A best reflects practical application of data minimization to machine learning. Data minimization requires personal data to be adequate, relevant, and limited to what is necessary for the specified purpose. AI development does not categorically prohibit using personal data in training, so B is too absolute. Personal data may legitimately be required during both training and inference, making D incorrect. Manual review of every data item is neither the definition nor a necessary implementation of minimization. Privacy-enhancing techniques can instead reduce the amount or identifiability of personal data processed while retaining model utility. ICO AI guidance specifically identifies perturbation or adding noise, synthetic data, and federated learning as techniques that can support data minimization during model training. Thus, A is the technically and governance-aligned choice.

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