Question 1

Your company has recently begun using GitHub Copilot with a custom AI model to help streamline the development of proprietary software. You are tasked with explaining the advantages of using a custom model in this context. Which of the following benefits best illustrates the value of utilizing custom models within GitHub Copilot?

Options :

A : Custom models can be trained to include company-specific code patterns and best practices, improving the relevance of Copilot-s suggestions.

B : Custom models are optimized to generate documentation for code automatically without developer input.

C : Custom models ensure that no external open-source code is suggested during development, avoiding any licensing issues.

D : Custom models eliminate the need for reviewing code since they only generate perfectly optimized code.

Answer: A

Question 2

Your team is working on modernizing a legacy application written in COBOL by migrating its functionality to a modern cloud-native architecture in Python. You want to use GitHub Copilot to assist in this process. Which of the following is the best use of GitHub Copilot in migrating the legacy application to a modern architecture?

Options :

A : Use GitHub Copilot to convert low-level system commands from COBOL into equivalent high-level Python code without needing additional testing.

B : Allow GitHub Copilot to rewrite all the application’s business logic in Python, assuming that all functionality will work exactly the same.

C : Use GitHub Copilot to automatically convert the COBOL code to Python without human involvement, since AI can handle cross-language migration.

D : Ask GitHub Copilot to suggest Python code snippets that achieve the same functionality as specific COBOL code, then verify the functionality through testing and manual review.

Answer: D

Question 3

You are a developer working on a Python project, and you want to improve your unit test coverage using GitHub Copilot. You have implemented a function called calculate_discount(price, discount_rate) which calculates the discount for a given product price. The function works correctly, but you haven't written any tests for it yet. You decide to use GitHub Copilot to generate test cases for this function in a separate test file. Which of the following test case suggestions generated by GitHub Copilot is the most appropriate for testing the calculate_discount function under various conditions?

def calculate_discount(price, discount_rate):

return price - (price * discount_rate)

Options :

A :

def test_calculate_discount():
assert calculate_discount("100", 0.1) == "90"
assert calculate_discount(100, "0.1") == 90

B :

def test_calculate_discount():
assert calculate_discount(100, 2.0) == -100
assert calculate_discount(100, 0.2) == 20

C :

def test_calculate_discount():
assert calculate_discount(100, 0.1) == 90
assert calculate_discount(200, 0.2) == 160
assert calculate_discount(50, 0) == 50
assert calculate_discount(0, 0.1) == 0
assert calculate_discount(-100, 0.1) == -90

D :

def test_calculate_discount():
assert calculate_discount(100, 0.5) == 50
assert calculate_discount(0, 0.1) == 0
assert calculate_discount(100, 0.2) == 80

Answer: C

Question 4

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?

Options :

A : Assume that bias testing is unnecessary because Copilot is an AI tool, and AI systems are generally neutral and free from bias.

B : Ask Copilot to specifically avoid gender, race, and age attributes in its suggestions, which should eliminate any potential bias.

C : Conduct thorough bias testing on the Copilot-generated code to ensure that the system does not discriminate against any particular group.

D : Rely on Copilot’s large and diverse training set, assuming it has been trained on unbiased data sources, and trust its code suggestions.

Answer: C

Question 5

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

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