SQL Interview Question | 30 |#dataengineering |#dataanalyst |#powerbi | #sql #dataengineer

SQL Interview Question #30 | SQL JOINs Can you correctly calculate the number of rows returned by different SQL JOINs when the tables contain duplicate IDs and NULL values? In this SQL interview question, we work with two simple tables — Employee and Product — and determine the result of different JOIN operations. In this video, we cover: INNER JOIN row count LEFT OUTER JOIN row count RIGHT JOIN row count How duplicate values affect JOIN results How NULL values behave in SQL JOINs A simple approach to solving SQL JOIN interview questions Scripts; -- Create the employees table CREATE TABLE emp_tbl ( id INT, emp_name VARCHAR(50) ); -- Insert 4 rows with the requested IDs (1, 0, NULL, 1) INSERT INTO emp_tbl (id, emp_name) VALUES (1, 'Alice'), (0, 'Bob'), (NULL, 'Charlie'), (1, 'Diana') -- Create the product table CREATE TABLE c ( id INT, product_name VARCHAR(50) ); truncate table product -- Insert 4 rows with data INSERT INTO product (id, product_name) VALUES (0, 'Laptop'), (NULL, 'Wireless Mouse'), (1, 'Mechanical Keyboard'), (1, 'Monitor'); This is a great SQL interview problem for Data Engineers, Data Analysts, BI Developers, and SQL Developers preparing for technical interviews. 💡 Interview Tip: Don't just count the matching IDs. When a value appears multiple times, one row from one table can match multiple rows from the other table. Always think about the actual row combinations produced by the JOIN. Try solving the question yourself before watching the explanation! If you found this question useful, Like 👍, Subscribe, and follow for more SQL interview questions and Data Engineering concepts. Topics covered SQL JOINs | INNER JOIN | LEFT JOIN | RIGHT JOIN | NULL values | Duplicate rows | SQL Interview Questions | Data Engineering | Data Analytics | Power BI #sql #sqlinterview #dataengineering #dataanalyst #dataengineer #powerbi #sqljoins #sqlquestions #dataanalytics #interviewquestions #viral #viralvideo #datascience #database #youtube #databricks #apachespark #pyspark #python