Ultimate Guide: Business Analyst SQL Interview Questions

Tim S

Tim S

Tim currently works at FAANG as a data analyst. He's been in the industry for 5+ years and writes for Big Tech Interviews on the weekends. His favorite language is SQL and calls Denver, CO home.


In today’s fast-paced, data-driven world, Business Analysts play a crucial role in turning data into smart decisions. Whether you’re coming from a data engineering, data science, or business analyst field, SQL proficiency is a game-changer. It’s not just about learning a language; it’s about unraveling complex data, discovering insights, and shaping business strategies. This guide readies you for SQL interviews, offering tips to ace questions from beginner to expert levels. Get interview-ready and boost your skills to stand out as an essential business analyst in our data-centric economy.

Table of Contents

  • Easy Business Analyst SQL Interview Questions
  • Intermediate Business Analyst SQL Interview Questions
  • Hard Business Analyst SQL Interview Questions
  • Conclusion

Easy Business Analyst SQL Interview Questions

Entering the SQL interview as a Business Analyst may initially appear formidable, yet it’s akin to stepping into a new arena where the potential for growth is enormous. These so-called “easy” questions serve as the gateway to understanding SQL’s fundamental aspects, focusing on basic queries, simple functions, and introductory data manipulation techniques. These questions are less of a barrier and more of an opportunity to display your understanding of SQL’s basic principles, setting a solid foundation for the more intricate queries that follow.

Sample Questions and Answers

Q: What is SQL and why is it crucial in the field of data management?

A: SQL, or Structured Query Language, is the standard language for managing and manipulating databases. It is essential because it enables users to create, read, update, and delete database records, making it a fundamental skill for navigating today’s data-rich environments efficiently.

Q: Explain the difference between the `DELETE` and `TRUNCATE` commands in SQL.

A: The `DELETE` command is used to remove specific records from a table, allowing the possibility to filter which records to delete. `TRUNCATE`, however, instantly removes all records from a table without maintaining any logs of the deleted records, making it faster but irreversible.

Q: Describe the purpose of a primary key in a SQL database.

A: A primary key is a unique identifier for each record in a SQL database table, ensuring data integrity by preventing duplicate entries. It serves as a critical reference point for establishing relationships between tables in the database.

Q: What are SQL `JOIN` operations and their different types?

A: SQL `JOIN` operations are used to combine records from two or more tables in a database, based on related columns between them. The main types are `INNER JOIN`, which selects records with matching values in both tables; `LEFT JOIN` and `RIGHT JOIN`, which select all records from one table and matching records from another; and `FULL JOIN`, which selects all records when there is a match in either table.

Q: When is a subquery used in SQL?

A: A subquery, which is a query within another query, is used when performing operations that require a second level of querying, such as filtering results based on the outcome of another query. It’s particularly useful for breaking down complex problems into manageable parts, allowing for more precise data extraction.

Q: Meta SQL Question: Two different products

Your manager at Meta asks you to write a query that returns the name of brands that have at least two different products and its average price is greater than $3. Return the result order by brand name.

					table: products
|1         |1               |brand_1   |product_1   |4    |
|2         |1               |brand_2   |product_2   |2    |
|3         |2               |brand_2   |product_3   |9    |
|4         |2               |brand_3   |product_4   |1    |
|5         |3               |brand_3   |product_5   |5    |

For this problem, we need to aggregate by brand name and look for the ones that have count(distinct product id) greater or equal than 2, and whose average(price) is greater than 3. This requires basic knowledge on how group by works.

					select p.brand_name
from products p
group by p.brand_name
having avg(p.price) > 3 and count(distinct p.product_id) >= 2
order by p.brand_name;

Intermediate Business Analyst SQL Interview Questions

The intermediate SQL interview questions for Business Analysts go beyond the basics and delve into more complex concepts, requiring a deeper understanding of data manipulation, querying, and database management. These questions aim to gauge your ability to apply SQL functions and techniques in real-world scenarios, showcasing your proficiency in using SQL as a powerful data analysis tool.

Sample Questions and Solutions

Q: Explain the difference between a `UNION` and a `UNION ALL` statement in SQL.

A: `UNION` combines the results of two or more SELECT statements, removing any duplicate rows. On the other hand, `UNION ALL` includes all rows from each SELECT statement, including duplicates. Therefore, if you want to remove duplicate records, use

Q: What is an `INDEX` in SQL and why is it used?


A: An `INDEX` in SQL is a performance tuning method of allowing faster retrieval of records from the table. It is used to speed up the search queries by providing quick access to rows in the data tables, similar to indexes in books.

Q: Describe the `GROUP BY` clause in SQL.

A: The `GROUP BY` clause in SQL is used with aggregate functions (like `SUM`, `AVG`, `MAX`, etc.) to group the result set by one or more columns. It is useful for obtaining aggregated data by grouping records based on one or more columns.

Q: How does the `HAVING` clause differ from the `WHERE` clause in SQL?

A: The `HAVING` clause is used to filter rows after an aggregation process has been performed by `GROUP BY`, essentially applying a filter to grouped rows. The `WHERE` clause filters rows before the aggregation process. Thus, `HAVING` is used when working with aggregates that `WHERE` cannot filter.

Q: Explain the concept of `NORMALIZATION` in a database.

A: `NORMALIZATION` is a database design technique that reduces data redundancy and improves data integrity by dividing large tables into smaller tables and linking them using relationships. It involves organizing fields and tables of a database to minimize dependency and redundancy.

Q: What does a `LEFT JOIN` return?

A: A `LEFT JOIN` returns all records from the left table (table1), and the matched records from the right table (table2). The result is NULL from the right side, if there is no match.

Q: Amazon SQL Question: Bids by category in the last 7 by day of week

Your area manager submits an analytics ticket to understand how many bids have been completed in each category for each day of the week ordered by day of week in ASC order.

					Table: bids
|bid_id|customer_id| order_datetime     |item_id|order_quantity|
|A-001 |32483      |2021-12-15 09:15:22 |B000   |3             |
|A-002 |21456      |2022-01-10 09:28:35 |B001   |1             |
|A-003 |21456      |2022-01-09 09:28:35 |B005   |1             |
|A-004 |42491      |2022-01-16 02:52:07 |B008   |2             |
|A-005 |42491      |2022-01-18 02:52:07 |B008   |2             |
|A-006 |42491      |2022-01-18 02:52:07 |B008   |5             |
|A-007 |21456      |2022-01-17 09:28:35 |B000   |1             |
|A-008 |21456      |2022-01-17 10:28:35 |B008   |3             |
|A-009 |21456      |2022-01-19 10:28:35 |B000   |2             |

Table: items
| item_id | item_category |
|B000     |Outdoors       |
|B001     |Outdoors       |
|B002     |Outdoors       |
|B003     |Kitchen        |
|B004     |Kitchen        |
|B005     |Kitchen        |
|B006     |Bedroom        |
|B007     |Bedroom        |
|B008     |Bedroom        |

With a simple query, we’re able to dissect the data efficiently by aggregating across two key dimensions. By employing `TO_CHAR()` to extract the day name from `orderdatetime`, and `INITCAP()` to ensure the day names are capitalized, we refine our analysis. Further, grouping by these specified dimensions and quantifying bids by `bid_id`, we deepen our insights. A join operation between the `items` and `bids` tables through their shared `itemid` column permits us to categorize each bid according to `itemcategory`. Lastly, an `ORDER BY 2` command streamlines the sorting process based on the second column, avoiding redundancy in our query execution.

,initcap(to_char(b.order_datetime,'day')) day_of_week
from bids b
left join items i
on i.item_id=b.item_id
group by
order by 2 asc

Hard Business Analyst SQL Interview Questions

The hard SQL interview questions for Business Analysts challenge the candidate’s ability to solve complex data analysis problems. These questions test the depth of understanding in advanced SQL concepts, database optimization, and data manipulation strategies. They are designed to assess the candidate’s expertise in handling real-life data scenarios, requiring a high level of proficiency and strategic thinking.

Sample Questions and Solutions

Q: How can window functions be utilized in SQL for complex data analysis? Provide an example.

A: Window functions in SQL enable complex calculations across a set of rows related to the current row. Unlike standard aggregation functions, they do not collapse the rows into a single output row. Common window functions include `ROW_NUMBER()`, `RANK()`, and `SUM()`. For example, `ROW_NUMBER() OVER (PARTITION BY department ORDER BY salary DESC)` assigns a unique rank within each department based on salary.

Q: Explain the process and significance of `CROSS APPLY` and `OUTER APPLY` in SQL Server.

A: `CROSS APPLY` and `OUTER APPLY` are used in SQL Server to join a table to a table-valued function. `CROSS APPLY` returns only rows from the outer table that produce a result set from the table-valued function. `OUTER APPLY` returns all rows from the outer table, and if the table-valued function does not return a result, NULL values are returned for all columns from the function.

Q: Discuss the difference between `TEMPORARY TABLES` and `TABLE VARIABLES`. In what scenarios would each be preferable?

A: `TEMPORARY TABLES` are suitable for large operations and complex queries, where indices are necessary, or when transactions span multiple operations. `TABLE VARIABLES` are used for smaller, quick operations where simplicity and minimal logging are beneficial. `TEMPORARY TABLES` can be indexed and have statistics, while `TABLE VARIABLES` do not.

Q: Describe how to implement pagination in SQL queries. What are the performance considerations?

A: Pagination can be implemented using the `OFFSET` and `FETCH NEXT` clauses. For example, `SELECT * FROM Orders ORDER BY OrderID OFFSET 10 ROWS FETCH NEXT 10 ROWS ONLY` retrieves rows 11-20. Performance considerations include avoiding large offsets (as performance degrades) and leveraging indexes to speed up the order by operations.

Q: Explain the concept of `CTE (Common Table Expressions)` and its advantages over a traditional `SUBQUERY`.

A: A `CTE` is a temporary result set that you can reference within a SELECT, INSERT, UPDATE, or DELETE statement. `CTEs` provide better readability and can be recursively defined, allowing for complex queries like hierarchical tree structures. Unlike subqueries, `CTEs` can be self-referencing and can be referenced multiple times in the same query.

Q: What are `Non-Clustered Indexes` and how do they differ from `Clustered Indexes`? Include their impact on SQL query performance.

A: `Non-Clustered Indexes` store data separately from the table data, linking to the actual table rows by pointers, enabling faster searches on the indexed columns. `Clustered Indexes`, however, sort and store the data rows of the table based on their key values. While `Clustered Indexes` improve read operations by storing rows in sorted order, `Non-Clustered Indexes` can speed up access to data when searching for values within the indexed columns.

Q: Detail the process of converting a `JSON` string to a structured format in SQL Server.

A: SQL Server allows parsing `JSON` strings into a structured format using the `OPENJSON` function. For example, `SELECT * FROM OPENJSON(@json)` where `@json` contains the `JSON` string. This function transforms the `JSON` string into a set of rows and columns, which can then be used in standard SQL queries.

Q: Explain the difference between `RANK()`, `DENSE_RANK()`, and `ROW_NUMBER()` functions in SQL with practical examples.

A: `RANK()` assigns a unique rank for each row within a partition with gaps in rank values in case of ties. `DENSE_RANK()` also ranks within a partition but without gaps. `ROW_NUMBER()` assigns a distinct number to each row, starting at 1. For example, if two rows tie for first place, `RANK()` would assign both as 1 with the next rank as 3, `DENSE_RANK()` would also assign both as 1 but with the next rank as 2, and `ROW_NUMBER()` would arbitrarily assign one of them as 1 and the other as 2.

Q: Describe how to use SQL to identify and resolve deadlocks in database transactions.

A: To identify deadlocks, one can use SQL Server’s built-in system stored procedures such as `sp_who2` or the SQL Server Profiler to monitor and log event types related to locking. To resolve deadlocks, ensure transactions are as short as possible, avoid user interactions mid-transaction, access resources in a consistent order, or use locks at appropriate levels to minimize locking contention.

Q: Discuss the best practices for executing dynamic SQL within stored procedures. What are the security concerns?

A: When executing dynamic SQL, use parameterized queries or stored procedures to avoid SQL injection attacks. Ensure proper validation of input values. Utilize the `sp_executesql` stored procedure instead of `EXECUTE` for better performance and security. Understand and limit the permissions of the executing context to only what is necessary, mitigating the risk of malicious SQL execution.

Q: Robinhood SQL Question: Number of seats available

Your manager is concerned about the low flight bookings and wants you to write a query that shows us the number of seats not yet purchased for each flight_id.

					Table: flights
|1        |11      |
|2        |12      |
|3        |13      |
|4        |14      |
|5        |15      |

Table: planes
|11      |15             |
|12      |30             |
|13      |40             |
|14      |50             |
|15      |35             |

First, we’ll start by calculating the total purchases for each flight. Following this, we’ll merge the plane data with the flight information to accurately determine the number of seats available. The final step involves integrating our purchase data to calculate the remaining seats by subtracting the number of purchases from the available seats.

					with purchased as (
,count(flight_id) seats_purchased
from purchases
group by flight_id

,pl.number_of_seats-pu.seats_purchased number_of_seats_not_yet_purchased
from flights fl
left join planes pl
on pl.plane_id=fl.plane_id
left join purchased pu
on pu.flight_id=fl.flight_id
order by fl.flight_id asc;


SQL is a powerful and versatile language for managing, querying, and analyzing large datasets in relational databases. With its various features such as window functions, CTEs, and indexes, it enables developers to perform complex data operations efficiently. However, understanding best practices and potential pitfalls is crucial to utilizing SQL effectively. By constantly learning and leveraging SQL cheat sheets like this one, you can rapidly advance your SQL skills. So, it is essential to continue learning and exploring new features and techniques in SQL to enhance our skills as database professionals. There will always be more to learn and discover in SQL, making it an exciting field for data enthusiasts.

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