Google Cloud Professional Data Engineer — Question 123

You work for a bank. You have a labelled dataset that contains information on already granted loan application and whether these applications have been defaulted. You have been asked to train a model to predict default rates for credit applicants.
What should you do?

Answer options

• A. Increase the size of the dataset by collecting additional data.
• B. Train a linear regression to predict a credit default risk score.
• C. Remove the bias from the data and collect applications that have been declined loans.
• D. Match loan applicants with their social profiles to enable feature engineering.

Correct answer: B

Explanation

The correct answer is B because training a linear regression model is appropriate for predicting continuous outcomes like credit default risk scores. Option A may improve the dataset but does not directly contribute to model training. Option C focuses on bias removal and declined applications, which are not relevant for the task at hand. Option D, while interesting for feature engineering, does not directly address the prediction of default rates.