Practice joins, subqueries, indexing, and query optimization with real database scenarios
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Learn indexing strategies and query optimization techniques
Understand normalization, relationships, and schema design
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SELECT queries and JOINs (INNER, LEFT, RIGHT)
Subqueries and CTEs (Common Table Expressions)
Aggregations and GROUP BY
Window functions (ROW_NUMBER, RANK, LAG)
Indexes and query optimization
Transactions and ACID properties
A: INNER JOIN returns only matching rows from both tables. LEFT JOIN returns all rows from left table and matching rows from right (nulls for non-matches). RIGHT JOIN is opposite of LEFT. FULL OUTER JOIN returns all rows from both tables with nulls where no match exists. Use INNER JOIN for required relationships, LEFT for optional relationships.
A: An index is a data structure (usually B-tree) that improves data retrieval speed. Like a book index, it allows the database to find data without scanning every row. Trade-off: faster SELECT queries but slower INSERT/UPDATE/DELETE and additional storage. Create indexes on columns frequently used in WHERE, JOIN, ORDER BY clauses.
A: Normalization reduces data redundancy and improves integrity. 1NF: atomic values, no repeating groups. 2NF: 1NF + no partial dependencies. 3NF: 2NF + no transitive dependencies. BCNF: stricter 3NF. Denormalization sometimes makes sense for read performance, but normalization prevents update anomalies and saves storage.
A: Transactions are logical units of work. ACID ensures reliability: Atomicity (all or nothing), Consistency (valid state), Isolation (concurrent transactions don't interfere), Durability (committed changes persist). Use BEGIN/COMMIT/ROLLBACK to control transactions. Essential for maintaining data integrity in multi-step operations.
Practice writing complex queries with multiple JOINs and subqueries
Understand query execution plans and how to optimize slow queries
Master window functions: ROW_NUMBER, RANK, LAG, LEAD
Know the difference between WHERE and HAVING clauses
Understand indexing strategies and when to use composite indexes
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