AWS Certified Machine Learning – Specialty — Question 45

Machine Learning Specialist is building a model to predict future employment rates based on a wide range of economic factors. While exploring the data, the
Specialist notices that the magnitude of the input features vary greatly. The Specialist does not want variables with a larger magnitude to dominate the model.
What should the Specialist do to prepare the data for model training?

Answer options

• A. Apply quantile binning to group the data into categorical bins to keep any relationships in the data by replacing the magnitude with distribution.
• B. Apply the Cartesian product transformation to create new combinations of fields that are independent of the magnitude.
• C. Apply normalization to ensure each field will have a mean of 0 and a variance of 1 to remove any significant magnitude.
• D. Apply the orthogonal sparse bigram (OSB) transformation to apply a fixed-size sliding window to generate new features of a similar magnitude.

Correct answer: C

Explanation

The correct choice is C because normalization standardizes the data, ensuring that all features contribute equally to the model by removing bias from different magnitudes. Options A and B do not address the issue of varying magnitudes directly, and option D, while generating new features, does not ensure uniform scale across existing features.