Logits in Transformer Language Models

A language model processes the following two sentences independently:

1. 'The river bank was steep and muddy.'
2. 'He withdrew cash from the bank.'

Considering the final layer of the model, how would the output vector (the final hidden state) for the word 'bank' in the first sentence compare to the output vector for 'bank' in the second sentence?

A language model with multiple layers processes an input sequence to predict the next token. For a single token within that sequence, arrange the following representations in the chronological order they are computed by the model.

A machine learning engineer is building a system to classify the sentiment of customer reviews (e.g., positive, negative). They decide to use the internal representations from a pre-trained, multi-layered language model as features for their classifier. Which of the following model outputs would provide the most contextually-rich and effective representation of an entire review for this classification task?