The problem of mastery decisions and optimizing cutoff scores on criterion-referenced tests is considered. This problem can be formalized as an (empirical) Bayes problem with decisions rules of a monotone shape. Next, the derivation of optimal cutoff scores for threshold, linear, and normal ogive loss functions is addressed, alternately using such psychometric models as the classical model, the beta-binomial, and the bivariate normal model. One important distinction made is between decisions with an internal and an external criterion. A natural solution to the problem of reliability and validity analysis of mastery decisions is to analyze with a standardization of the Bayes risk (coefficient delta). It is indicated how this analysis proceeds and how, in a number of cases, it leads to coefficients already known from classical test theory. Finally, some new lines of research are suggested along with other aspects of criterion-referenced testing that can be approached from a decision-theoretic point of view.