GITHUBGitHub Copilot Certification
The GitHub Copilot Certification validates your ability to optimize software development workflows with GitHub Copilot. It covers responsible AI, Copilot plans and features, prompt engineering, AI developer use cases, testing with Copilot, and privacy and exclusions. Earning this certification demonstrates that you can effectively integrate AI-assisted development into your daily work, boosting productivity and code quality.
328 practice questions · Updated 2026-07-30
6Domains
14Objectives
85Concepts
328Questions
GH-300 Curriculum
Every domain, objective, and concept the GH-300 exam measures.
- Risks of Generative AI
- Limitations of Generative AI
- Ethical AI Principles
- Responsible AI Practices
- Potential Harms of AI
- Mitigation Strategies
- Purpose of AI output validation
- Validation techniques for AI-generated code
- Responsible use principles for GitHub Copilot
- Operational safeguards for Copilot
- Enabling GitHub Copilot in the IDE
- Triggering inline suggestions
- Using Copilot Chat
- Using Copilot in the CLI
- Using Copilot agent mode
- Configuring content exclusions
- Definition and benefits of GitHub Copilot CLI
- Installation prerequisites and steps
- Core features and commands
- Interactive usage and session management
- Script generation and file management
- Agent Mode
- Copilot Edits
- MCP (Model Context Protocol)
- Agent Sessions
- Sub-Agents
- Copilot Code Review
- Copilot Coding Assistance
- Spaces
- Spark
- Pull Request Summaries
- Customizable Review Standards via Instructions Files
- Chat Limits and Options
- Chat Feedback
- Chat Commands
- Prompt File Reuse
- Organization-wide policy management
- Copilot Code Review policy configuration
- Feature availability across IDEs and github.com
- Audit log events for Copilot
- Subscription management via REST API
- Data usage and flow
- Data sharing and privacy
- Input processing
- Prompt building
- Proxy filtering
- Post-processing of outputs
- Code suggestion lifecycle overview
- Prompt construction and context assembly
- Suggestion generation and ranking
- User interaction and feedback loop
- LLM limitations in code generation
- Copilot-specific limitations
- Security and privacy limitations
- Mitigation strategies for limitations
- Prompt structure components
- Context in prompts
- Context determination factors
- Zero-shot prompting
- Few-shot prompting
- Prompt crafting best practices
- Prompt engineering principles
- Prompt process flow
- Chat history usage
- Code generation with Copilot
- Refactoring with Copilot
- Documentation generation with Copilot
- Learning acceleration with Copilot
- Reducing context switching with Copilot
- Generating sample data with Copilot
- Modernizing legacy code with Copilot
- Generate unit tests
- Generate integration tests
- Identify edge cases
- Write assertions
- Suggest security improvements
- Suggest performance optimizations
- Configure content exclusions
- Configure editor settings for privacy
- Describe ownership of outputs
- Describe limitations of outputs
- Enable suggestions matching public code filtering
- Troubleshoot suggestions matching public code filtering
- Resolve issues with suggestions
- Resolve issues with content exclusions
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Percentages reflect share of the current practice bank, not official exam weightings — no structured per-skill weight is published for GH-300, so none is invented.