Google Cloud Professional Data Engineer — Question 265

You're training a model to predict housing prices based on an available dataset with real estate properties. Your plan is to train a fully connected neural net, and you've discovered that the dataset contains latitude and longitude of the property. Real estate professionals have told you that the location of the property is highly influential on price, so you'd like to engineer a feature that incorporates this physical dependency.
What should you do?

Answer options

• A. Provide latitude and longitude as input vectors to your neural net.
• B. Create a numeric column from a feature cross of latitude and longitude.
• C. Create a feature cross of latitude and longitude, bucketize it at the minute level and use L1 regularization during optimization.
• D. Create a feature cross of latitude and longitude, bucketize it at the minute level and use L2 regularization during optimization.

Correct answer: C

Explanation

Option C is correct because creating a feature cross of latitude and longitude can capture the interaction between these two variables, and bucketizing at the minute level allows for better granularity in the data. Using L1 regularization helps in feature selection by penalizing less important features, which is beneficial in this context. Options A and B do not effectively capture the interaction between latitude and longitude, while options C and D differ only in the type of regularization applied.