  Up to date, only lower and upper bounds for the optimal configuration of a
Square Array (A2) Group Testing (GT) algorithm are known. We establish exact
analytical formulae and provide a couple of applications of our result. First,
we compare the A2 GT scheme to several other classical GT schemes in terms of
the gain per specimen attained at optimal configuration. Second, operating
under objective Bayesian framework with the loss designed to attain minimum at
optimal GT configuration, we suggest the preferred choice of the group size
under natural minimal assumptions: the prior information regarding the
prevalence suggests that grouping and application of A2 is better than
individual testing. The same suggestion is provided for the Minimax strategy.
