How to pass input to deep learning models for Multiple choice question answering problem?
The basic idea of most of the current question answering architectures is:

get a common representation of the question and the input text (e.g., using BERT)
get a representation of the answers
do sort of attention over the answers: compute a scalar score for each of the answers (using a dot-product, linear layer, multilayer-perceptron) and normalize the scores using softmax

The architectures are typically based on the Bidirectional Attention Flow Model, although it was designed for a slightly different task and although the pre-trained word embeddings and RNNs in the model are today usually replaced with BERT-like models. In 2018, there was a competition in question answering at SemEval where many interesting ideas on this problem were presented.