Transfer learning takes a model already trained on a large, general-purpose dataset (e.g., ImageNet for vision, or a large text corpus for language models) and adapts it to a new, typically smaller and more specific target task --- either by fine-tuning some or all of the pretrained weights, or by freezing the pretrained backbone and training only new task-specific layers on top. Because the pretrained model has already learned general-purpose, reusable features (edge and texture detectors in early CNN layers, syntactic and semantic structure in language model layers), the target task requires substantially less labeled data and less compute than training a comparable model from random initialization.

Prompt engineering (C) is a related but distinct technique specific to large language and generative models: it adapts a *frozen* pretrained model's behavior through the design of the input prompt alone, without any weight updates --- a lighter-weight technique than transfer learning, applicable only where a sufficiently capable pretrained model already exists. Options A and D are not standard, well-defined ML techniques matching this description; 'state management and composition' and 'neural network integration' are generic software-engineering-sounding terms without a specific technical meaning in this context, making them straightforward distractors to eliminate.