Accurate classification of echocardiographic views (e.g. apical 2- and 4-chamber, parasternal long-axis) is an important prerequisite for reliable ejection-fraction assessment and heart failure diagnosis, but manual classification is time-consuming. This study evaluates the EchoJEPA foundation model, pretrained on more than 18 million ultrasound recordings, with modified output layers for automatic echocardiographic view classification, providing a baseline component for a larger automated pipeline for HF diagnosis from echocardiography video/image data. This work was presented at the 5th Serbian International Conference on Applied Artificial Intelligence (SICAAI 2026), Kragujevac, Serbia, and was carried out within the STRATIFYHF project.