Database Management Systems Interview Questions 2026

Tip: 1NF: atomic values, no repeating groups. 2NF: 1NF + no partial dependency on part of a composite key. 3NF: 2NF + no transitive dependency (non-key attributes depend only on the key). Goal: remove redundancy and update/insert/delete anomalies.

Tip: A candidate key uniquely identifies a row; the chosen one is the primary key (unique, not null). Other candidates are alternate keys. A foreign key references another table's primary key to enforce referential integrity.

Tip: Atomicity: a transaction is all-or-nothing. Consistency: it moves the DB between valid states honouring constraints. Isolation: concurrent transactions do not interfere. Durability: committed changes survive failures. These guarantee reliable transactional processing.

Tip: Read Uncommitted allows dirty reads; Read Committed prevents dirty reads; Repeatable Read prevents non-repeatable reads; Serializable prevents phantom reads (full isolation). Higher isolation reduces concurrency — choose the lowest level that prevents the anomalies your app cares about.

Tip: A clustered index defines the physical row order (one per table — often the primary key); the leaf level IS the data. A non-clustered index is a separate structure pointing back to rows, so multiple can exist. Clustered scans are great for range queries on the key.

Tip: A deadlock occurs when two transactions each hold a lock the other needs, waiting forever. DBMSs detect cycles in the wait-for graph and abort a victim transaction (to be retried). Prevent by acquiring locks in a consistent order and keeping transactions short.

Tip: DELETE removes rows (optionally with WHERE), is logged row-by-row, and can be rolled back. TRUNCATE removes all rows quickly with minimal logging and resets identity, but can't use WHERE. DROP removes the table structure entirely.

Tip: SQL databases are relational with fixed schemas, strong ACID guarantees, and powerful joins — ideal for structured, transactional data. NoSQL (document, key-value, column, graph) offers flexible schemas and horizontal scalability with eventual consistency — ideal for large-scale, varied, or rapidly-evolving data.