DataX Practice Questions - CompTIA DataX (DY0-001) Exam

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A data scientist observes that a gradient boosting model achieves 98% accuracy on training data but only 72% on held-out test data. Which regularization approach MOST directly addresses this overfitting?

Reducing the number of estimators, decreasing max tree depth, and increasing the subsample ratio and min_samples_leaf Increasing the learning rate to allow the model to converge faster on training data Adding more features from the training dataset to improve model expressiveness Switching to a simpler linear regression model to eliminate all overfitting

Correct Answer:

Reducing the number of estimators, decreasing max tree depth, and increasing the subsample ratio and min_samples_leaf

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