Predicting the missing word using fasttext pretrained word embedding models (CBOW vs skipgram)

I am trying to implement a simple word prediction algorithm for filling a gap in a sentence by choosing from several options:

Driving a —- is not fun in London streets.

1. Apple
2. Car
3. Book
4. King

With the right model in place:

Question 1. What operation/function has to be used to find the best fitting choice? The similarity functions in the library are defined between one word to another word and not one word to a list of words (e.g. most_similar_to_given function). I don’t find this primitive function anywhere while it is the main operation promised by CBOW (see below)! I see some suggestions here that are not intuitive! What am I missing here?

I decided to follow the head first approach and start with fastText which provides the library and pre-trained datasets but soon got stuck in the documentation:

fastText provides two models for computing word representations: skipgram and cbow (‘continuous-bag-of-words’). The skipgram model
learns to predict a target word thanks to a nearby word. On the other
hand, the cbow model predicts the target word according to its
context. The context is represented as a bag of the words contained in
a fixed size window around the target word.

The explanation is not clear for me since the “nearby word” has a similar meaning as “context”. I googled a bit and ended up with this alternative definition:

In the CBOW model, the distributed representations of context (or surrounding words) are combined to predict the word in the middle.
While in the Skip-gram model, the distributed representation of the
input word is used to predict the context.

With this definition, CBOW is the right model that I have to use. Now I have the following questions:

Question 2. Which model is used to train fastText pre-trained word vectors? CBOW or skipgram?

Question 3. Knowing that the right model that has to be used is CBOW, can I use the pre-trained vectors trained by skipgram model for my word prediction use case?