If you keep the FastText embeddings unchanged a do not finetune them during training, it does not really matter that the words were not in the training set as long as they are in the FastText embeddings. After all, this is the biggest advantage of using pre-trained word embeddings.
The important property of the embeddings is that similar words get similar embeddings. The CNN might not have seen the exact same embedding, but similar words probably were in the training data.
Words that are not covered by the pre-trained embeddings, got a common representation for an unknown (out-of-vocabulary, OOV) word. These are usually proper names. It is usually good if you make sure that CNN learns to deal with them already at the training time (you can randomly replace some infrequent words by some random strings) because if the unknown token embedding (that is typically dissimilar to all other embeddings) appears at the inference time and it was never seen at training, it can lead to unexpected behavior.