We investigate the geometry of the maximal a posteriori (MAP) partition in the Bayesian Mixture Model where the component and the base distributions are chosen from conjugate exponential families. We prove that in this case the clusters are separated by the contour lines of a linear functional of the sufficient statistic. As a particular example, we describe Bayesian Mixture of Normals with Normal-inverse-Wishart prior on the component mean and covariance, in which the clusters in any MAP partition are separated by a quadratic surface. In connection with results of Rajkowski (2018), where the linear separability of clusters in the Bayesian Mixture Model with a fixed component covariance matrix was proved, it gives a nice Bayesian analogue of the geometric properties of Fisher Discriminant Analysis (LDA and QDA).