My answers for my onsite interview: 1. Do as you did 2. Assuming some distributions hidden in your missing, then filling the missing value accordingly 3. If we can justify the missing at random (or complete at random, but it is too optimistic), try several imputation methods like multiple imputation methods. Basic idea is to assume some dependency relationship with other predictors, using them to predict the missings. For information, see Rubin's paper about Multiple imputation.