SQL

Top SQL Interview Questions for 2025: Your Ultimate Preparation Guide

Adam Fils

Apr 22, 2025 — 6 min read

TLDR: Top 10 SQL Interview Questions You Need to Master in 2025

Database Fundamentals: Be ready to explain SQL vs. NoSQL databases and key RDBMS concepts.
SQL Joins: Know how to implement and explain all types of joins (INNER, LEFT, RIGHT, FULL).
Window Functions: Understand ROW_NUMBER(), RANK(), DENSE_RANK() for solving complex aggregation problems.
Common Table Expressions (CTEs): Be able to use the WITH clause to simplify complex queries.
Performance Optimization: Explain indexing strategies and query tuning techniques.
Subqueries vs. Joins: Know when to use each and be able to convert between them.
Advanced GROUP BY: Apply aggregation with HAVING clauses for data analysis tasks.
Second-Highest Salary Problem: Solve this classic problem using subqueries or window functions.
UNION vs. UNION ALL: Understand the difference and performance implications.
Recursive Queries: Use recursive CTEs to query hierarchical data structures.

Introduction

In today's data-driven world, SQL remains essential for many tech roles, from data analysts and engineers to backend developers. As we move into 2025, SQL interview questions continue evolving, focusing more on practical application and problem-solving than just syntax knowledge.

If preparing for a SQL interview, you must be ready for questions that test your understanding of database fundamentals, query optimization, and real-world problem-solving. This guide will walk you through the most common SQL interview questions for 2025, with easy-to-understand explanations and examples.

Want to practice these skills in a real interview environment? Try Wyspa, an AI-powered interview preparation platform that creates custom mock interviews for your target role. Wyspa lets you practice answering SQL questions out loud, providing immediate feedback on your responses and helping you build confidence before your real interview.

Fundamental SQL Concepts

What is SQL, and how does it differ from NoSQL?

SQL (Structured Query Language) is a standardized programming language for managing relational databases. It's designed to manage structured data where relationships exist between different entities.

Key differences from NoSQL:

SQL databases are table-based with predefined schemas
NoSQL databases are document, key-value, graph, or wide-column stores
SQL excels at complex queries and transactions
NoSQL offers more flexibility for unstructured data

Explain the different types of SQL commands

SQL commands are broadly categorized into:

Data Definition Language (DDL): Commands like CREATE, ALTER, and DROP that define database structure
Data Manipulation Language (DML): Commands like SELECT, INSERT, UPDATE, and DELETE that manipulate data
Data Control Language (DCL): Commands like GRANT, REVOKE that control access permissions
Transaction Control Language (TCL): Commands like COMMIT, ROLLBACK that manage transactions

What is normalization, and why is it important?

Normalization is organizing data in a database to reduce redundancy and improve data integrity. It involves dividing large tables into smaller, related tables and defining their relationships.

Benefits include:

Reducing data duplication
Preventing update anomalies
Improving query performance
Making the database more flexible for future changes

SQL Joins and Relationships

Explain the different types of JOINs in SQL

Understanding joins is crucial for SQL interviews. The main types are:

CROSS JOIN: Returns the Cartesian product (all combinations of rows)

SELECT * FROM employees CROSS JOIN departments;

FULL JOIN: Returns all rows when there's a match in either table

SELECT * FROM employees 
FULL JOIN departments ON employees.dept_id = departments.id;

RIGHT JOIN: Returns all rows from the right table and matching rows from the left

SELECT * FROM employees 
RIGHT JOIN departments ON employees.dept_id = departments.id;

LEFT JOIN: Returns all rows from the left table and matching rows from the right

SELECT * FROM employees 
LEFT JOIN departments ON employees.dept_id = departments.id;

INNER JOIN: Returns only matching rows from both tables

SELECT * FROM employees 
INNER JOIN departments ON employees.dept_id = departments.id;

What's the difference between UNION and UNION ALL?

Both UNION and UNION ALL combine result sets from multiple SELECT statements, but:

UNION removes duplicate rows
UNION ALL includes all rows, including duplicates
UNION ALL is faster because it doesn't need to check for duplicates

How would you find employees who don't belong to any department?

SELECT * FROM employees 
WHERE department_id IS NULL;
-- OR using LEFT JOIN
SELECT e.* FROM employees e
LEFT JOIN departments d ON e.department_id = d.id
WHERE d.id IS NULL;

Advanced SQL Features

Explain Common Table Expressions (CTEs)

Common Table Expressions (CTEs) are temporary result sets that exist only within the execution scope of a single SQL statement. They make complex queries more readable and maintainable.

WITH EmployeeSalaries AS (
    SELECT department_id, AVG(salary) as avg_salary
    FROM employees
    GROUP BY department_id
)
SELECT e.name, e.salary, es.avg_salary
FROM employees e
JOIN EmployeeSalaries es ON e.department_id = es.department_id
WHERE e.salary > es.avg_salary;

What are window functions, and when would you use them?

Window functions perform calculations across table rows related to the current row. They're handy for analytical queries.

Standard window functions include:

ROW_NUMBER(): Assigns a unique number to each row
RANK() and DENSE_RANK(): Assign ranking with different handling of ties
LAG() and LEAD(): Access data from previous or subsequent rows
SUM(), AVG(), etc. over a window: Calculate aggregates over specific groups

Example:

SELECT 
    name,
    department,
    salary,
    RANK() OVER (PARTITION BY department ORDER BY salary DESC) as salary_rank
FROM employees;

Explain how you would write a recursive query in SQL

Recursive CTEs help query hierarchical data like organizational charts or a bill of materials:

WITH RECURSIVE EmployeeHierarchy AS (
    -- Base case: start with the CEO
    SELECT id, name, manager_id, 1 as level
    FROM employees
    WHERE manager_id IS NULL
    
    UNION ALL
    
    -- Recursive case: add subordinates
    SELECT e.id, e.name, e.manager_id, eh.level + 1
    FROM employees e
    JOIN EmployeeHierarchy eh ON e.manager_id = eh.id
)
SELECT * FROM EmployeeHierarchy;

SQL Query Optimization

What are indexes, and how do they improve performance?

Indexes are special data structures that speed up data retrieval operations. They work similarly to a book's index, providing quick access to data without scanning the entire table.

Best practices for indexing:

Index columns frequently used in WHERE clauses
Index columns used in JOIN conditions
Consider composite indexes for queries with multiple conditions
Avoid over-indexing as it slows down write operations

How can you identify and optimize slow queries?

Steps to identify and optimize slow queries:

Use tools like EXPLAIN to analyze query execution plans
Look for full table scans, which indicate missing indexes
Examine join operations for inefficiencies
Check for unnecessary sorts or temporary tables
Consider rewriting complex queries using CTEs or temp tables
Ensure proper indexing strategy

What is query caching, and how does it affect performance?

Query caching stores the result set of a query in memory, so identical queries can retrieve results without executing the query again. This can significantly improve performance for read-heavy applications.

Considerations:

Caching is most effective for queries that are run frequently
It's less useful for constantly changing data
Cache invalidation occurs when the underlying data changes

Common SQL Interview Problems

How would you find the second-highest salary in a table?

This is a classic SQL interview question with several solutions:

Using a subquery:

SELECT MAX(salary) 
FROM employees 
WHERE salary < (SELECT MAX(salary) FROM employees);

Using window functions:

SELECT salary
FROM (
    SELECT salary, DENSE_RANK() OVER (ORDER BY salary DESC) as rank
    FROM employees
) ranked
WHERE rank = 2;

How would you handle duplicate records in a table?

To identify duplicates:

SELECT column1, column2, COUNT(*)
FROM table_name
GROUP BY column1, column2
HAVING COUNT(*) > 1;

To remove duplicates:

-- Using a CTE and ROW_NUMBER
WITH DuplicatesCTE AS (
    SELECT *, ROW_NUMBER() OVER (PARTITION BY column1, column2 ORDER BY id) as row_num
    FROM table_name
)
DELETE FROM DuplicatesCTE WHERE row_num > 1;

How would you calculate running totals and moving averages?

Using window functions:

Running total:

SELECT 
    date,
    amount,
    SUM(amount) OVER (ORDER BY date) as running_total
FROM transactions;

Moving average (3-day):

SELECT 
    date,
    amount,
    AVG(amount) OVER (ORDER BY date ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) as moving_avg
FROM daily_sales;

Real-World SQL Scenarios

How would you design a schema for an e-commerce website?

A basic e-commerce schema might include:

Users table: customer information
Products table: product details
Categories table: product categorization
Orders table: order information
OrderItems table: linking orders to products
Inventory table: stock management
Reviews table: product reviews

Key considerations:

Proper relationships between tables
Normalization to avoid redundancy
Indexing strategy for common queries
Handling of variable product attributes

How would you analyze user activity data to find patterns?

Sample queries for user activity analysis:

User engagement by day of week:

SELECT 
    DAYOFWEEK(activity_date) as day_number,
    COUNT(DISTINCT user_id) as active_users
FROM user_activities
GROUP BY DAYOFWEEK(activity_date)
ORDER BY day_number;

User retention analysis:

WITH FirstLogins AS (
    SELECT user_id, MIN(activity_date) as first_login
    FROM user_activities
    GROUP BY user_id
),
RetentionData AS (
    SELECT 
        f.user_id,
        DATEDIFF(a.activity_date, f.first_login) as days_since_first
    FROM FirstLogins f
    JOIN user_activities a ON f.user_id = a.user_id
)
SELECT 
    days_since_first,
    COUNT(DISTINCT user_id) as retained_users
FROM RetentionData
WHERE days_since_first <= 30
GROUP BY days_since_first
ORDER BY days_since_first;

Preparing for Your SQL Interview

Practice with Real-World Problems

The best way to prepare for SQL interviews is to practice with real-world problems. Platforms like LeetCode, HackerRank, and DataLemur offer SQL challenges that mimic interview questions.

Mock Interviews with Wyspa

Want to take your preparation to the next level? Try Wyspa, an AI-powered mock interview platform. Wyspa creates customized SQL interview scenarios based on your target role, allows you to respond verbally as in a real interview, and provides immediate feedback on your answers.

With Wyspa, you can:

Practice real SQL interview questions
Get instant AI feedback on your responses
Build confidence in explaining technical concepts
Prepare for your specific job domain
Use the "Teach Me" feature when you're stuck on a difficult question

All you need is to sign in to Wyspa, create an account, and start practicing in less than a minute.

Conclusion

SQL remains a crucial skill for many tech roles in 2025. By mastering these key SQL interview questions and practicing with real-world scenarios, you'll be well-prepared to demonstrate your database expertise in your next interview.

Remember that interviewers are looking for not just syntactical knowledge but also your problem-solving approach and understanding of database principles. By practicing with tools like Wyspa, you can ensure you're ready to tackle even the most challenging SQL interview questions.

Are you ready to ace your next SQL interview? Start practicing today!

This blog post was created to help job seekers prepare for SQL interviews in 2025. For more interview preparation resources, visit Wyspa and start practicing with AI-powered mock interviews tailored to your needs.