SQL Interview Questions & Answers
The most common SQL interview questions with clear answers — joins, keys, indexes, window functions and more. Practise them live in a free AI mock interview.
Practise these in a free AI mock interview1. What are the different types of JOINs?
INNER (matches in both), LEFT and RIGHT (all rows from one side plus matches), FULL OUTER (all rows from both), and CROSS (every combination). A self-join joins a table to itself.
2. Primary key vs foreign key?
A primary key uniquely identifies each row (unique, not null). A foreign key references another table's primary key, enforcing the relationship and integrity between tables.
3. What is an index and when should you use one?
An index speeds up lookups, filters and sorts on a column, like a book's index. Add them on columns used in WHERE/JOIN/ORDER BY — but not everywhere, since indexes slow writes and use storage.
4. Explain GROUP BY with an example.
GROUP BY collapses rows sharing a value so you can aggregate: SELECT region, SUM(sales) FROM orders GROUP BY region returns total sales per region.
5. What is a window function?
A function that computes across a set of rows related to the current row without collapsing them — ROW_NUMBER(), RANK(), running SUM() OVER (PARTITION BY ... ORDER BY ...). Ideal for rankings and running totals.
6. Difference between DELETE, TRUNCATE and DROP?
DELETE removes selected rows (can use WHERE, logged, roll-back-able). TRUNCATE quickly removes all rows with minimal logging. DROP removes the entire table structure.
7. What is a subquery?
A query nested inside another, in SELECT, FROM or WHERE — e.g. find employees earning above the average using (SELECT AVG(salary) ...).
8. WHERE vs HAVING?
WHERE filters rows before grouping; HAVING filters groups after GROUP BY. Use WHERE for raw conditions and HAVING for aggregate conditions.
9. How do you find duplicate rows in SQL?
GROUP BY the columns that define a duplicate and use HAVING COUNT(*) > 1 to list the values that repeat.
10. Normalization vs denormalization?
Normalization splits data to remove redundancy (better integrity, more joins). Denormalization combines data to speed reads (fewer joins, some redundancy) — common in reporting and analytics.
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