Exam Vouchers: GitHub Copilot (GH-300)
- Price: SAR188.00
- Code: GH-300
Description
TopObjectives
TopYou will be assessed on the following:
- Domain 1: Responsible AI 7%
- Domain 2: GitHub Copilot plans and features 31%
- Domain 3: How GitHub Copilot works and handles data 15%
- Domain 4: Prompt crafting and Prompt engineering 9%
- Domain 5: Developer use cases for AI 14%
- Domain 6: Testing with GitHub Copilot 9%
- Domain 7: Privacy fundamentals and context exclusions 15%
Content
TopExplain 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
Pre-requisites
TopIt is recommended that students have attended the following course before attempting the exam:
- GitHub Copilot - GH300