Top 50 data preprocessing Interview Questions
The most frequently asked data preprocessing questions across all roles and companies, ranked by real interview frequency. Updated daily.
Impute, normalize, and one-hot encode raw Randstad Digital Belgium model features in deterministic order.
Randstad Digital BelgiumImpute missing values and cap numeric outliers using medians, IQR bounds, and categorical modes.
Product & DesignImplement column-wise min-max and z-score feature scaling from scratch for Wadhwani Institute model inputs.
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Explain how to engineer features for high-dimensional sparse data while controlling overfitting, dimensionality, and training cost.
Linktree
Truveta
HP SCDSExplain a practical preprocessing strategy for missing values and outliers before training a supervised learning model.
Publicis Production
PaytmAAutoZoneClean noisy time-stamped sensor data by handling missing values, outliers, drift, and derived features before model training.
MICHELIN Connected Fleet
Rivian
Signature ScienceDesign leakage-safe feature engineering and validation for a predictive churn model.
Accenture
Roche
Amazon AdvertisingCompare normalized character signatures to determine whether each pair of strings is an anagram.
AdobeClean Salesforce lead data with CTEs, LEFT JOINs, CASE, and COALESCE to standardize missing values for analysis.
SalesforceNormalize mixed datetime values and flag missing vehicle, mileage, and timestamp fields in Toyota event data.
ToyotaTests ownership and communication in an ML project with messy data, preprocessing ambiguity, and class imbalance trade-offs.
Expedia Group
Cambia Health Solutions
Daimler Trucks North America