeline lifecycle of Copilot suggestions
- Visualize the end-to-end flow of a Copilot code suggestion:
- Context gathering
- Prompt building
- Proxy service and filters
- Model response generation
- Post-processing through proxy
- Code matching and ranking
- Explain how GitHub Copilot gathers context from your code
- Understand prompt construction and filtering logic
- Identify different input types used in Copilot Chat
- Describe context window limitations
- Describe how GitHub Copilot handles:
- User data and context inputs
- Copilot Individual vs Copilot Business/Enterprise data handling
- Explain:
- Data flow for code completion
- Data flow for Copilot Chat
- Explain the limitations of Copilot and large language models (LLMs):
- Limited context awareness
- Influence of frequently seen code examples
- Staleness of training data
- Lack of precise reasoning or computation
🧩 A clear understanding of how Copilot works under the hood helps you make informed, secure, and effective use of its code suggestions.
This domain covers the principles and techniques for crafting effective prompts when working with GitHub Copilot and Copilot Chat. It introduces foundational strategies like zero-shot and few-shot prompting, structuring inputs, and best practices to maximize AI-generated suggestions.
- Describe the fundamentals of prompt crafting
- Identify the parts of a prompt and their roles
- Understand how context is determined for a prompt
- Describe different language options used in prompts
- Understand how chat history contributes to prompt context
- Describe prompt engineering and its relevance in Copilot usage
- Understand zero-shot vs few-shot prompting
- Explain the prompt process flow
- Learn prompt crafting best practices to improve suggestion quality
💡 Well-crafted prompts are essential to guide GitHub Copilot effectively. Treat the AI like a pair programmer—clear communication leads to better output.
This domain explores how GitHub Copilot can enhance productivity across the software development lifecycle. It includes real-world scenarios and tasks where Copilot can assist—ranging from learning new languages to debugging, refactoring, and even managing legacy systems.
- Describe how AI can enhance developer productivity in day-to-day workflows
- Identify use cases where Copilot helps with:
- Learning new languages or frameworks
- Language translation
- Context switching between tasks
- Writing documentation
- Generating sample data
- Modernizing legacy codebases
- Debugging code
- Performing code refactoring
- Supporting data science workflows
- Explain how Copilot supports various phases of the Software Development Lifecycle (SDLC)
- Discuss how to integrate Copilot into planning, coding, testing, and reviewing stages
- Understand how to use the Copilot Productivity API to:
- Track usage
- Measure impact on code completion and task efficiency
🚀 This domain focuses on practical applications—knowing when and how to use Copilot is key to unlocking real productivity gains.
This domain focuses on leveraging GitHub Copilot to assist with software testing. It covers test generation, enhancing test coverage, and identifying edge cases—all aimed at improving code quality and development confidence.
- Describe how GitHub Copilot can generate different types of tests:
- Unit tests
- Integration tests
- Other test types (e.g. API, UI, smoke tests)
- Explain how Copilot helps with:
- Identifying edge cases
- Suggesting assertions
- Improving effectiveness of existing tests
- Writing boilerplate for test scenarios
- Describe how Copilot can:
- Suggest test-driven improvements
- Learn from existing tests to propose better structure or catch issues
- Identify security vulnerabilities
- Recommend performance optimizations
- Understand how to configure Copilot for testing workflows:
- Editor-level configuration
- Organization-level configuration
- Understanding the Copilot Editor config file
✅ This domain demonstrates that GitHub Copilot is not just for writing application code—it’s also a valuable assistant for improving software quality through automated testing support.
This domain covers the privacy, security, and governance features of GitHub Copilot. It includes how Copilot handles content exclusions, safeguards against data leakage, and how users and organizations can configure Copilot to align with privacy and compliance requirements.
- Identify how to configure content exclusions:
- At the repository level
- At the organization level
- Explain the effects and limitations of exclusions:
- What Copilot can or cannot see
- How exclusions impact suggestion accuracy
- Describe ownership considerations for Copilot-generated content
- Explain the concept of Copilot outputs as suggestions—not authored code
- Clarify the responsibility of developers in reviewing, editing, and validating AI output
- Describe the role of the duplication detector filter
- Understand contractual protections (especially in Copilot Business/Enterprise)
- Explain Copilot settings on GitHub.com:
- Enabling/disabling duplication detection
- Enabling/disabling prompt & suggestion collection
- Describe Copilot’s security checks and warning messages
- Troubleshoot when:
- Suggestions are not appearing
- Context exclusions aren’t applied
- Suggestions are suboptimal
- Context is missing due to editor configuration
🔍 This domain ensures you are aware of how GitHub Copilot fits into a secure and compliant development workflow, particularly in enterprise environments.