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Advanced feature engineering: temporal and nested-CV leakage, train–serve skew, entity embeddings, automated interactions, monotonic constraints, drift monitoring, null importance, and causal vs predictive features. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.
Intermediate feature engineering: encoding high-cardinality categories, binning, interactions, point-in-time aggregates, cross-validated encoders, multicollinearity, and feature selection trade-offs. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.
Foundations of feature engineering: raw inputs vs model features, numeric and categorical handling, basic encoding, scaling, missing values, and avoiding obvious leakage. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.
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