AWS Certified AI Practitioner AIF-C01 Practice Question

Unsupervised learning is suited to situations where the training data lacks predefined labels. Algorithms such as clustering can examine purchase‐history features and group customers into segments based solely on similarities in the data. Supervised learning requires labeled examples, so it cannot be applied directly here. Reinforcement learning relies on an agent receiving rewards from interacting with an environment, which does not match the scenario. Transfer learning reuses knowledge from a different, often pre-trained model and is not the primary approach for discovering new clusters in unlabeled data.