GH-300 Sample Questions Answers

GitHub Copilot primarily impacts the software development process by increasing productivity through automating repetitive coding tasks.

Exact extracts:

“Copilot requires an active internet connection to provide suggestions.”

“Copilot does not support all programming languages.”

“Copilot can assist in the design phase by suggesting design patterns, frameworks, and best practices relevant to the context of your project.”

This shows Copilot contributes by offering design-related recommendations, not by independently producing full designs or managing collaboration.

The quality of Copilot's suggestions is enhanced by clearly defining the task, using meaningful variable names, and providing examples of the desired output.

“If excluded content is referenced by non-excluded code, such as through function calls, Copilot may still use that reference context.”

This confirms that excluded content can indirectly appear if referenced elsewhere.

“Content exclusions are available only with GitHub Copilot Business and Enterprise. Copilot Individual users cannot configure exclusions.”

This confirms that the only way for the team to achieve exclusions is by upgrading to Copilot Business.

GitHub Copilot Chat can suggest assertions based on the code's context and semantics to help developers verify the correctness of their functions. These assertions serve as checks that the function behaves as expected under various conditions.

Exact extracts:

“Copilot Chat can generate documentation based on your code.”

Gen AI tools can produce inaccurate outputs if the training data contains biases or inconsistencies, which can lead to flawed or misleading results.

GitHub Copilot Chat is effective for explaining and translating legacy code and generating unit test scenarios for new code.

“GitHub Copilot generates suggestions based on context from the IDE, including the file you are editing, open tabs, cursor position, and any selected code.”

This proves that the context is drawn from editor state information, making option A correct.

Exact extracts:

“Copilot applies post-processing checks for code quality to ensure that responses meet baseline standards.”

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

“Securing the code used with generative AI prevents unauthorized access, helps safeguard sensitive data, and protects against potential breaches.”

This confirms option A is correct because the primary importance lies in avoiding unauthorized access and breaches.

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.

Exact extracts:

“The Copilot usage metrics API allows organizations to track the number of suggestions shown and accepted by members.”

“GitHub Copilot for Azure DevOps is available to Azure DevOps organizations without requiring a GitHub Enterprise license.”

This confirms that Copilot for Azure DevOps is the correct plan for such organizations.

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.

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

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

“GitHub Copilot Knowledge Bases allow you to ask questions about your organization’s documentation, design patterns, and code snippets.”

This proves that Copilot Knowledge Base can work with code snippets, documentation, and design patterns, but not binaries or screenshots.

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,Bis 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.

“Because GitHub Copilot is trained on a large amount of publicly available code, it often suggests snippets that reflect common coding practices and idioms.”

This shows that Copilot reflects the most common practices it has seen in its training data.

“/fix: Propose changes to resolve detected issues, such as syntax errors or common programming mistakes, in the selected code.”

This confirms that the /fix command is used to suggest corrections for identified problems.

“Few-shot prompting is a technique where you provide examples of the mechanism or output you want the model to follow. These examples guide the AI’s responses.”

This proves that option A is correct.

“The accuracy of Copilot’s test generation depends heavily on the available context, including existing code, file structure, and open selections.”

This confirms that providing the correct context is the most important factor.

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 proxy service for GitHub Copilot is hosted on Microsoft Azure.

“Audit logs in Copilot Business allow enterprise and organization administrators to monitor administrator activities and configuration changes.”

This proves that their purpose is not line tracking or license assignment but monitoring admin activities.

The /tests slash command in GitHub Copilot Chat creates unit tests for the selected code, helping developers ensure the functionality and reliability of their 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.

GitHub Copilot Enterprise provides access to your company's knowledge bases, enabling the tool to provide contextually relevant suggestions based on your organization's specific documentation and code.

“While Copilot can suggest refactoring changes, the code may not always follow best practices or be fully optimized. Developers must review and validate suggestions.”

This confirms that limitations exist in optimization and best practices, making option A correct.