We show that a class of optimal input design problems have only discrete spectral measures as solutions. If we fix any finite set of possible frequencies then a randomized version of the resulting convex problem has a unique (sparse) solution with probability 1. We also propose a data-driven approach to optimal input design via virtual off-line estimators that coincide with the optimized PE estimator modulo a negligible error, both for open loop and closed loop systems.