8 Toast SQL Interview Questions (Updated 2024)

Updated on

March 2, 2024

At Toast, SQL is used quite frequently for analyzing restaurant-industry data trends and managing complex datasets involving customer transaction histories. That's why Toast often tests SQL problems in interviews for Data Science, Analytics, and & Data Engineering jobs.

To help you practice for the Toast SQL interview, this blog covers 8 Toast SQL interview questions – able to answer them all?

8 Toast SQL Interview Questions

SQL Question 1: Identify the top spending customers at Toast

Given a two tables: and , identify the top 5 customers who have the highest total spending amount in the past month. In this case, a VIP customer is defined as a user who has made significant total purchases.

Example Input:
10138206/19/2022 12:00:00
10277106/29/2022 14:00:00
10338207/18/2022 18:00:00
10492507/04/2022 09:00:00
10539607/22/2022 11:00:00
Example Input:


This query begins by joining the two tables on the field. It filters the data set down to only include orders placed in the month leading up to the current date. It then groups the data by the and calculates the total amount spent by each customer by multiplying the by the for each product in each order. The results are ordered by in descending order and limited to the top 5 customers.

To work on a similar customer analytics SQL question where you can solve it interactively and have your SQL query instantly graded, try this Walmart Labs SQL Interview Question: Walmart SQL Interview Question

SQL Question 2: Analyzing Product Reviews

Given a database of customer reviews for the products sold by a restaurant technology company, your task is to write a SQL query to find the average stars awarded to each product, grouped by month.

This will help the company understand which products are performing well over time and which ones might need improvement based on customers' feedback. The reviews have a "stars" scoring system, with 1 being the least and 5 being the most satisfied.



Here is how you might write that query in SQL using PostgreSQL flavor:


In this query, we first extract the month component from the using and use it to group our data. We also use for grouping as each group should represent data for a specific product for a certain month. Then we calculate the average stars for each group with to get the value rounded to two decimal places.

Finally, the results are sorted by and for better readability.

Pro Tip: Window functions are a popular SQL interview topic, so practice all the window function problems on DataLemur

SQL Interview Questions on DataLemur

SQL Question 3: What are some similarities and differences between unique and non-unique indexes?


While both types of indexes improve the performance of SQL queries by providing a faster way to lookup rows of data, a unique index enforces the uniqueness of the indexed columns, meaning that no duplicate values are allowed in the indexed columns. On the other hand, a non-unique index allows duplicate values in the indexed columns.

Here is an example of a unique index on the column of a table of Toast employees:

This index would ensure that no two Toast employees have the same , which could be used as a unique identifier for each employee.

Here is an example of a non-unique index on the column of the same table:

This index would not enforce uniqueness, but it could be used to improve the performance of queries that filter or sort the data based on the column. For example, if you want to retrieve all employees who were Data Analysts, the database can use the index to quickly locate and retrieve the desired records without having to search through all the employees.

Toast SQL Interview Questions

SQL Question 4: Calculate the average order value for each customer for "Toast"

Toast is a company that provides restaurant management software and point of sale (POS) systems to restaurants. For our purposes, consider that they track each order that gets processed in their system across various restaurants which also includes the total cost of the order.

As a data analyst, you've been tasked to find the average order value for each customer.

Example Input:
100150008/20/2022 13:00:002000140
100230508/21/2022 12:00:002025230
100355008/21/2022 14:00:002030120
100450008/22/2022 12:00:002000150
100530508/22/2022 16:00:002025225
Example Output:


In the above PostgreSQL query, we're calculating the average () order value for each customer. groups the results based on the unique , so each row in the output represents the average order value for a specific customer. Each row in the output shows the customer's id and their average order value calculated from all their orders.

To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for calculating highest grossing products or this Alibaba Compressed Mean Question which is similar for computing average order values.

SQL Question 5: What does do in a SQL query?

is used to combine the output of multiple statements into one big result!

For a concrete example, say you were a Data Analyst supporting the Sales Analytics team at Toast, and data on potential sales leads lived in both Salesforce CRM and Hubspot. To write a query to analyze leads created after 2023 started, across both CRMs, you could use in the following way:


SQL Question 6: Filter Customers Based On Particular String From The Database

You are given a table named as a database of a company named Toast, which contains information about customer name, ID, email, and their location. Your task is to write a SQL query to filter all the customers who are located in New York and their names start with the letter 'A'.

Example Input:
1001Alan Smithalan.smith@gmail.comNew York
1002Bob Petersbob.peters@yahoo.comLos Angeles
1003Alice Morganalice.morgan@hotmail.comNew York
1004Daniel Doedaniel.doe@yahoo.comChicago
1005Alex Greenalex.green@gmail.comNew York
Example Output:
1001Alan Smithalan.smith@gmail.comNew York
1003Alice Morganalice.morgan@hotmail.comNew York
1005Alex Greenalex.green@gmail.comNew York


With the keyword in SQL, we can search for patterns in columns. Here, we have used it to extract all names that start with 'A'. In the pattern, the '%' sign is a wildcard that can match any string of any length. The keyword is then used to acquire the names from New York. The resulting database is filtered to only include rows that fulfill both conditions.

SQL Question 7: Why is normalizing a database helpful?

Database normalization is helpful because it improves the performance, flexibility, and scalability of a database. Some specific benefits of normalization include:

  • Reducing Redundancy: Normalization helps to minimize redundancy by breaking down a larger general table into smaller, more specific tables. This can often reduce the amount of data that needs to be accessed for particular queries.

  • Improving Data Integrity: Normalization helps to ensure the integrity of the data by minimizing the risk of data inconsistencies and anomalies. It does this by establishing clear relationships between the tables via primary and foreign keys.

  • Increasing Flexibility: Normalization makes it easier to modify the structure of the database, as there is less redundancy, so it allows you to make changes to one table without affecting others. This makes it easier to adapt the database to changing business needs (and change is basically constant at Toast!)

  • Improving Performance: Normalization can improve the performance of the database by reducing the amount of data that needs to be stored and accessed in a single table. This can result in faster query times and better overall performance.

  • Easier Data Modeling: Normalization can make it easier to model the data in a logical and intuitive way, since less random data is commingled together, which improves the overall database design.

SQL Question 8: Compute Average Daily Sales

Toast is a company that provides a restaurant management platform. You are provided with data about each day's sales for multiple restaurants and you're required to write a query to calculate the average daily sales for each restaurant for the month of January, 2023.

Example Input:
Example Output:


In the above query, we first filter out the sales records for the month of January, 2023 using a simple WHERE clause. Next, we GROUP BY the restaurant_id to get the records for each restaurant separately. Finally, we compute the average sales for each group using the AVG aggregate function.

Toast SQL Interview Tips

Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Toast SQL interview is to solve as many practice SQL interview questions as you can! Besides solving the earlier Toast SQL interview questions, you should also solve the 200+ SQL Interview Questions on DataLemur which come from companies like Google, Facebook, Microsoft and Amazon. DataLemur Questions

Each exercise has hints to guide you, full answers and crucially, there is an interactive coding environment so you can instantly run your SQL query answer and have it checked.

To prep for the Toast SQL interview you can also be wise to practice interview questions from other tech companies like:

However, if your SQL coding skills are weak, forget about going right into solving questions – refresh your SQL knowledge with this free SQL for Data Analytics course.

SQL tutorial for Data Scientists & Analysts

This tutorial covers topics including working with string/text data and inner vs. outer JOIN – both of these show up frequently during SQL interviews at Toast.

Toast Data Science Interview Tips

What Do Toast Data Science Interviews Cover?

Beyond writing SQL queries, the other question categories to prepare for the Toast Data Science Interview are:

Toast Data Scientist

How To Prepare for Toast Data Science Interviews?

The best way to prepare for Toast Data Science interviews is by reading Ace the Data Science Interview. The book's got:

  • 201 Interview Questions from FAANG & startups
  • A Refresher on Product Analytics, SQL & ML
  • Amazing Reviews (900+ reviews, 4.5-star rating)

Acing Data Science Interview