600+ SQL Interview Questions for 2025

Explore SQL aggregate functions with practical examples on a product table, including count, min, max, and avg, and combining multiple aggregates in a single query.

Learn how the distinct keyword returns unique values from a column like category, and how count(distinct category) reveals the number of unique records in product.

Learn how to use the cast function to convert salary data stored as strings into numeric types by casting to float and then to integer, including handling decimals.

Explore SQL aliases with the as keyword to rename columns and tables, apply aliases across single or multiple columns, and troubleshoot alias usage in queries.

Master limit and offset to control query results. Use limit to fetch the first five rows and offset to skip rows and retrieve subsequent ranges from the product table.

Explore how to filter string columns with the SQL where clause, including equals and not equals, and combining product name and category with and/or to retrieve precise results.

Learn how the where clause filters data in sql queries using equals, not equals, greater than, and less than on stock and price.

Explore filtering string columns with the like keyword, using % and _ wildcards to match starts with, ends with, or contains patterns in product names.

Explore how the SQL like keyword uses underscore for a single character and percent for any string, with examples like laptop, tablet, Powerbank, and Bluetooth speaker.

Filter boolean columns with the where clause to retrieve active products, using true or 1 for true and false or 0 for false, and combine conditions with and and or.

Learn how null and not null affect SQL tables, including constraints, inserting with nulls, querying with is null and is not null, and how nulls behave in aggregate calculations.

Explore the between clause to filter numeric and date columns, including endpoints. See price between 49.99 and 200.99 and order date between 1st January 2024 and 5th January 2024.

Discover how the SQL between operator works, its limits, and how to combine it with other conditions like active = false using price between 10 and 20.

Learn how the group by clause groups rows by category (and is active), and apply aggregate functions such as sum, max, min, and count to derive category level insights.

Learn to use group by with having to filter aggregated category totals, summing prices and stock, and apply multiple conditions beyond where.

Learn to use the where clause with group by to filter groups before aggregation, using price > 200 and active = true on product name, price, and is active.

Explore how the union operator in SQL combines results from two tables, removes duplicate values, and consolidates employee data from multiple sources.

Explore the union all operator in SQL to combine results from two or more tables, including duplicates, and compare it with union, which removes duplicates.

Explore the intersect operator in SQL by comparing two tables to return only common rows and matching columns, while contrasting with union and removing duplicates.

Learn how to use subqueries to filter data dynamically with inner queries, the in clause, and distinct product IDs from orders to retrieve sold products.

Learn to use subqueries to filter data with inner queries. Compare equals and in, manage multiple results, and map a single column between inner and outer queries.

Sort data with the order by clause in SQL, mastering ascending and descending order on product name and price, and multi-column sorting like product name with stock.

Explore SQL window functions with the row_number function, using over, partition by, and order by to rank employees by salary and find top earners per department.

Explore rank and dense_rank window functions, compare with row_number, learn how they handle ties, partition by department, and find top n in each group, plus percentile ranks.

Explore window aggregate functions to compute running totals, moving averages, and percentages of total across rows, using over with partition by and order by for department and overall insights.

Explore common table expressions (ctes) in sql, using the with keyword to create a temporary result set that improves readability and enables reuse and recursive operations.

demonstrates how to use common table expressions with filters in SQL by creating an active_products cte and querying it, including category and is_active filters.

Explore common table expressions (CTEs) with unions and union all, using a product table to demonstrate distinct categories and the with block syntax.

Learn to write recursive cte queries that generate numbers from 1 to 10 using a base case, union all, and a stop condition to prevent infinite loops and retrieve results.

Explore recursive cte queries that generate even numbers from two to ten using a base case and union all. Run the final select from even numbers to view the results.

Generate Fibonacci numbers in SQL with a recursive CTE, initializing n, a, and b, then use union all to compute up to the tenth position and select from Fibonacci.

Explore recursive ctes to build an employee hierarchy and retrieve direct and indirect reports. Learn how to use union all and level tracking to traverse managers up to the top.

Learn how to implement range partitioning by date to create partitioned tables, automatically route data, and query specific partitions for faster time-based analytics.

Learn how to insert data into a range partition table, with the database automatically routing each row to the correct partition based on sale_date.

Explore range partitioning and partition pruning to accelerate queries by scanning only relevant partitions, using the sale date as the partition key.

Explore list partitioning to divide data by discrete region values, and learn to create a main partitioned table with North and South region partitions and automatic data routing.

Insert into a list partitioned table automatically routes records to partitions by region, and you can verify correct routing by querying the main table and each partition.

Harness list partitioning by region to enable partition pruning, so the optimizer scans only the north or south partition when filtering on region.

Explore hash partitioning to distribute data evenly by hashing the customer_id with modulus four, enabling load balancing and parallel query processing.

Learn how inserts target a hash partition table, with automatic routing by the hash of customer_id, even distribution across partitions, and how to handle partition absence.

Enable robust data routing with default partitioning, a catch-all for values not matching range, list, or hash partitions. See how customer_default stores unknown regions to prevent insert failures.

Explore arrays in PostgreSQL, learning how to store multiple values in a single column and create array columns. Learn to insert, query, and retrieve specific indices for marks and subjects.

Explore arrays with aggregate functions in SQL; compute array lengths with array_length, and sum scores by unnesting the marks array via a subquery to get total marks per student.

Explore arrays in SQL with the all and any operators in the where clause to filter data by every or at least one element in a multi-value column.

Learn how to create and use SQL views, a virtual table that forwards queries to the underlying table, enabling column and row filtering for security and data subset.

Explore case statements in SQL to implement conditional logic and categorize products by price and active status, using basic and advanced case statements, including or conditions for dynamic results.

Explore the coalesce function to gracefully handle null values in SQL, replacing null emails with no email and salaries with zero in an employee table.

Learn how the null if function handles conditional nulls in SQL by replacing specific salaries with null. See practical employee table examples with 75,000, 70,000, and 55,000.