GH-300 GitHub Copilot Exam Questions and Answers

Fairness in AI tools is achieved by training the data and algorithms to be free from biases. This ensures that the tool treats all users equitably and avoids discriminatory outcomes.

GitHub Copilot and GitHub Copilot Chat are powerful AI-driven tools designed to assist developers by providing context-aware code suggestions and interactive support. Specifically, in the context of modernizing applications, GitHub Copilot excels at analyzing existing code and suggesting modern programming patterns, best practices, and syntax improvements that align with contemporary development standards. For example, it can recommend updates to outdated constructs, propose more efficient algorithms, or suggest frameworks and libraries that are widely used in modern application development.

Why not A? GitHub Copilot does not "directly convert" legacy applications into cloud-native architectures. It can assist by suggesting code changes or patterns that support such a transition, but it doesn’t autonomously perform the full conversion process, which involves architectural decisions and deployment steps beyond its scope.

Why not C? While GitHub Copilot can generate code snippets and even larger portions of an application, it cannot create and deploy full-stack applications from a single query. It requires developer input, refinement, and integration to achieve a complete, deployable solution.

Why not D? GitHub Copilot can assist with refactoring by suggesting improvements to existing code, but it doesn’t inherently "align with upcoming standards" in a predictive sense. Its suggestions are based on current best practices and the data it was trained on, not future standards that are yet to be defined.

Thus, B is the most accurate and realistic way GitHub Copilot aids developers in modernizing applications, leveraging its ability to provide relevant, context-based suggestions to update and improve codebases.

Content exclusions typically take up to 30 minutes to be added or updated.

GitHub Copilot Chat helps in understanding existing codebases by answering questions about the code and generating explanations. This allows developers to quickly grasp the functionality and structure of unfamiliar code.

GitHub Copilot can analyze existing code and suggest refactoring improvements to enhance code quality, readability, and maintainability. It can propose changes to improve code structure, reduce complexity, and follow best practices.

Code review is necessary because GitHub Copilot's generated test cases might not cover all possible scenarios, especially edge cases and complex interactions.

GitHub Copilot Individual is billed as a monthly or annual subscription.

To help protect against IP infringement, you need to configure GitHub Copilot to block suggestions that match public code. This ensures that the generated code is not directly copied from publicly available sources.

The correct way to exclude files is through the GitHub Copilot settings in the user interface, which allows administrators to specify files and directories to be ignored.

Chat history provides valuable context to GitHub Copilot, helping it generate more relevant and accurate code suggestions based on previous interactions and conversations.

Audit log events for GitHub Copilot Business can be accessed through the Audit log section within the organization's GitHub settings. This log provides a record of activities related to Copilot usage and configuration.

The duplication detection filter can be configured at the organization level to prevent GitHub Copilot from suggesting publicly available code snippets.

GitHub Copilot Enterprise is the most cost-effective plan for managing users with an Identity Provider like Okta, as it provides enterprise-level features and integration.

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.

Improving the context for GitHub Copilot involves opening relevant files in your IDE to provide immediate context and adding relevant code snippets directly to your prompts to give Copilot specific examples and information.

GitHub Copilot Knowledge Bases is an Enterprise feature that allows the tool to use an organization's internal documentation and code for more accurate and relevant suggestions.