Handling Missing Values in SQL | Dataford Interview Questions

Your question is Handling Missing Values 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).

Problem

Context

Missing values are common in analytics tables and operational datasets. Interviewers ask this question to see whether you can distinguish between simply replacing NULLs and making a sound data-cleaning decision.

Core question

Explain how you would handle missing values in a SQL dataset. Your answer should cover:

How to identify missing values and understand why they are missing
When to leave NULLs as-is versus filling them with defaults or derived values
Common SQL techniques for handling missing data in numeric, categorical, and date fields
Risks of introducing bias or incorrect business logic when imputing values

Scope guidance

The interviewer is usually looking for a practical, SQL-oriented explanation rather than advanced statistical imputation. Focus on data quality checks, business context, and common PostgreSQL techniques such as COALESCE, CASE WHEN, filtering, and aggregation-based replacement strategies.

Your question is Handling Missing Values 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).

Context

Core question

Explain how you would handle missing values in a SQL dataset. Your answer should cover:

How to identify missing values and understand why they are missing
When to leave NULLs as-is versus filling them with defaults or derived values
Common SQL techniques for handling missing data in numeric, categorical, and date fields
Risks of introducing bias or incorrect business logic when imputing values