Your question is Handling Missing Data in SQL. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
Missing data is common in operational and analytics datasets, including telemetry and incident records from platforms like Cortex XDR. If you handle it poorly, you can distort aggregates, hide data quality issues, or produce misleading dashboards.
Explain how you handle missing data in a SQL workflow. You should describe how you first identify missing values, how you decide whether to filter, impute, or preserve them, and how SQL functions such as IS NULL and COALESCE help. Also explain how missing values can affect aggregates, grouping, and downstream reporting.
Keep your answer practical and SQL-focused. The interviewer is looking for a clear approach to working with NULL values in analysis, not a deep discussion of machine learning imputation techniques.