AWS Certified AI Practitioner AIF-C01 Practice Question

Supervised learning relies on a training dataset in which every example includes both the input features and the correct output label. The model learns to map inputs to these known outputs so it can generalize to new, unseen data. Methods that work with unlabeled data (unsupervised learning), learn by trial-and-error rewards (reinforcement learning), or simply form similarity-based clusters do not use labeled input-output pairs, so they are not considered supervised.