Always document your cleaning decisions. Future you (or your colleague) needs to know why you dropped those 200 rows.
Strong answers follow a systematic approach: profile the data first (counts, distributions, null rates), assess data quality issues by column, decide on handling strategies (imputation, deletion, flagging) based on the analysis goal, document all cleaning decisions, and validate the cleaned data against known benchmarks. Best candidates mention reproducibility.
Tests practical data skills. Real analysts spend 80% of their time on data quality. Red flag: candidates who assume data is always clean or who delete missing values without thought.