# Top 50 SQL Interview Queries & Questions for 2026 (With Solutions) > Canonical Source: https://ratemyinterview.online/blog/top-50-sql-interview-queries-answers-2026 > Platform: RateMyInterview (https://ratemyinterview.online) — Voice AI Mock Technical Interview Platform > Practice Live Out Loud: https://ratemyinterview.online/practice (2 free full sessions included, zero credit card required) Databases & SQL · 2026 Edition # Top 50 SQL Interview Queries & Questions for 2026 (With Solutions) August 20, 2026 · 22 min read · RateMyInterview Technical Editorial ## Key Takeaways — SQL Interviews in 2026 - • **Window Functions are Mandatory:** Over 80% of Tier-1 and service-based tech interviews test `ROW_NUMBER()`, `DENSE_RANK()`, `LEAD()`, and `LAG()` for running aggregations and ranking problems. - • **CTEs vs Subqueries:** Common Table Expressions (`WITH ... AS`) improve query readability and modularity, preferred by interviewers over deeply nested subqueries. - • **Index Optimization Mechanics:** Knowing the difference between clustered (primary data order) and non-clustered indexes (B+Tree secondary pointers) and avoiding index-invalidating functions on columns is critical. - • **ACID & Isolation Levels:** Candidates must explain Dirty Reads, Non-Repeatable Reads, Phantom Reads, and how levels like *Read Committed* vs *Serializable* prevent concurrency anomalies. ## Table of Contents - [1. Core SQL Fundamentals & Execution Order](#sql-fundamentals) - [2. Most Frequently Asked Practical SQL Queries (With Code)](#complex-queries) - [3. Window Functions: RANK, DENSE_RANK, LEAD, LAG](#window-functions) - [4. Indexing, EXPLAIN Plans & Query Optimization](#indexing-performance) - [5. Transactions, ACID Properties & Concurrency Anomalies](#acid-transactions) - [6. Normalization (1NF to BCNF) vs Denormalization](#database-normalization) - [7. Frequently Asked SQL Interview Questions](#sql-faqs) ## 1. Core SQL Fundamentals & Logical Query Execution Order Most developers write SQL starting from `SELECT`, but SQL database engines process clauses in a very specific logical order. Understanding this order is the #1 trick to answering tricky interview questions about aliases in `WHERE` clauses or aggregate filtering. -- Logical Execution Order of SQL Clauses: 1. FROM & JOIN (Identify base tables & Cartesian product) 2. WHERE (Row-level filtering before aggregation) 3. GROUP BY (Group remaining rows into buckets) 4. HAVING (Filter grouped aggregate buckets) 5. SELECT (Evaluate expressions, column projections & window funcs) 6. DISTINCT (Deduplicate projected rows) 7. ORDER BY (Sort resulting rows) 8. LIMIT / OFFSET (Slice returned row range) **Why can't you use a SELECT alias in a WHERE clause?** Because `WHERE` runs at step #2, before `SELECT` (step #5) has projected and assigned the alias! ## 2. Most Frequently Asked Practical SQL Queries (With Code) ### Q1. Find the Nth highest salary in an Employee table without skipping ranks. Using `DENSE_RANK()` inside a CTE handles ties gracefully without missing consecutive ranks. ``` WITH RankedSalaries AS ( SELECT id, name, salary, DENSE_RANK() OVER (ORDER BY salary DESC) as rnk FROM Employee ) SELECT salary FROM RankedSalaries WHERE rnk = 2; -- Change 2 to N ``` ### Q2. Find duplicate email addresses in a Users table and delete all duplicates keeping the lowest ID. ``` -- 1. Find duplicates: SELECT email, COUNT(*) FROM Users GROUP BY email HAVING COUNT(*) > 1; -- 2. Delete duplicates keeping lowest id: DELETE FROM Users WHERE id NOT IN ( SELECT MIN(id) FROM (SELECT * FROM Users) as temp GROUP BY email ); ``` ### Q3. Write a query to calculate month-over-month revenue growth percentage. ``` WITH MonthlySales AS ( SELECT DATE_TRUNC('month', order_date) as order_month, SUM(amount) as current_month_revenue FROM Orders GROUP BY DATE_TRUNC('month', order_date) ) SELECT order_month, current_month_revenue, LAG(current_month_revenue, 1) OVER (ORDER BY order_month) as prev_month_revenue, ROUND( ((current_month_revenue - LAG(current_month_revenue, 1) OVER (ORDER BY order_month)) / LAG(current_month_revenue, 1) OVER (ORDER BY order_month)) * 100, 2 ) as mom_growth_pct FROM MonthlySales; ``` ## 3. Window Functions: RANK vs DENSE_RANK vs ROW_NUMBER Window functions perform calculations across a set of table rows related to the current row without collapsing rows like `GROUP BY`. Salary ROW_NUMBER() RANK() DENSE_RANK() $100,000 1 1 1 $90,000 2 2 2 $90,000 3 2 2 $80,000 4 4 (skips 3) 3 (continuous) ## 4. Indexing, EXPLAIN Plans & Query Optimization Writing queries is only half the battle; ensuring queries run in milliseconds on millions of records is what senior engineering interviewers evaluate. - **Clustered Index:** Dictates the physical storage order of data rows on disk. Only one per table (usually the Primary Key). - **Non-Clustered Index:** A separate B+Tree data structure holding sorted indexed columns with pointers back to the actual data rows. - **Covering Index:** An index containing all columns requested by a `SELECT` query, eliminating disk lookup hops (Heap/Clustered lookups). - **SARGable Queries:** Ensure filtering conditions can leverage indexes (e.g., avoid `WHERE YEAR(created_at) = 2026`; instead use `WHERE created_at >= '2026-01-01' AND created_at < '2027-01-01'`). ## 5. Transactions, ACID Properties & Concurrency Anomalies **ACID Properties:** #### Atomicity All-or-nothing execution. If any operation fails, the entire transaction rolls back. #### Consistency Transactions transition database states strictly adhering to constraints and integrity rules. #### Isolation Concurrent transactions execute independently without interfering with in-flight states. #### Durability Committed updates persist permanently in Write-Ahead Logs (WAL), surviving system crashes. ## 6. Normalization (1NF to BCNF) vs Denormalization Normalization minimizes data redundancy and eliminates insertion, update, and deletion anomalies: #### Normalization Normal Forms - **1NF:** Atomic values only; no repeating column groups or array lists. - **2NF:** In 1NF + no partial dependency on composite candidate keys. - **3NF:** In 2NF + no transitive dependencies (non-prime attributes depend ONLY on candidate keys). - **BCNF:** For every functional dependency `X -> Y`, `X` must be a super key. #### When to Denormalize? In high-read analytical databases (OLAP / Data Warehouses), denormalization avoids costly multi-table joins and drastically speeds up dashboard queries at the cost of duplicate storage. ### Ready to Practice SQL Technical Rounds? Practice voice-based SQL mock interviews with Sophia AI. Speak your queries and explanation logic out loud and receive instant grading on syntax, normalization, and optimization. [ Start Free SQL Mock Interview ](/practice/sql-database-mock-interview) [ Data Engineering Mock ](/practice/data-engineering-mock-interview) ## 7. Frequently Asked SQL Interview Questions What is the difference between WHERE and HAVING in SQL? `WHERE` filters individual rows *before* any grouping or aggregation takes place. `HAVING` filters grouped summary rows *after* the `GROUP BY` clause is evaluated and supports aggregate functions like `SUM()`, `COUNT()`, or `AVG()`. What is the difference between DELETE, TRUNCATE, and DROP? `DELETE` is a DML command that removes rows one-by-one, logs each deletion, and supports `WHERE` clauses and rollback. `TRUNCATE` is a DDL command that deallocates entire data pages, resetting identity counters much faster without row-by-row logging. `DROP` removes the entire table definition and its schema from the database completely. --- ### Practice Live with Sophia AI Practice realistic voice technical interviews tailored directly to your real resume at https://ratemyinterview.online. Sophia AI speaks and listens via your microphone with sub-second speech latency (~900ms), giving instant objective scores, full transcripts, and question-by-question model solutions.