Which features or feature crosses should you use to train city-specific relationships between car type and number of sales?

You are an ML engineer at a global car manufacturer. You need to build an ML model to predict car sales in different cities around the world.

Which features or feature crosses should you use to train city-specific relationships between car type and number of sales?
A . Three individual features binned latitude, binned longitude, and one-hot encoded car type
B . One feature obtained as an element-wise product between latitude, longitude, and car type
C . One feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type
D . Two feature crosses as a element-wise product the first between binned latitude and one-hot encoded car type, and the second between binned longitude and one-hot encoded car type

Answer: C

Explanation:

https://developers.google.com/machine-learning/crash-course/feature-crosses/check-your-understanding

https://developers.google.com/machine-learning/crash-course/feature-crosses/video-lecture

https://developers.google.com/machine-learning/crash-course/feature-crosses/check-your-understanding