The GH-300 practice test provides a comprehensive learning path for developers and technical professionals seeking to master AI-assisted development using GitHub Copilot. As modern software delivery increasingly relies on AI pair-programming, this certification preparation material is designed to equip you with the essential skills to effectively integrate Copilot into your daily workflows. You will gain a deep understanding of prompt engineering, model limitations, security and privacy implications, and responsible AI usage. By simulating the actual certification environment, this practice test bridges the gap between theoretical knowledge and real-world application, ensuring you are fully prepared to pass the exam and elevate your productivity in coding, testing, and documentation tasks.
Note: This is merely a practice test to prepare for the professional certification exam, and no certificate is issued by the center for passing it.
| Questions | 126 |
|---|---|
| Release Date | 09/2025 (Last Update: 09/2025) |
| Job Role | Software Developer |
| Language | English |
Why should I use the GH-300 Practice Test to prepare for the official exam?
Preparing with the GH-300 practice test is a critical step for validating your expertise in the GitHub Copilot ecosystem. This tool mirrors the structure and complexity of the official certification exam, allowing you to familiarize yourself with question formats while identifying specific areas for improvement. By focusing on your weak points through detailed performance metrics, you can optimize your study time and approach the real assessment with confidence. Achieving this certification not only demonstrates your proficiency in AI-powered development but also positions you as a forward-thinking professional capable of leveraging cutting-edge tools to enhance code quality, security, and developer productivity in enterprise environments.
Domain 1: Responsible AI – 8 questions
Explain responsible usage of AI
- Describe the risks associated with using AI
- Explain the limitations of using generative AI tools (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 (bias, secure code, fairness, privacy, transparency)
- Explain how to mitigate the occurrence of potential harms
- Explain ethical AI
Domain 2: GitHub Copilot plans and features – 37 questions
Identify the different GitHub Copilot plans
- Understand the differences between Copilot Individual, Copilot Business, Copilot Enterprise, and Copilot Business for non-GHE
- Understand Copilot for non-GitHub customers
- Define GitHub Copilot in the IDE
- Define GitHub Copilot Chat in the IDE
- Describe the different ways to trigger GitHub Copilot (chat, inline chat, suggestions, multiple suggestions, exception handling, CLI)
Identify the main features with GitHub Copilot Individual
- Explain the difference between GitHub Copilot Individual and GitHub Copilot Business (data exclusions, IP indemnity, billing, etc.)
- Understand the available features in the IDE for GitHub Copilot Individual
Identify the main features of GitHub Copilot Business
- Demonstrate how to exclude specific files from GitHub Copilot
- Demonstrate how to establish organization-wide policy management
- Describe the purpose of organization audit logs for GitHub Copilot Business
- Explain how to search audit log events for GitHub Copilot Business
- Explain how to manage GitHub Copilot Business subscriptions via the REST API
Identify the main features with GitHub Copilot Chat
- Identify the use cases where GitHub Copilot Chat is most effective
- Explain how to improve performance for GitHub Copilot Chat
- Identify the limitations of using GitHub Copilot Chat
- Identify the available options for using code suggestions from GitHub Copilot Chat
- Explain how to share feedback about GitHub Copilot Chat
- Identify the common best practices for using GitHub Copilot Chat
- Identify the available slash commands when using GitHub Copilot Chat
Identify the main features with GitHub Copilot Enterprise
- Explain the benefits of using GitHub Copilot Chat on GitHub.com
- Explain GitHub Copilot pull request summaries
- Explain how to configure and use Knowledge Bases within GitHub Copilot Enterprise
- Describe the different types of knowledge that can be stored in a Knowledge Base (e.g., code snippets, best practices, design patterns)
- Explain the benefits of using Knowledge Bases for code completion and review (e.g., improve code quality, consistency, and efficiency)
- Describe instructions for creating, managing, and searching Knowledge Bases within GitHub Copilot Enterprise, including details on indexing and other relevant configuration steps
- Explain the benefits of using custom models
Using GitHub Copilot in the CLI
- Discuss the steps for installing GitHub Copilot in the CLI
- Identify the common commands when using GitHub Copilot in the CLI
- Identify the multiple settings you can configure within GitHub Copilot in the CLI
Domain 3: How GitHub Copilot works and handles data – 18 questions
Describe the data pipeline lifecycle of GitHub Copilot code suggestions in the IDE
- Visualize the lifecycle of a GitHub Copilot code suggestion
- Explain how GitHub Copilot gathers context
- Explain how GitHub Copilot builds a prompt
- Describe the proxy service and the filters each prompt goes through
- Describe how the large language model produces its response
- Explain the post-processing of GitHub Copilot’s responses through the proxy server
- Identify how GitHub Copilot identifies matching code
Describe how GitHub Copilot handles data
- Describe how the data in GitHub Copilot individual is used and shared
- Explain the data flow for GitHub Copilot code completion
- Explain the data flow for GitHub Copilot Chat
- Describe the different types of input processing for GitHub Copilot Chat (types of prompts it was designed for)
Describe the limitations of GitHub Copilot (and LLMs in general)
- Describe the effect of most seen examples on the source data
- Describe the age of code suggestions (how old and relevant the data is)
- Describe the nature of GitHub Copilot providing reasoning and context from a prompt vs calculations
- Describe limited context windows
Domain 4: Prompt Crafting and Prompt Engineering – 11 questions
Describe the fundamentals of prompt crafting
- Describe how the context for the prompt is determined
- Describe the language options for promoting GitHub Copilot
- Describe the different parts of a prompt
- Describe the role of prompting
- Describe the difference between zero-shot and few-shot prompting
- Describe the way chat history is used with GitHub Copilot
- Identify prompt crafting best practices when using GitHub Copilot
Describe the fundamentals of prompt engineering
- Explain prompt engineering principles, training methods, and best practices
- Describe the prompt process flow
Domain 5: Developer use cases for AI – 19 questions
Improve developer productivity
- Describe how AI can improve common use cases for developer productivity
- Learning new programming languages and frameworks
- Language translation
- Context switching
- Writing documentation
- Personalized context-aware responses
- Generating sample data
- Modernizing legacy applications
- Debugging code
- Data science
- Code refactoring
- Discuss how GitHub Copilot can help with SDLC (Software Development Lifecycle) management
- Describe the limitations of using GitHub Copilot
- Describe how to use the productivity API to see how GitHub Copilot impacts coding
Domain 6: Testing with GitHub Copilot – 10 questions
Describe the options for generating testing for your code
- Describe how GitHub Copilot can be used to add unit tests, integration tests, and other test types to your code
- Explain how GitHub Copilot can assist in identifying edge cases and suggesting tests to address them
Describe the different SKUs for GitHub Copilot
- Describe the different SKUs and the privacy considerations for GitHub Copilot
- Describe the different code suggestion configuration options on the organization level
- Describe the GitHub Copilot Editor config file
Domain 7: Privacy fundamentals and context exclusions – 23 questions
Enhance code quality through testing
- Describe how to improve the effectiveness of existing tests with GitHub Copilot’s suggestions
- Describe how to generate boilerplate code for various test types using GitHub Copilot
- Explain how GitHub Copilot can help write assertions for different testing scenarios
Leverage GitHub Copilot for security and performance
- Describe how GitHub Copilot can learn from existing tests to suggest improvements and identify potential issues in the code
- Explain how to use GitHub Copilot Enterprise for collaborative code reviews, leveraging security best practices, and performance considerations
- Explain how GitHub Copilot can identify potential security vulnerabilities in your code
- Describe how GitHub Copilot can suggest code optimizations for improved performance
Identify content exclusions
- Describe how to configure content exclusions in a repository and organization
- Explain the effects of content exclusions
- Explain the limitations of content exclusions
- Describe the ownership of GitHub Copilot outputs
Safeguards
- Describe the duplication detector filter
- Explain contractual protection
- Explain how to configure GitHub Copilot settings on GitHub.com
- Enabling/disabling duplication detection
- Enabling/disabling prompt and suggestion collection
- Describe security checks and warnings
Troubleshooting
- Explain how to solve the issue if code suggestions are not showing in your editor for some files
- Explain why context exclusions may not be applied
- Explain how to trigger GitHub Copilot when suggestions are either absent or not ideal
- Explain steps for context exclusions in code editors
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