We consider the Adaptive Regularization with Cubics approach for solving nonconvex optimization problems and propose a new variant based on inexact Hessian information chosen dynamically. The theoretical analysis of the proposed procedure is given. The key property of ARC framework, constituted by optimal worst-case function/derivative evaluation bounds for first- and second-order critical point, is guaranteed. Application to largescale finite-sum minimization based on subsampled Hessian is discussed and analyzed in both a deterministic andmprobabilistic manner and equipped with numerical experiments on synthetic and real datasets.