Miss it here,
not in the exam.

The softmax function converts the raw attention scores (logits from query-key dot products) into a valid probability distribution where all weights are non-negative and sum to 1. This means each token's representation is a weighted combination of all value vectors, with the weights reflecting learned relevance. Without softmax, the raw scores could be arbitrarily large or negative, making the weighted sum unstable and uninterpretable. The option about eliminating negatives is partially true (softmax outputs are non-negative) but mischaracterises the purpose — softmax normalises, it does not simply clip negatives. The option about equal weights describes mean pooling, not attention. The option restricting softmax to training is wrong — softmax is applied at every forward pass, including inference.