15 Essential SQL Interview Questions Every Data Professional Should Master
Years ago, when I began guiding learners through SQL interviews, I realised something interesting. Most candidates could explain basic concepts, but when the discussion moved toward slightly deeper topics—joins, window functions, indexing, performance tuning—they started losing confidence. And this gap often determined whether they got shortlisted or not.
Over time, while helping students crack interviews and supporting companies through freelance consulting, I curated a list of SQL questions that truly matter in real-world work. These are the questions that test not just your memory, but your understanding of how databases behave under pressure, how queries scale, and how business logic evolves.
What you’ll find below is a refined version of those essential SQL concepts. If you’re preparing for analytics roles, transitioning into data engineering, or simply strengthening your SQL foundations, treat these as your core checklist.
1. Difference Between INNER JOIN and LEFT JOIN
This is one of the earliest concepts I teach because it forms the backbone of relational thinking.
An INNER JOIN returns only matching records from both tables.
A LEFT JOIN returns all records from the left table and the matching ones from the right—unmatched values become NULL.
In real projects, LEFT JOIN is often used for audits, exception reports, and comparisons where missing values should remain visible.
2. Filtering Unique Records
To return distinct rows, you use the DISTINCT keyword.
Understanding uniqueness is crucial when analysing customer data, product performance, or deduplicating large datasets before modelling.
3. Purpose of GROUP BY in SQL
GROUP BY helps aggregate rows sharing the same value—for example, total sales per product or number of orders per customer.
Many dashboards and BI systems rely on accurate grouping to summarise data meaningfully.
4. Difference Between HAVING and WHERE
WHERE filters rows before grouping.
HAVING filters aggregated results after grouping.
I often see beginners misuse HAVING for non-aggregated filters, which affects performance and readability.
5. Finding the Second-Highest Salary
This classic interview question teaches candidates to think beyond simple MAX queries.
The most common solution is using a subquery or a ranking function such as DENSE_RANK.
6. What Is a CTE and Why It’s Used
A Common Table Expression (CTE) allows you to create a temporary result set that can be referenced multiple times within the same query.
CTEs improve readability, break down complex logic, and enable recursive queries.
I encourage learners to use CTEs to simplify difficult queries rather than writing long nested subqueries.
7. Removing Duplicate Rows
You can remove duplicates using ROW_NUMBER() inside a CTE, keeping only rows where the ranking equals 1.
Data quality checks in real analytics pipelines heavily rely on deduplication techniques.
8. Window Functions: RANK, DENSE_RANK, ROW_NUMBER
Window functions help in ordering, ranking, and performing analytics at the row level without collapsing data.
Modern reporting, trend analysis, and even fraud detection models rely heavily on window functions.
9. SQL Query Optimisation
Query optimisation is a top skill I teach during consulting engagements.
It includes removing unnecessary subqueries, using proper indexing, reducing function usage in WHERE clauses, and analysing execution plans.
Optimised SQL saves time, money, and computing resources.
10. Indexing and Performance
Indexes work like the index of a book, helping the database locate rows faster.
They significantly improve query performance, especially on large datasets, but must be used wisely to avoid heavy write overhead.
11. Writing Efficient Stored Procedures
Stored procedures allow you to write reusable, precompiled logic.
Efficient procedures reduce network overhead, centralise business logic, and improve performance in enterprise applications.
12. Partitioning Large Datasets
Partitioning splits large tables into smaller logical units, improving query speed and making maintenance easier.
This becomes essential when dealing with years of historical data or billions of rows.
13. Pivoting and Unpivoting Data
Pivoting transforms rows into columns, while unpivoting does the reverse.
Businesses often use these transformations for reporting, especially when datasets must be reshaped for visualisation tools.
14. Recursive Queries and Their Usage
Recursive queries are used for hierarchical data such as organisational structures, product categories, or file systems.
They help retrieve parent-child relationships efficiently.
15. Implementing Slowly Changing Dimensions (SCD)
SCD techniques preserve historical changes in dimensional tables.
In BI systems such as Power BI, Tableau, or data warehouses, SCDs ensure accurate reporting across time periods.
Final Thoughts
The more I train learners and collaborate with companies, the more convinced I become that SQL mastery is built on understanding these fundamentals deeply. If you’re aiming for a stronger data career, consistent practice with these concepts can quickly elevate your confidence and visibility.
Many of my students have cracked interviews simply by learning how to apply these concepts in real-world scenarios. Whether you’re preparing for your next role or working on improving your project performance, these SQL principles will guide you well.
If you ever need personalised guidance, structured SQL sessions, or project-level consulting support, I am always open to helping individuals and teams strengthen their data capabilities.
Comments
Post a Comment