Question 1

GitHub Copilot uses machine learning to provide code suggestions based on context within a code editor. Which of the following best describes how Copilot generates these suggestions while handling user data?

Options :

A : It sends portions of user code to a cloud-based model where the code is processed, and suggestions are returned. GitHub Copilot does not store this code data long-term.

B : It runs a local model on the user-s device, generating suggestions by processing the codebase within the local file system only.

C : It stores all user code and interactions indefinitely in the cloud, making it accessible to improve its machine learning model and generate future suggestions.

D : It uses a static model that generates suggestions solely based on hardcoded rules without any internet connectivity or data exchange with GitHub-s servers.

Answer: A

Question 2

Your development team is working on a project to modernize a legacy application. The team is following an Agile methodology with two-week sprints. GitHub Copilot has been integrated into the development workflow to assist with various stages of the SDLC, including planning, coding, testing, and deployment. How can GitHub Copilot best assist your team in managing the Software Development Lifecycle (SDLC)?

Options :

A : GitHub Copilot can help improve code quality by generating code that adheres to common standards and practices during the coding phase, reducing technical debt over time.

B : GitHub Copilot can automatically generate feature specifications and user stories for your Agile sprints based on previous projects in your repository.

C : GitHub Copilot can handle project management tasks such as assigning issues, tracking progress, and managing deployment pipelines.

D : GitHub Copilot can assist in writing test cases, ensuring complete coverage of all functionality, and automatically fixing issues that arise during testing.

Answer: A

Question 3

You are developing an e-commerce web application using GitHub Copilot and need help writing a complex function to calculate shipping costs based on multiple factors like weight, distance, and delivery speed. You first use a prompt that says, “write a function to calculate shipping costs,” but the result is overly simplistic and doesn’t account for all the necessary factors. You realize that prompt engineering is necessary to get a more sophisticated code suggestion. Which of the following prompt strategies is most likely to help you generate a comprehensive shipping cost calculation function with Copilot?

Options :

A : Writing a detailed prompt that breaks down each factor (e.g., weight, distance, speed) along with example data and expected output formats.

B : Providing an incomplete function for calculating shipping costs and letting Copilot fill in the missing logic based on its default understanding of e-commerce applications.

C : Providing a broad prompt with the expectation that Copilot will offer several options, from which you can choose and modify the most relevant suggestion.

D : Using a vague prompt and letting Copilot’s built-in machine learning capabilities handle the rest, trusting it to generate a sophisticated solution.

Answer: A

Question 4

You are working on an application that uses machine learning to classify images. You want GitHub Copilot to generate a Python function using TensorFlow that loads a dataset, builds a simple convolutional neural network (CNN), trains the model, and evaluates its accuracy. Your first attempt at a prompt was: “Write a Python function to classify images using TensorFlow.” However, the solution Copilot provided lacked key elements like dataset loading and training loops. Which of the following refined prompts best applies the fundamentals of prompt engineering to guide Copilot in generating a complete solution?

Options :

A : "Write a Python function using TensorFlow to load a dataset, build a CNN, train it, and evaluate its accuracy."

B : "Write a Python function to classify images using TensorFlow and create a CNN."

C : "Generate a Python function to classify images using TensorFlow and neural networks."

D : "Write a TensorFlow Python function to classify images using a CNN, handling training and testing."

Answer: A

Question 5

You are working on a Python project in your integrated development environment (IDE), and you activate GitHub Copilot to assist you with code suggestions. As you begin typing, Copilot provides a series of code snippets. You want to understand how GitHub Copilot generates these suggestions and handles data at each step. Which of the following best describes the lifecycle of how GitHub Copilot processes your input and provides a code suggestion?

Options :

A : GitHub Copilot retrieves the code from a pre-trained local database on your machine, matches it to similar code snippets from your previous projects, and presents suggestions based on your coding style.

B : GitHub Copilot transmits your code to an AI model hosted in the cloud, which analyzes both your code and GitHub repositories you’ve starred, presenting suggestions tailored to these repositories.

C : GitHub Copilot sends your code and surrounding context to a large language model hosted on a remote server, which processes it through machine learning models trained on public GitHub repositories and other sources, returning contextually relevant code suggestions.

D : GitHub Copilot sends your code to a remote server, which compares it with existing open-source repositories hosted on GitHub, and generates suggestions by copy-pasting code from the most popular projects.

Answer: C

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