Exam: GH-300

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Audience Profile
This exam is designed for individuals in the field of software development who are proficient in using GitHub, including software developers, administrators, and project managers. This certification is intended for individuals who have a foundational understanding of GitHub Copilot as a product and its available features, along with hands-on experience in optimizing software development workflows using GitHub Copilot.

Skills Measured
NOTE: The bullets that follow each of the skills measured are intended to illustrate how we are assessing that skill. Related topics may be covered in the exam.

NOTE: Most questions cover features that are general availability (GA). The exam may contain questions on Preview features if those features are commonly used.

Domain 1: Responsible AI (7%)
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 (31%)
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 (15%)
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 (9%)
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 (14%)
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 (9%)
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 (15%)
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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Sample Question and Answers

QUESTION 1
Which of the following describes role prompting?

A. Describing in your prompt what your role is to get a better suggestion
B. Tell GitHub Copilot in what tone of voice it should respond
C. Prompt GitHub Copilot to explain what was the role of a suggestion
D. Giving GitHub Copilot multiple examples of the form of the data you want to use

Answer: A

Explanation:
Role prompting involves explicitly stating your role or the persona you want GitHub Copilot to adopt
within your prompt. This helps Copilot provide more contextually relevant and accurate suggestions.
By defining your role (e.g., "As a senior software engineer," "As a technical writer"), you guide
Copilot to tailor its responses to align with the expertise and perspective associated with that role.
This improves the quality and relevance of the generated code and explanations.
Reference: GitHub Copilot documentation on prompt engineering and best practices.

QUESTION 2
Which of the following scenarios best describes the intended use of GitHub Copilot Chat as a tool?

A. A complete replacement for developers generating code.
B. A productivity tool that provides suggestions, but relying on human judgment.
C. A solution for software development, requiring no additional input or oversight.
D. A tool solely designed for debugging and error correction.

Answer: B

Explanation:
GitHub Copilot Chat is designed to be a productivity enhancer, not a replacement for human developers.
It provides suggestions and assists with coding tasks, but the final decision and validation
always rest with the developer. Copilot Chat is meant to augment the developer's workflow, making
it faster and more efficient, but it does not remove the need for human oversight and judgment.
Reference: GitHub Copilot official documentation on the tool's purpose and usage.

QUESTION 3
How long does GitHub retain Copilot data for Business and Enterprise? (Each correct answer presents part of the solution. Choose two.)

A. Prompts and Suggestions: Not retained
B. Prompts and Suggestions: Retained for 28 days
C. User Engagement Data: Kept for Two Years
D. User Engagement Data: Kept for One Year

Answer: B, C

Explanation:
For GitHub Copilot Business and Enterprise, prompts and suggestions are retained for 28 days to
provide context and improve the service. User engagement data, which includes usage patterns and
interactions, is kept for two years. This data retention policy is designed to balance service
improvement with user privacy.
Reference: GitHub Copilot documentation on data privacy and retention policies for Business and Enterprise plans.

QUESTION 4
What types of prompts or code snippets might be flagged by the GitHub Copilot toxicity filter? (Each correct answer presents part of the solution. Choose two.)

A. Hate speech or discriminatory language (e.g., racial slurs, offensive stereotypes)
B. Sexually suggestive or explicit content
C. Code that contains logical errors or produces unexpected results
D. Code comments containing strong opinions or criticisms

Answer: A, B

Explanation:
GitHub Copilot includes a toxicity filter to prevent the generation of harmful or inappropriate
content. This filter flags prompts or code snippets that contain hate speech, discriminatory language,
or sexually suggestive or explicit content. This ensures a safe and respectful coding environment.
Reference: GitHub Copilot documentation on safety and content filtering.

QUESTION 5
What is a benefit of using custom models in GitHub Copilot?

A. Responses are faster to produce and appear sooner
B. Responses use practices and patterns in your repositories
C. Responses use the organization's LLM engine
D. Responses are guaranteed to be correct

Answer: B

Explanation:
Custom models in GitHub Copilot allow the tool to learn from the specific code patterns and
practices within your repositories. This results in suggestions that are more aligned with your
organization's coding standards and conventions, improving the relevance and accuracy of the
generated code.
Reference: GitHub Copilot Enterprise documentation on custom models.

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