Welcome to your interview.
The question is on your right: Handling Missing Data in SQL. 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.
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.