SVM - hard or soft margins?

Question

Given a linearly separable dataset, is it necessarily better to use a a hard margin SVM over a soft-margin SVM?

Answer 1

In my opinion, Hard Margin SVM overfits to a particular dataset and thus can not generalize. Even in a linearly separable dataset (as shown in the above diagram), outliers well within the boundaries can influence the margin. Soft Margin SVM has more versatility because we have control over choosing the support vectors by tweaking the C.