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PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #73
PROFESSIONAL-MACHINE-LEARNING-ENGINEER Question #73: Real Exam Question with Answer & Explanation
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Submitted by priya_blr· Apr 18, 2026Data processing and feature engineering
Question
While conducting an exploratory analysis of a dataset, you discover that categorical feature A has substantial predictive power, but it is sometimes missing. What should you do?
Options
- ADrop feature A if more than 15% of values are missing. Otherwise, use feature A as-is.
- BCompute the mode of feature A and then use it to replace the missing values in feature A.
- CReplace the missing values with the values of the feature with the highest Pearson correlation
- DAdd an additional class to categorical feature A for missing values. Create a new binary feature
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Topics
#Missing data handling#Categorical features#Feature engineering#Data imputation