NULL values makes business logic far more compact and intuitive. For enumerated values in fields it's easy enough to define another value in the same field to mean 'unknown' or 'not entered', but you can't do that for strings, numbers, datetimes, etc. -- you have to throw in a bunch of unwieldy additional boolean fields instead.
In contrast to what SQL has, Sum Types combined with Product Types (which is basically what a row is) are actually a universal way to model all possible data[0]. (Of course you may want syntax sugar, etc. on top of that, but Sums and Products at the bottom is sufficient.)
[0] I'm actually not sure if I should qualify that -- I believe Sums+Products can actually model anything, assuming you allow recursive type definitions -- which might be hard to make perform well. Storing a linked list in a database field, e.g. might not be the best idea.
e.g.
select questions.Id , questions.Text , answers.Id , answers.Text
from questions left join answers on answers.QuestionId = Questions.Id
answers.Id is non-nullable as a primary key, so if answers.Text is null but answers.Id is not null they've declined to answer.
In your example it is very likely that the primary key is the clustering key, so will be present in the non-clustered index that I assume will be on answers.questionId, making my point moot, but if for some unusual reason neither Id nor questionId were the clustering key checking Id may result in extra reads being needed.
In DBMSs without clustering keys implemented similarly to SQL Server, there may be such concerns in all cases.