GitHub · Associate
GitHub Copilot (GH-300)
Demonstrate knowledge of GitHub Copilot features, responsible AI principles, and practical developer use cases across IDE, CLI, and code review.
GH-300 exam at a glance
| Exam code | GH-300 |
|---|---|
| Vendor | GitHub |
| Level | Associate |
| Domains | 6 |
| Published practice questions | 115 |
| Our mock exam | 60 questions in 90 minutes |
| Our practice pass target | 70% |
| Checked against the exam guide | September 22, 2026 |
| Official exam page | GitHub — GH-300 |
The mock exam length, time limit and pass target are our practice settings, not official exam scores or a guarantee of passing. Check the official GH-300 exam page for the current format, price and scoring before you book.
GH-300 exam domains
Weights show our practice allocation, built from the official exam guide; official blueprints may give ranges. Each domain links to its free sample questions (with explanations) and study guide preview once they are published.
Use GitHub Copilot Responsibly
18%Understand responsible AI principles, the risks and limitations of generative AI tools, and how to validate Copilot's output.
- Responsible AI principles: fairness, reliability, privacy, security, inclusiveness
- Risks and limitations of generative AI tools
- Ethical and responsible AI usage; identifying potential harms and mitigations
- The need to validate AI output before relying on it
- Operating GitHub Copilot responsibly in day-to-day development
- GitHub Copilot Trust Center: data handling, telemetry, opt-outs
- GitHub Copilot for Business vs Individual: data privacy differences
- Copyright and IP: GitHub Copilot and intellectual property concerns
Free Use GitHub Copilot Responsibly practice questions →Use GitHub Copilot Responsibly study guide →
Use GitHub Copilot Features
27%Know the GitHub Copilot subscription tiers, IDE and CLI features, Agent Mode, and code review capabilities.
- Copilot Individual, Business, and Enterprise: what each plan includes
- Copilot in the IDE: enabling Copilot, inline suggestions, chat, CLI, and Agent Mode
- Copilot Chat: slash commands, @workspace, @terminal, prompt file reuse
- GitHub Copilot CLI: installation, interactive sessions, generating scripts and managing files
- Agent Mode, Copilot Edits, and MCP for enhanced development workflows
- Agent Sessions and delegating tasks to Sub-Agents for optimized context usage
- Copilot PR summaries: auto-generating PR descriptions
- Copilot code review: requesting Copilot as a reviewer, customizable review standards via instructions files
- Spaces and Spark for organizing and prototyping with Copilot
- Organization-wide policy management, Copilot Code Review policies, and audit log events
- Managing Copilot subscriptions and seats via the REST API
Free Use GitHub Copilot Features practice questions →Use GitHub Copilot Features study guide →
Understand GitHub Copilot Data and Architecture
13%Explain how Copilot processes data, builds prompts, and where its architecture limits what it can do.
- How GitHub Copilot works: LLM training on code, context window, token limits
- Data usage, flow, and sharing between the IDE, GitHub, and the model
- Input processing and prompt building
- Proxy filtering and post-processing of suggestions
- Proxy and network requirements for Copilot in restricted environments
- The code suggestion lifecycle, end to end
- Limitations of LLMs and of Copilot specifically
Free Understand GitHub Copilot Data and Architecture practice questions →Understand GitHub Copilot Data and Architecture study guide →
Apply Prompt Engineering and Context Crafting
13%Write effective prompts and understand how Copilot determines and uses context.
- Prompt structure and context: what goes into an effective prompt
- How Copilot determines context from open files, comments, and history
- Zero-shot vs few-shot prompting
- Best practices for prompt crafting
- Prompt engineering principles and prompt process flow
- Chat history usage across a session
- Chat variables: #file, #selection, #editor, #terminalLastCommand
- Context optimization: keeping relevant files open, comments as prompts
Free Apply Prompt Engineering and Context Crafting practice questions →Apply Prompt Engineering and Context Crafting study guide →
Improve Developer Productivity with GitHub Copilot
15%Apply Copilot to accelerate coding, testing, documentation, and legacy-code work.
- Code completion: accepting full suggestion (Tab), partial word (Ctrl+Right)
- Code generation, refactoring, and documentation with Copilot
- Generating unit and integration tests; identifying edge cases and writing assertions
- Explaining unfamiliar or legacy codebases; accelerating learning and reducing context switching
- Generating sample data and modernizing legacy code
- Suggesting security improvements and performance optimizations
- Fixing bugs: /fix on highlighted errors, explaining stack traces
- GitHub Copilot CLI: gh copilot suggest, gh copilot explain
- Limitations: hallucinations, outdated APIs, security misses — always review
Free Improve Developer Productivity with GitHub Copilot practice questions →Improve Developer Productivity with GitHub Copilot study guide →
Configure Privacy, Content Exclusions, and Safeguards
14%Configure content exclusions, understand output ownership, and apply safeguards against unwanted suggestions.
- Content exclusion: .copilotignore, repository-level and editor-level exclusions
- Ownership and limitations of Copilot-generated outputs
- Public code filtering: enabling suggestions matching public code, and duplicate detection
- Policy management for content exclusions and safeguards, enabling/disabling features per org
- Resolving issues with suggestions and content exclusions
- Audit events: Copilot seat assignment, suggestion acceptance telemetry
- Proxy configuration: allowing Copilot through corporate proxies
- Feedback collection: thumbs up/down on suggestions, reporting issues
Free Configure Privacy, Content Exclusions, and Safeguards practice questions →Configure Privacy, Content Exclusions, and Safeguards study guide →
GH-300 FAQ
How is the GitHub Copilot (GH-300) practice assessment configured?
Our practice format targets 60 questions in 90 minutes. Availability depends on the published question bank and is shown on this course page. Official exam question counts and formats may differ.
What does the 70% practice target mean?
Check the provider's current exam guide for official scoring. A practice percentage is not an official scaled score. DataCertPrep uses 70% as an internal practice target, not a prediction or guarantee of passing.
How should I assess my progress in GitHub Copilot (GH-300)?
Use practice accuracy to find weak topics. Repeating familiar questions can inflate your score. Check coverage of the current objectives, practise hands-on tasks, and assess yourself on unseen questions.
What is the best way to study GitHub Copilot (GH-300)?
Read the available guides, work through practice explanations, use flashcards and complete practical exercises. Take a timed assessment when available. Check the provider's current exam guide before booking your exam.
Should I use GH-300 exam dumps?
No. "Dumps" are questions copied or recalled from the live exam. Certification exams are covered by a candidate agreement that prohibits sharing or using them, and breaking it can lead to a revoked certification or a ban from future exams. Dumps are also often wrong, and they go out of date when the exam blueprint changes. Memorising answers does not build the skills the exam tests. Study the official GitHub exam guide and practise with original questions that explain each answer, such as the free sample questions linked on this page.