This tripped me up a bit because it sounds like a cleanup question but it's really asking you to justify a design decision under pressure.
Acknowledge that the choice depends on the data's meaning and the model's requirements, then explain the trade-offs of each option (drop, NULL, zero-fill) in terms of bias, information loss, and downstream compatibility. Finally, justify your current approach by tying it to the specific context and mention alternatives you considered.
Pro tip: Show that you understand missing data mechanisms (MCAR, MAR, MNAR) and that zero-filling can be a deliberate choice for sparse features, but always validate with domain knowledge and consider model sensitivity.
Start by stating that the right approach depends on why values are missing, what the feature represents, and how the model will use it. Mention that dropping, NULL, and zero-fill each have different implications for bias and performance.
Explain that dropping can be valid if missingness is random and the dataset is large, but it risks losing valuable information and introducing bias if missingness is systematic. Also note that dropping may not be feasible if many features have missing values.
Discuss that NULL preserves the missingness but many models (e.g., linear models, neural networks) cannot handle NULL directly, requiring imputation or special handling. NULL is useful for tree-based models that can treat it as a separate category, but may complicate feature engineering.
Explain that zero-filling is a simple imputation that works when zero is a meaningful value (e.g., counts, sparse features) and when missingness likely means absence. However, it can distort distributions and introduce bias if zero is not semantically appropriate.
Conclude by stating why zero-filling was chosen in this case (e.g., domain knowledge, model compatibility, simplicity) and mention that you considered dropping or NULL but rejected them for specific reasons. Optionally, suggest more sophisticated imputation methods if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.