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Databricks Certified Machine Learning Associate · Databricks
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- Correct answer : A — It creates new synthetic rows by interpolating between a minority-class sample and one of its nearest minority neighbors. A . SMOTE generates a new point along the line segment between a minority-class sample and one of its k-nearest minority-class neighbors, producing genuinely new feature values rather than exact copies. This interpolation is the defining mechanism of the technique. B . Verbatim duplication describes random oversampling, not SMOTE; SMOTE is specifically designed to avoid exact duplicates by synthesizing new feature vectors instead of repeating rows. C . Removing majority-class rows near the boundary describes an undersampling strategy such as Tomek links or edited nearest neighbors, not SMOTE, which only adds minority-class rows. D . Adjusting the loss function without changing the dataset describes class weighting, a separate mitigation technique that leaves the row count untouched, unlike SMOTE.