The correct answer is False.

R squared is defined as one minus the sum of squared errors divided by the total sum of squares and it compares model error to the error of predicting the mean. If the model's predictions are worse than always predicting the mean then the sum of squared errors can be larger than the total sum of squares. That yields one minus SSE over SST that is negative so R squared can be negative.

Negative values often arise when you compute R squared on new data and the model generalizes poorly or when the regression omits an intercept or when you use a constrained or non least squares fitting method. By contrast, when you fit ordinary least squares with an intercept on the same data used for training, the intercept only model is a feasible baseline and OLS minimizes SSE so R squared will lie between zero and one in that special situation.

True is incorrect because it asserts that R squared must always be zero or greater. That statement is false for the general definitions and common evaluation scenarios, so the correct choice is False.

Full AWS Practitioner Certification Question