How does deterministic noise cause overfitting?

[mic00]

I am still a little confused about this. It's clear to me that reducing deterministic noise can lead to overfitting (if there is not enough in-sample data), but the presence of deterministic noise itself seems (to me) to cause underfitting. Am I just being pedantic?

(I contrast this with stochastic noise: it cannot be reduced, and clearly any attempt to fit it is overfitting, because no amount of in-sample data will clarify its shape).