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About this interview
A technical interview on Feature Engineering & Data Handling, pitched at the medium 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
Describe strategies for handling missing data, outliers, and skewed distributions
Explain techniques for addressing class imbalance such as oversampling, undersampling, and class weights
Articulate feature selection methods including filter, wrapper, and embedded approaches
Identify data distribution shift and its impact on model performance in production
Topics covered
Data Quality AssessmentMissing DataData LeakageSkewed DistributionsFeature ScalingOutlier HandlingCategorical EncodingClass ImbalanceFeature SelectionDistribution ShiftFeature ConstructionDimensionality
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“When you receive a new dataset before any modeling, what are the first things you look for to understand its quality?
“What does class imbalance mean in a classification problem, and why does it make accuracy a misleading metric?
“What corrective actions would you take when you confirm that covariate shift is degrading your model's performance in production?