Never Go to SQL Interviews Without Knowing These Advanced Questions

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SQL interviews often test your ability to handle complex queries, optimize performance, and design scalable databases. Below are key questions and practical solutions to help you prepare.

1. Using Window Functions for Moving Average

SELECT 
date,
sales,
AVG(sales) OVER (
PARTITION BY product_id 
ORDER BY date 
ROWS BETWEEN 2 PRECEDING AND CURRENT ROW
) AS moving_avg
FROM sales_data;

– Purpose: Calculates a 3-day moving average per product.
– Key Functions: AVG() OVER(), PARTITION BY, ROWS BETWEEN.

2. Finding Nth Highest Salary Using CTE

WITH RankedSalaries AS (
SELECT 
salary,
DENSE_RANK() OVER (ORDER BY salary DESC) AS salary_rank
FROM employees
)
SELECT salary
FROM RankedSalaries
WHERE salary_rank = 3; -- Replace 3 with desired rank

– Why CTE? Improves readability and reusability.

3. Optimizing Slow-Running Queries

Steps:

1. Check Execution Plan (`EXPLAIN ANALYZE` in PostgreSQL).

2. Add Indexes on frequently filtered columns:

CREATE INDEX idx_customer_id ON orders(customer_id);

3. Avoid `SELECT ` – Fetch only required columns.
4. Use Query Hints (e.g., `/+ INDEX /` in Oracle).

4. Designing an E-Commerce Database Schema

Key Tables:

– `users` (user_id, name, email)
– `products` (product_id, name, price)
– `orders` (order_id, user_id, order_date)
– `order_items` (order_item_id, order_id, product_id, quantity)

Scalability Tips:

  • Sharding by user_id.
  • Partitioning `orders` by order_date.

5. Identifying Performance Bottlenecks

  • Check for:
  • Full table scans (WHERE without indexes).
  • Nested loops in joins.
  • Excessive subqueries (replace with CTEs).

6. Implementing ACID Properties

BEGIN TRANSACTION;
UPDATE accounts SET balance = balance - 100 WHERE user_id = 1;
UPDATE accounts SET balance = balance + 100 WHERE user_id = 2;
COMMIT;

– Atomicity: All or nothing.
– Isolation: `SET TRANSACTION ISOLATION LEVEL SERIALIZABLE;`

7. Optimizing Multi-Join Queries

  • Use Indexes on join keys.
  • Replace Subqueries with CTEs.
  • Limit Result Sets with LIMIT.

8. OLTP vs. OLAP Query Structures

  • OLTP (Transactional): 
    SELECT  FROM orders WHERE order_id = 123;
  • OLAP (Analytical): 
    SELECT region, SUM(sales) FROM sales_data GROUP BY region;

9. Partitioning & Sharding Large Datasets

  • Partitioning by Range: 
    CREATE TABLE logs (
log_id INT,
log_date DATE
) PARTITION BY RANGE (log_date);
  • Sharding: Distribute data across servers (e.g., by user_id).

10. Advanced Aggregation (ROLLUP, CUBE)

SELECT 
region, 
product, 
SUM(sales) AS total_sales
FROM sales
GROUP BY ROLLUP(region, product);

– Output: Sales by region, product, and grand total.

You Should Know:

Essential SQL Commands for Interviews

-- Find duplicates
SELECT email, COUNT() FROM users GROUP BY email HAVING COUNT() > 1;

-- Pivot data (PostgreSQL)
SELECT 
FROM crosstab('SELECT product, month, sales FROM sales_data') 
AS (product TEXT, jan INT, feb INT, mar INT);

-- Recursive CTE (for hierarchies)
WITH RECURSIVE org_tree AS (
SELECT id, name, manager_id FROM employees WHERE id = 1
UNION ALL
SELECT e.id, e.name, e.manager_id FROM employees e
JOIN org_tree ot ON e.manager_id = ot.id
)
SELECT  FROM org_tree;

Linux Commands for Database Admins

 Monitor PostgreSQL queries
pg_top -U postgres

Backup a MySQL database
mysqldump -u root -p db_name > backup.sql

Check disk I/O (for performance tuning)
iotop -o

Windows Commands for SQL Server

 Start SQL Server service
net start MSSQLSERVER

Check active connections
sqlcmd -Q "SELECT spid, status, loginame FROM sys.sysprocesses"

What Undercode Say:

Mastering SQL requires hands-on practice. Use Docker to run databases locally (docker run postgres) and experiment. Always analyze query performance and understand indexing trade-offs.

Expected Output:

  • Optimized SQL queries.
  • Efficient database design.
  • Performance tuning skills.

Prediction:

SQL will remain critical in data engineering, but AI-assisted query optimization (like OpenAI’s Codex) will rise.

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