Free GitHub-Copilot Mock Exam – Practice Online Confidently

As a senior developer, you’ve been tasked with improving workflow efficiency for your team. The team uses a mix of editors and IDEs, including VS Code, but there’s also a strong preference for using the command line. You suggest integrating GitHub Copilot directly into the team's workflow via the CLI to help with code suggestions, file generation, and automation tasks. One team member asks if this integration is possible and what the setup might involve. Which of the following accurately describes how GitHub Copilot can be used in the CLI?

You are developing an AI-based recruiting system and are using GitHub Copilot to help write code that filters job applicants based on their qualifications. Given that Copilot’s training data might contain historical biases (e.g., gender, race), how can you ensure that the code it generates does not inadvertently introduce bias into the system?

You are working on a small web application with a User model that contains the following method:

class User:

    def __init__(self, username, age):

        self.username = username

        self.age = age

    def is_adult(self):

        return self.age >= 18

You want to use GitHub Copilot to generate boilerplate integration tests to verify that the is_adult() method works correctly when integrated with other parts of the web application, such as a view that restricts certain content to adults. What is the best strategy to generate these integration tests using GitHub Copilot?

You are using GitHub Copilot to write code for a machine learning pipeline in Python. While Copilot can suggest basic functions and code snippets, you find that it often fails to understand the broader logic and business rules that are crucial to your project. You are aware that Copilot is based on a large language model (LLM), and you begin to consider the inherent limitations of such models when it comes to producing context-aware suggestions for complex projects. Which of the following statements accurately describes the limitations of GitHub Copilot and large language models in general?

You are building a predictive model to classify customer behavior based on transactional data. Your dataset includes raw transactional details, such as amounts, timestamps, and customer IDs, but no features that directly help with classification. You want to perform feature engineering to create new, meaningful features from the raw data. Your team is using GitHub Copilot to accelerate this process. Which of the following best demonstrates how GitHub Copilot can be used effectively for feature engineering in this scenario?