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About this interview
A technical interview on Data Preparation and Feature Engineering, pitched at the easy level. A voice AI interviewer leads the conversation, adapts its questions to your answers, keeps you on topic, and afterward gives you honest, specific feedback on where you were strong and where to improve. Expect roughly 30 minutes.
What you'll be assessed on
Enumerate strategies for handling missing values, outliers, and class imbalance
Explain feature scaling, encoding of categorical variables, and when each approach is appropriate
Describe common feature selection techniques and rank their trade-offs
Recognize patterns that indicate data leakage and explain how to prevent it
Topics covered
EDA and data qualityMissing dataOutliersFeature scalingCategorical encodingClass imbalanceFeature selectionData leakageFeature engineeringFeature relationshipsDistribution shift
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“When you first receive a new dataset, what do you typically look at to understand whether it's clean enough to build a model on?
“What is one-hot encoding, and what problem does it create when a categorical feature has a very high number of unique values?
“How would you use feature importances from a random forest to decide which features to drop, and what caveats should you keep in mind when interpreting those importances?