Top 25 Interview Q&A for SQL (Basic to Advanced) for Data Analytics

SQL (Structured Query Language) is the backbone of Data Analytics, enabling analysts to extract, manipulate, and analyze data stored in relational databases. Whether you are a fresher, business analyst, or experienced data professional, SQL interview questions often range from basic querying concepts to advanced analytical techniques.

This blog covers Top 25 SQL Interview Questions and Answers, progressing from basic fundamentals to advanced queries, with explanations tailored specifically for data analytics roles.

1. What is SQL and why is it important for Data Analytics?

SQL is a standard language used to interact with relational databases. In data analytics, SQL is crucial for retrieving datasets, filtering records, aggregating metrics, and preparing data for visualization and reporting. It allows analysts to convert raw data into meaningful insights efficiently.

2. What are the different types of SQL commands?

SQL commands are categorized into:

• DDL (Data Definition Language): CREATE, ALTER, DROP
• DML (Data Manipulation Language): INSERT, UPDATE, DELETE
• DQL (Data Query Language): SELECT
• DCL (Data Control Language): GRANT, REVOKE
• TCL (Transaction Control Language): COMMIT, ROLLBACK, SAVEPOINT

3. What is a primary key?

A primary key uniquely identifies each record in a table. It cannot contain NULL values and must be unique. In analytics, primary keys help ensure data integrity and enable accurate joins between tables.

4. What is the difference between WHERE and HAVING?

• WHERE filters rows before aggregation.
• HAVING filters aggregated results after GROUP BY.

5. What is NULL in SQL?

NULL represents missing or unknown data. It is different from zero or an empty string. In analytics, handling NULL values properly is important to avoid incorrect calculations.

6. What is a JOIN and why is it important?

A JOIN combines data from multiple tables based on a related column. It allows analysts to analyze relationships between datasets such as customers, orders, and products.

7. Explain different types of JOINs.

• INNER JOIN: Returns matching records from both tables
• LEFT JOIN: Returns all records from the left table
• RIGHT JOIN: Returns all records from the right table
• FULL JOIN: Returns all records from both tables

8. What is GROUP BY used for?

GROUP BY groups rows with similar values and applies aggregate functions like COUNT, SUM, AVG, MAX, and MIN. It is essential for summarizing analytical data.

9. What are aggregate functions in SQL?

Aggregate functions perform calculations on a set of values:

• COUNT()
• SUM()
• AVG()
• MIN()
• MAX()

10. What is the difference between COUNT(*) and COUNT(column)?

• COUNT(*) counts all rows including NULLs
• COUNT(column) counts only non-NULL values

11. What is a subquery?

A subquery is a query nested inside another query. It helps break down complex analytical problems into manageable steps.

12. What are correlated subqueries?

Correlated subqueries depend on the outer query for execution. They are executed row by row and are often used for row-level comparisons in analytics.

13. What are window functions?

Window functions perform calculations across a set of rows without collapsing the result set. Common functions include ROW_NUMBER(), RANK(), and SUM() OVER().

14. Difference between RANK(), DENSE_RANK(), and ROW_NUMBER()?

• ROW_NUMBER(): Unique number for each row
• RANK(): Skips ranks when there are ties
• DENSE_RANK(): Does not skip ranks

15. What is a CTE (Common Table Expression)?

A CTE is a temporary result set defined using WITH clause. It improves readability and simplifies complex analytical queries.

16. What is indexing and why is it important?

Indexes speed up data retrieval by reducing table scans. In analytics, indexes significantly improve performance when querying large datasets.

17. What is the difference between DELETE and TRUNCATE?

• DELETE: Removes specific rows and can be rolled back
• TRUNCATE: Removes all rows quickly and cannot be rolled back

18. What is normalization?

Normalization organizes data to reduce redundancy and improve data integrity. It ensures clean and consistent analytical datasets.

19. What is denormalization?

Denormalization introduces redundancy to improve query performance. Analytics systems often use denormalized tables for faster reporting.

20. What is an execution plan?

An execution plan shows how SQL processes a query. Analysts use it to optimize slow queries by identifying bottlenecks.

21. How do you handle duplicate records?

Duplicates can be removed using DISTINCT, GROUP BY, or ROW_NUMBER() with DELETE. Removing duplicates ensures accurate analytics results.

22. How do you calculate running totals?

Running totals are calculated using window functions like SUM() OVER(ORDER BY date). They are common in trend analysis.

23. How do you find the second highest value?

This can be done using subqueries, LIMIT with OFFSET, or window functions like DENSE_RANK().

24. What is pivoting in SQL?

Pivoting converts rows into columns to improve readability of analytical reports, especially for dashboards.

25. Why is SQL critical for Data Analysts?

SQL enables analysts to access, clean, transform, and summarize data directly from databases, making it a core skill for data-driven decision-making.

Conclusion

SQL remains a must-have skill for Data Analytics professionals, regardless of experience level. Mastering both basic querying and advanced analytical techniques like window functions and CTEs can significantly improve your interview success and job performance.

Preparing these Top 25 SQL Interview Questions and Answers will help you confidently handle real-world data analytics interviews and stand out as a strong candidate.