Question 1

You are working on a large project and notice that GitHub Copilot’s suggestions sometimes seem irrelevant to the code you are working on, especially when dealing with files with many lines of code. You want to understand why this is happening and how GitHub Copilot handles large files. Which of the following statements best describes the limitations of GitHub Copilot in relation to limited context windows?

Options :

A : GitHub Copilot can analyze and understand the entire project, no matter how large the files are.

B : GitHub Copilot has no issues with context windows, as it continuously learns from the entire codebase as you write more code.

C : GitHub Copilot can only take into account a limited number of lines of code around the cursor due to the limited context window.

D : GitHub Copilot processes all lines of code in a file, but prioritizes recent changes near the cursor.

Answer: C

Question 2

You are working on a project to build a loan approval system for a bank using GitHub Copilot to speed up development. The system will be used to make important financial decisions, and your company is focused on ensuring Responsible AI usage. What steps should you take to ensure transparency and accountability when using Copilot for this project?

Options :

A : Allow Copilot to suggest code but ensure all suggestions are logged and reviewed during code audits to maintain transparency and ensure accountability.

B : Document every decision Copilot makes during code generation and flag any code that relates to sensitive areas like loan eligibility criteria.

C : Since Copilot-generated code is likely compliant with industry standards, you don’t need to perform additional logging or auditing of AI-generated code.

D : Rely on Copilot’s code suggestions without documenting how the generated code was used, as the system’s focus is on speeding up development.

Answer: A

Question 3

You are a software developer at a financial institution, and you're using GitHub Copilot to assist in writing a program that handles sensitive user data such as social security numbers (SSNs), bank account information, and transaction histories. Your team is concerned about responsible AI usage and the potential ethical and legal ramifications of relying on AI-generated code. Which of the following actions demonstrates responsible usage of GitHub Copilot in this scenario?

Options :

A : Using Copilot to generate code that handles sensitive data directly, without reviewing it, as Copilot is trained on public code bases and assumed to be reliable.

B : Relying entirely on Copilot for implementing encryption algorithms for sensitive data handling, as Copilot is built on a large code base and can suggest the most optimized algorithms.

C : Allowing Copilot to suggest code for all areas of the project, including sensitive data handling, without consulting your organization-s legal or compliance teams.

D : Using Copilot to generate code, but reviewing all AI-generated code, especially sections dealing with sensitive data handling and encryption, before using it in production.

Answer: D

Question 4

A team is tasked with modernizing a legacy application written in an outdated programming language with many inefficiencies. They plan to refactor portions of the application to make it more efficient and maintainable. The team decides to use GitHub Copilot to assist in the refactoring process. Which of the following strategies would best utilize GitHub Copilot to modernize the legacy codebase?

Options :

A : Use GitHub Copilot to generate documentation for the legacy codebase but avoid using it for code refactoring, since AI cannot understand the legacy code-s logic.

B : Use GitHub Copilot to suggest modern alternatives for outdated or inefficient code patterns, and then manually review and test the suggestions.

C : Depend on GitHub Copilot to detect and refactor performance bottlenecks without requiring further testing or validation.

D : Rely on GitHub Copilot to automatically rewrite the entire legacy codebase in a modern programming language.

Answer: B

Question 5

You are using GitHub Copilot in your Integrated Development Environment (IDE) while writing code in Python. As you type, Copilot suggests a function implementation based on your comments and code context. However, you are concerned about how your data and code are handled by GitHub Copilot during this process. Consider how GitHub Copilot accesses and processes the data to provide the code suggestions. Which of the following statements is correct about how GitHub Copilot handles your data and provides code suggestions?

Options :

A : Copilot does not store any user data, but it sends your entire project code to its servers for analysis every time you make a change.

B : The Copilot model does not use any user data or external data to provide code suggestions and relies only on locally cached files.

C : GitHub Copilot sends all your code directly to the cloud where it is stored for future use by the Copilot model.

D : Copilot generates suggestions based on both the context of your code in the editor and a large language model trained on public code repositories, without storing your code data.

Answer: D

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