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GitHub CopilotCertification Exam Sample Questions (Q52-Q57):
NEW QUESTION # 52
Which principle emphasizes that AI systems should be understandable and provide clear information on how they work?
Answer: B
Explanation:
The principle of transparency emphasizes that AI systems should be understandable and provide clear information about their operations. This ensures that users can understand how the AI arrivesat its decisions and suggestions.
NEW QUESTION # 53
Which of the following prompts can be used to guide GitHub Copilot Chat in refactoring code for quality improvements? (Each correct answer presents part of the solution. Choose two.)
Answer: A,D
Explanation:
Effective prompts for refactoring include requests that focus on specific quality improvements, such as readability and maintainability. These prompts guide GitHub Copilot to provide relevant and actionable suggestions.
NEW QUESTION # 54
How can GitHub Copilot assist developers during the requirements analysis phase of the Software Development Life Cycle (SDLC)?
Answer: B
Explanation:
GitHub Copilot can assist during the requirements analysis phase by providing templates and code snippets that aid in documenting requirements. This helps streamline the process of capturing and organizing project requirements.
NEW QUESTION # 55
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.)
Answer: B,D
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.
NEW QUESTION # 56
In what way can GitHub Copilot and GitHub Copilot Chat aid developers in modernizing applications?
Answer: D
Explanation:
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 For example, it can recommend updates to outdated constructs, propose more efficient algorithms, or suggest frameworks and libraries that are widely used in modern application development.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.
NEW QUESTION # 57
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