A hospital wants to predict patient outcomes for the coming year An ML engineer must...

Linear regression model whose coefficients should shrink but not become zero

Answer: L2 regularization

AWS says L2 produces smaller overall weight values and is the right fit when coefficients should be reduced without being forced to zero.

Polynomial regression model with irrelevant polynomial terms that should be eliminated

Answer: L1 regularization

AWS says L1 reduces the number of features used by pushing small weights to zero, which matches elimination of irrelevant terms.

Logistic regression model that has highly correlated features to eliminate highly redundant predictors

Answer: L1 regularization

This is the nuanced one. AWS says L2 stabilizes weights when there is high correlation between features, but because the question explicitly says eliminate highly redundant predictors, L1 is the better match since it creates sparsity and removes predictors by zeroing coefficients.