I'm assuming you don't mean missing values in your dataset.

1. You can create 'missing' data, but if you create the missing data out of the data you already give to the model you're sort of doing the work for it. For compute efficient reasons you might want to avoid giving it 'unnecessary' data. What is unnecessary can be hard to define. Think about what you want the model to grasp in the first place.

2. I'm not sure what you mean by performing a test. If you were to train a language model the context of the word would define its meaning. You can always take the output probs of a model and do something with that if you'd like (for instance, if it's lots of low probability alternatives - do something).