Google Cloud Certified Professional Machine Learning Engineer Practice Mode

Community Discussion

Written by other learners. Comments can disagree with the marked answer — check the official documentation when in doubt.

• Chose C2

  Captures City-Specific Relationships: Crossing binned latitude and longitude creates a unique identifier for a specific geographic area or "city block". Crossing this with the one-hot encoded car type allows the model to learn sales patterns that are specific to each car type within those defined city areas. Binning Handles Continuous Data: Binning (bucketizing) latitude and longitude converts these continuous values into discrete, categorical features, which are then suitable for feature crosses. Binning also enables the model to learn non-linear relationships within a single feature. Handles Non-Linearities and Interactions: Feature crosses, like this element-wise product, allow linear models to capture non-linear relationships and interactions between features, providing more predictive power than individual features alone

• Chose D1

  Two feature crosses as an 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. 🔍 Why? This approach allows the model to learn city-specific relationships between car types and sales by: Binning latitude and longitude: Groups nearby locations into discrete regions (e.g., cities or metro areas). One-hot encoding car types: Makes each car type a distinct feature. Crossing location bins with car types: Captures how preferences for car types vary by geographic region. By creating two separate feature crosses (latitude × car type and longitude × car type), the model can learn nuanced patterns without overcomplicating the feature space.

• Chose C1

  C is the closest here. The REAL answer would be feature cross only Binned Latitude and Binned Longitude to get a feature that represents a city/location/sales area. There is no real need to also add the model in the feature cross. But given the choices C is the best fit.