Welcome to your interview.
The question is on your right: Handling Missing and Skewed Data. Take a moment with it first.
Talk your thinking through with me if you like - when you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes). Discussion and graded submissions share your five interviewer interactions, so spend them well.
Data preparation often matters more than the final analysis. In tools like McKinsey & QuantumBlack workflows, poor handling of missing values or highly skewed distributions can distort summary metrics, segment comparisons, and downstream models.
Explain how you would handle missing data and highly skewed data when preparing a dataset for analysis in SQL. Your answer should cover how you would identify the issue, decide whether to filter, impute, cap, transform, or flag values, and how you would preserve analytical transparency.
You should answer at a practical analyst level: describe the decision process, trade-offs, and the SQL techniques you would use. It is helpful to mention how CASE WHEN, COALESCE, and aggregate checks can support data quality review before analysis.