A. Zero-shot learning is a concept where a model can generalize to new classes it has never seen during training. For instance, if a model is trained to recognize various dog breeds but hasn't encountered a specific breed like "Xoloitzcuintli," it can still make accurate predictions about it by leveraging shared attributes and information from related classes. This ability to infer without explicit training on a class characterizes the essence of zero-shot learning.