Sublinear time algorithms, particularly deterministic ones, can solve specific problems effectively without examining the entire input. There exist interesting scenarios where these algorithms can outperform traditional methods by leveraging statistical insights or specific problem constraints. Examples such as finding the minimum element under certain conditions showcase the potential of these algorithms to provide useful approximations efficiently. Notably, when dealing with integer inputs, rethinking the input representation can facilitate sublinear solutions by reducing the complexity significantly, thus benefiting from logarithmic characteristics.