In themulti-head attentionmechanism:

A:True — the input embedding dimension is split across multiple heads, so each head operates on a lower-dimensional subspace before concatenation.

B:True — having multiple attention heads allows the model to attend to information from different representation subspaces simultaneously.

C:False — each head has its own learned linear transformations for queries, keys, and values.

D:False — after concatenation, the result is passed through a final linear projection, not fed back into the attention module directly.

Exact Extract from HCIP-AI EI Developer V2.5:

"Multi-head attention divides the embedding dimension across heads to learn from multiple subspaces in parallel, then concatenates and linearly projects the result."