AWS Certified Cloud Practitioner

The correct answer is B. Self-Attention mechanism.

Explanation:

• Self-Attention mechanism is the core component in Transformer architecture that allows the model to weigh the importance of different words in a sentence relative to each other. It processes all words in parallel and captures contextual relationships simultaneously, unlike sequential models.
• Recurrent loops (Option A) are used in RNNs and LSTMs, which process sequences sequentially and cannot capture all relationships simultaneously due to their sequential nature.

The Self-Attention mechanism enables Transformers to handle long-range dependencies efficiently and has become fundamental in modern NLP models like BERT and GPT.