GH-300: GitHub Copilot Practice Exam

The GitHub Copilot Certification (GH‑300) is an intermediate‑level credential offered through GitHub in partnership with Microsoft. It validates your proficiency in using GitHub Copilot as an AI-powered coding assistant across different development scenarios. The certificate is valid for two years.
 

Who should take the exam?

The GH-300: GitHub Copilot Exam is ideal for developers, DevOps professionals, team leads, or project managers who already use GitHub and want to level up their AI-assisted workflows.

Knowledge Gained

By preparing for this certification, you’ll sharpen your understanding of:

• Ethical and responsible AI use: Understand risks, bias, and why validation of AI output matters.
• GitHub Copilot features and plans: From Individual to Business and Enterprise tiers, chat vs inline suggestions, CLI use, audit logs, and policy controls.
• How Copilot works under the hood: Data flow, prompt creation, context handling, proxy filtering, and LLM response lifecycle.
• Prompt crafting and prompt engineering: Best practices for zero‑shot vs few‑shot prompting, context setup, chat history, and language selection.
• Developer-centric AI use cases: Boost productivity in testing, scaffolding, documentation, refactoring, context-switching, and learning new languages/frameworks.
• AI-powered testing: How Copilot can assist in generating unit and integration tests, identifying edge cases, and strengthening test coverage.
• Privacy, context exclusions, and policy setting: Managing content exclusions, privacy settings, duplicate detection, ownership, and secure configuration across repo/org.
   

Skills Required Before Taking the Exam

To take this exam confidently, you should already:

• Be familiar with GitHub basics, such as repos, pull requests, and workflows
• Have hands‑on experience using GitHub Copilot in your IDE or CLI
• Understand different Copilot subscription plans, how to configure exclusions, and manage policies
• Be comfortable crafting effective prompts and interpreting AI‑generated code
• Know how to spot and resolve biases or inaccuracies in AI code suggestions
• Understand privacy controls, content exclusion rules, and how they affect AI outputs

Course Outline

The GH-300: GitHub Copilot Exam covers the following topic -

Module 1: Understand Responsible AI (7%)
1.1 Describe the 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

Module 2:Understand GitHub Copilot plans and features (31%)
2.1 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)

2.2 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

2.3 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

2.4 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

2.5 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

2.6 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

Module 3: Understand How GitHub Copilot works and handles data (15%)
3.1 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

3.2 Explain 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)

3.3 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

Module 4: Understand Prompt Crafting and Prompt Engineering (9%)
4.1 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

4.2 Explain the fundamentals of prompt engineering

• Explain prompt engineering principles, training methods, and best practices
• Describe the prompt process flow

Module 5: Understand Developer use cases for AI (14%)
5.1 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

Module 6: Understand Testing with GitHub Copilot (9%)
6.1 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

6.2 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

Module 7: Understand Privacy fundamentals and context exclusions (15%)
7.1 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

7.2 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

7.3 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

7.4 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