Google Cloud Professional Machine Learning Engineer — Question 118

You are building an ML model to predict trends in the stock market based on a wide range of factors. While exploring the data, you notice that some features have a large range. You want to ensure that the features with the largest magnitude don’t overfit the model. What should you do?

Answer options

• A. Standardize the data by transforming it with a logarithmic function.
• B. Apply a principal component analysis (PCA) to minimize the effect of any particular feature.
• C. Use a binning strategy to replace the magnitude of each feature with the appropriate bin number.
• D. Normalize the data by scaling it to have values between 0 and 1.

Correct answer: D

Explanation

The correct choice is D, as normalizing the data ensures that all features contribute equally to the model, preventing any single feature with a large range from dominating the predictions. Option A is incorrect because logarithmic transformation does not guarantee values between 0 and 1. Option B, while useful for dimensionality reduction, may not specifically address the issue of feature magnitude. Option C, binning, can lose significant information and is not an effective method for this scenario.