SQL Problems for Data Science

The SQL skills are evaluated by a broad selection of questions in the field of data science interviewing, which measure the lower-level knowledge and problem-solving ability. Interviewers usually pay attention to the ability of the candidates to write efficient queries, clean and manipulate data, use aggregation functions, and operate joins and subqueries to derive significant conclusions.

Besides, the optimization of performance and work with huge amounts of data are often tested because these aspects are crucial in the real world. Learning these concepts not only prepares candidates to respond to interview questions but also helps them to use SQL effectively in performing their daily data analysis and decision-making processes.

SQL usage in data science by role

Not all data science roles use SQL in the same way. Some positions focus more on data architecture and ETL processes, while others emphasize writing queries and optimizing them for performance. In data science interviews, SQL questions are typically grouped into the following categories based on how SQL is used in real-world tasks.

Data Definition Language (DDL)

• CREATE
• ALTER
• DROP
• RENAME
• TRUNCATE
• COMMENT

Data Manipulation Language (DML)

• INSERT
• UPDATE
• DELETE
• MERGE
• CALL
• EXPLAIN PLAN
• LOCK TABLE

Data Query Language (DQL)

• SELECT

Data Control Language (DCL)

• GRANT
• REVOKE

In most cases, data scientists and analysts primarily work with the SELECT statement, along with advanced SQL concepts such as Common Table Expressions (CTEs), grouping, rollups, window functions, and sub queries. If you are already working as an analyst, you are likely using SQL regularly in your day-to-day tasks.

Entry-Level SQL Interview Questions (Data Science)

These questions will assess your understanding of simple filters, aggregations, sorts, and joins as simple SQL constructs. You can comfortably search datasets and derive easily readable insights, which interviewers use to verify this.

1. Write a query to get the total number of customers

This question will provide a test of your knowledge of the fundamental aggregation in SQL. It assesses your ability to count rows in a table properly, which is frequently needed to verify the size of the dataset or user expansion. Such a query is mostly employed in dashboards and summary reports.

Hint: Discuss the difference between COUNT(*) and COUNT(column name) in order to show an understanding of the treatment of NULL values.

2. Fetch the top 5 highest-paid employees.

This question is a test of sorting and limiting results. It verifies the ability to sort records by a numeric column and limit the results to a specified number of rows. The trend is common in performance evaluation and ranking reports.

Hint: It is mentioned that the SQL syntax varies across platforms (LIMIT vs TOP), which demonstrates real-world knowledge of SQL.

3. Find the number of unique users who logged in during the month of January 2026

This question is a test of event-based filtering and deduplicated counting. It helps to check whether it is possible to separate user actions on a certain date, and that only once the user is counted. Such analysis is typical in the analysis of engagement and campaign assessment.

4. Identify all those customers who are not making orders

This question is a challenge to your knowledge of joins and missing data processing. It verifies the ability to find users whose associated activity is nonexistent, and this is a common necessity of conversion funnel analysis.

Hint: Be descriptive on why a LEFT JOIN is needed and why the null values signify a lack of a match.

5. Get the highest salary record of the employees table.

This query tries a basic aggregation logic by using MAX. It tests the ability to get the best out of a column, which is a basic requirement of the more advanced grouped query.

Hint: state that more logic is necessary in case employee information is also needed.

Intermediate SQL Interview Questions (Data Science)

This set of questions is grouped into analysis, conditional logic questions, time-based filtering, and multiple table combination questions. They are reflective of the kind of queries that are written by data scientists in actual analytical processes.

1. Determine the highest salary with departmental grouping

This question is a test of grouped aggregation and categorical analysis. It tests the ability to compute department-level summaries, commonly used in both organisational and compensation analysis.

Hint: State how window functions can be used when the interviewer requires the name of the employee and the salary.

2. Find users who have had fewer than 3 orders or a total expenditure of less than $500

This question is a conditional logical question over aggregated data. It verifies that you are able to map business rules to SQL using HAVING. This finds extensive application in customer segmentation and customer retention modelling.

Hint: It is important to stress the distinction between pre-aggregating (WHERE) and post-aggregating (HAVING).

3. Return the overall distance covered by an individual user

These questions are combined with numerical aggregation. It tests whether you are able to combine user and activity data to come up with meaningful metrics, including total engagement or usage.

Hint: When referring to measuring units, also say that you verified them to prevent the confusion of findings.

4. Identify customers who made orders in 2023 and 2024

This query is a test of time-based grouping and set logic. It also tests the ability to find repeat customers across a series of years, and this is very important in retention and loyalty analysis.

Hint: COUNT(DISTINCT YEAR(orderdate)) is easier to understand and makes the query easier to read.

5. Prepare a query to respond to several transaction-related questions

Multi-metric aggregation and conditional expressions are tested in this question. It also verifies the ability to compute multiple KPIs, including transaction count, revenue, and status metrics in one query (like actual dashboards would).

Hint: Be mindful of clear aliases and formatting using the structured form of query to make it easy to read and define.

Advanced-Level SQL Interview Question (Data Science)

These questions involve more serious analytical thinking, which involves window functions, ranking logic, time windows, and data validation. They usually apply to the positions of data scientist and senior analyst.

1. Select the 2nd largest salary in the department

This question involves advanced ranking logic and duplicate handling. It is to judge whether you can properly determine the second different value when more than one employee has the same salary.

Suppose you are given two tables:

• Employee (e_id, name, salary, d_id)
• Department (id, name)

The id column in the Department table is linked to the d_id column in the Employee table.

Because d_id refers to the primary key (id) of another table, it is called a foreign key. A foreign key establishes a relationship between two tables.

Using this relationship, we can apply an INNER JOIN to combine both tables based on the common field. This allows us to retrieve employee details along with the names of the departments in which they work.

2. Determine the largest wireless packets transmitted on the SSID in a timeframe.

This question is a test of time-window analysis with aggregation. It verifies the possibility to filter the events in a specified time frame and calculate the maximum values per group.

Hint: Before typing in the query, always make sure to check the time zones and time stamp formats.

3. Identify duplicate entries in a huge transactions table

This query is a quality and validation test. It determines the possibility of finding duplicate records that can skew metrics or draw the wrong conclusion.

Hint: Suggest that it is more effective to be careful about duplicates in the ETL process rather than correcting them at the end of the chain.

4. Write a query to determine the third purchase of each user

This query is used to test the functions of Windows and the sequence of events. It examines the ability to rank user events in order and derive a particular milestone of the customer lifecycle.

5. Calculate cumulative users added to the day with monthly resets

This question is an advanced window framing and partitioning test. It tests the ability to calculate running totals that roll over depending on the time period commonly used in growth and cohort reporting.

Hint: Check the boundaries and order of partitions so that cumulative counts start at the right place at the beginning of each month.

SQL Interview questions and answers

Below are some commonly asked SQL interview questions that can help you prepare effectively for your interview:

1) What is SQL?

SQL is an abbreviation of Structured Query Language. It is a standard computer programming language, which is used to communicate with relational database management systems (RDBMS). SQL enables users to access, add, modify, and remove data in tables of the database.

2) What is an SQL statement?

An SQL statement or SQL command is a sequence of commands that are written in SQL and then interpreted and executed by the database engine. These statements are executed to accomplish a given operation on a database, such as retrieving data, updating data, or operating on database objects. Usually, they are SELECT, CREATE, DELETE, DROP, and REVOKE.

3) What is an SQL query?

A SQL query is a SQL statement that has been coded to access or update information held in a database. Queries are mostly employed to manipulate instead of organise the database structure.

SQL queries have two major classes:

• Data retrieval queries. It is a query used to retrieve data and can be filtered, grouped, ordered, or joined with multiple tables.
• Queries to modify data, which are needed to insert, update, delete, or rename data in tables.

4) What is an SQL subquery?

An SQL subquery or an inner query is a query that is contained within another SQL query, which is referred to as the outer query. The subqueries may be used in clauses like SELECT, FROM, WHERE, or UPDATE.

A subquery may also have one more subquery inside it. The deepest subquery is first run and the result transmitted to the outer query to process further.

5) What is an SQL join?

SQL join is a statement that is used to merge records of two or more tables, depending on a related column between them. Joins enable you to access valuable information that is related to one another, including corresponding customer information with their orders.

6) What is an SQL comment?

An SQL comment is a human-understandable explanation of what a given section of SQL code does. The SQL engine does not take into account comments, and they do not influence the results of a query.

SQL supports:

• One-line remarks, beginning with the --.
• Multi-line comments, consisting of a comment between / /.
• Comments enhance the readability of the code and simplify the SQL scripts to be easier to comprehend and maintain in the future.

7) What is a unique key in SQL?

A unique key is a column or a combination of columns, under which a UNIQUE constraint has been placed in order to ensure that the values in that column are unique. This limit is useful in ensuring that the integrity of data is preserved by eliminating repetition of values. Depending on the database system, a unique key may include NULL values.

Note:

• SQL Server: Only one permits the null value unless a filtered index is used.
• PostgreSQL / Oracle / MySQL: Multiple values of NULL can be used since NULL is not identical to another NULL.

8) What is a foreign key in SQL?

A foreign key is a column or a combination of columns that creates a linkage between two tables. It connects a column in one table with the primary key or the unique key in another table. A foreign key is primarily used to provide referential integrity, meaning that any relationships between tables will be consistent.

9) What is an SQL index?

An SQL index is a unique data structure related to a table that enhances the speed at which data retrieval operations are carried out. References to table data are stored in indexes in a manner that enables the database engine to find records fast, making them particularly useful in large datasets and columns that are frequently used in queries.

10) What kind of indices are you aware of?

• Unique Index: Maintains a unique value of all entries at the indexed column, and this is useful in preserving data integrity by ensuring that no duplicate values exist.
• Clustered Index: Defines the physical storage sequence of information in a table. The fact that only one physical order is possible when storing the data in a table means that a table can only contain a single clustered index.
• Non-Clustered Index: The indexing of data is done separately from the table data. It also has a logical hierarchy that is not the same as the physical storage, which can have several non-clustered indexes on one table.

11) What is a schema?

A schema is a logical construct that stores the database objects, including tables, views, indexes, stored procedures, functions, and triggers. It determines the general format of a database, illustrates the relationships between the objects, and aids in the management of access control and permissions.

12) What is an SQL operator?

A SQL operator is a symbol or a keyword that is used to perform operations on data in an SQL query. The WHERE clause may also include operators that are typically used to specify the conditions to filter the data and may either present comparisons, logical assessment, or arithmetic operations.

Key SQL Skills Every Data Scientist Should Master

QL is an essential tool for data scientists because most real-world data resides in relational databases. Mastering SQL allows data scientists to extract, clean, manipulate, and analyse data efficiently. Here are the key SQL skills every data scientist should focus on:

• SELECT, WHERE, GROUP BY
• JOIN operations (INNER, LEFT, RIGHT)
• Window functions
• Subqueries and CTEs
• Data cleaning functions
• Aggregations and statistics
• Query optimization

Conclusion

SQL plays a critical role in the daily work of data scientists and analysts. Before any modeling, visualization, or advanced analytics can happen, data must first be extracted, cleaned, transformed, and analyzed. SQL is the primary tool used to perform these tasks efficiently. Therefore, SQL is a crucial skill for a data scientist, as it provides the possibility to explore the data and is an essential factor in data-driven decision-making.