A printed circuit board inspection apparatus comprising: a memory configured to store a machine-learning based model that derives at least one value indicating relevance between at least one fault type of a screen printer and an image indicating whether an anomaly associated with a plurality of solder pastes printed on a printed circuit board by the screen printer is detected; and a processor electrically connected with the memory, wherein the processor is configured to: generate at least one image indicating whether an anomaly associated with a plurality of solder pastes printed on a first printed circuit board by the screen printer is detected, if an anomaly in at least one solder paste of the plurality of solder pastes is detected by using an image of the first printed circuit board, obtain at least one value indicating relevance between at least one fault type of the screen printer and the generated at least one image, using the machine-learning based model, and determine a fault type, associated with the at least one solder paste in which the anomaly is detected, of the screen printer, from the at least one fault type of the screen printer, based on the obtained at least one value wherein the at least one image distinguishes between at least one first solder paste in which the anomaly is detected and at least one second solder paste in which the anomaly is not detected. a memory configured to store a machine-learning based model that derives at least one value indicating relevance between at least one fault type of a screen printer and an image indicating whether an anomaly associated with a plurality of solder pastes printed on a printed circuit board by the screen printer is detected; and a processor electrically connected with the memory, wherein the processor is configured to: generate at least one image indicating whether an anomaly associated with a plurality of solder pastes printed on a first printed circuit board by the screen printer is detected, if an anomaly in at least one solder paste of the plurality of solder pastes is detected by using an image of the first printed circuit board, obtain at least one value indicating relevance between at least one fault type of the screen printer and the generated at least one image, using the machine-learning based model, and determine a fault type, associated with the at least one solder paste in which the anomaly is detected, of the screen printer, from the at least one fault type of the screen printer, based on the obtained at least one value wherein the at least one image distinguishes between at least one first solder paste in which the anomaly is detected and at least one second solder paste in which the anomaly is not detected. generate at least one image indicating whether an anomaly associated with a plurality of solder pastes printed on a first printed circuit board by the screen printer is detected, if an anomaly in at least one solder paste of the plurality of solder pastes is detected by using an image of the first printed circuit board, obtain at least one value indicating relevance between at least one fault type of the screen printer and the generated at least one image, using the machine-learning based model, and determine a fault type, associated with the at least one solder paste in which the anomaly is detected, of the screen printer, from the at least one fault type of the screen printer, based on the obtained at least one value wherein the at least one image distinguishes between at least one first solder paste in which the anomaly is detected and at least one second solder paste in which the anomaly is not detected.