The paper provides information on the need to pass the “Unified entrance exam” in a foreign (English) language by students who have received a bachelor’s degree and wish to continue their studies to obtain a master’s degree. It is determined that when working with undergraduate students, it is advisable, firstly, to determine the percentage of graduates whose passing EVE is unlikely, and secondly, to intensify work with such graduates to increase this probability. The task was set to create a model for predicting the results of the unified entrance exam in a foreign language by bachelor’s graduates of higher education institutions upon entering the master’s program. A number of factors that affect the EVE score are proposed, namely: competitive score at enrollment (indicator of the student’s basic level), rating (assessment) based on the results of the first year of study (exam in the compulsory subject “Foreign Language”), choice “Foreign language” in the 2-3rd year (maximum of all or “0”, if the student did not choose), the rating of additional classes “Foreign language” in the 4th year, the average rating for the penultimate session (indicator “current” student’s attitude to the educational process), the fact of having additional points (an indicator of the student’s interest in other activities than learning), the average rating of a bachelor’s degree (an indicator of the general student’s attitude to the educational process). The available data concerning students of two years of the department of intelligent decision-making systems of the Donbas State Engineering Academy are given. A method of artificial neural networks with a two-layer perceptron architecture with ten neurons in each hidden layer, a sigmoid activation function, and an error backpropagation algorithm for network training is proposed. Calculations were performed in the Deductor Studio Lite environment, their results were analyzed. It is noted that the proposed approach to forecasting can be applied when working with undergraduate students, to determine the percentage of graduates whose EVI is unlikely to pass, and to intensify work with such graduates to increase this probability. Keywords: educational and qualification level, the only entrance exam, forecasting, artificial neural network, perceptron, sigmoid, network training.