11 Domino's SQL Interview Questions (Updated 2024)
Data Analytics, Data Science, and Data Engineering employees at Domino's write SQL queries to analyze customer ordering patterns, helping them to create personalized pizza promotions that resonate with their customers' preferences. They also use SQL for managing inventory in real-time, ensuring that raw materials are used efficiently and that popular items are always in stock, this is why Domino's usually asks SQL interview problems.
To help you prepare for the Domino's SQL interview, here's 11 Domino's Pizza SQL interview questions – can you solve them?
11 Domino's Pizza SQL Interview Questions
SQL Question 1: Identify Power Users in Domino's Customer Database
Domino's is interested in identifying their "power users" - customers who order frequently and spend significantly. We define a "power user" as any customer who has placed more than 10 orders in the last month and whose total spend exceeds $300. Write a SQL query that identifies these power users from the following tables:
Example Input:
| order_id | user_id | order_date | total_price |
|---|---|---|---|
| 7891 | 110 | 2022-07-01 | $30 |
| 2835 | 220 | 2022-07-05 | $50 |
| 4172 | 110 | 2022-07-05 | $35 |
| 5821 | 330 | 2022-07-15 | $40 |
| 1779 | 220 | 2022-07-16 | $60 |
| 7454 | 110 | 2022-07-17 | $45 |
| 5123 | 220 | 2022-07-20 | $55 |
| 3548 | 330 | 2022-07-25 | $60 |
| 2567 | 330 | 2022-07-26 | $50 |
| 1036 | 110 | 2022-07-30 | $35 |
Example Input:
| user_id | name |
|---|---|
| 110 | John Doe |
| 220 | Jane Smith |
| 330 | Bob Johnson |
Answer:
This query brings together data from both the and table. It first filters the table for orders made in the last month. Then it groups the data by and , and applies the criteria of more than 10 orders and a total spend of over $300 for a user to be considered a 'power user'. The output will be the user ids and names of all those customers who meet the power user definition.
To practice a similar customer analytics SQL question where you can code right in the browser and have your SQL query instantly graded, try this Walmart Labs SQL Interview Question:
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SQL Question 2: Department Salaries
Assume there was a table of Domino's employee salary data, along with which department they belonged to. Write a query to compare the average salary of employees in each department to the company's average salary for March 2024. Return the comparison result as 'higher', 'lower', or 'same' for each department. Display the department ID, payment month (in MM-YYYY format), and the comparison.
You can solve this interview question directly within the browser on DataLemur:
The solution is LONG – 30+ lines of SQL. You can find a step-by-step solution here: Department Salaries.
SQL Question 3: What is database denormalization?
Denormalization is a technique used to improve the read performance of a database, typically at the expense of some write performance.
By adding redundant copies of data or grouping data together in a way that does not follow normalization rules, denormalization improves the performance and scalability of a database by eliminating costly join operations, which is important for OLAP use cases that are read-heavy and have minimal updates/inserts.
SQL Question 4: Track Average Monthly Reviews for Domino's Pizza
Domino's would like to track a monthly average review score for each pizza that they sell. They are interested in understanding if the monthly average review stars of each pizza has improved, decreased, or remained constant over time.
This is a perfect use case for SQL Window functions. In this scenario, we would have two tables: a table and a table.
Here's the sample input and output needed:
Example Input:
| pizza_id | pizza_name |
|---|---|
| 50001 | Pepperoni |
| 69852 | Margherita |
| 35297 | BBQ Chicken Feast |
Example Input:
| review_id | user_id | submit_date | pizza_id | stars |
|---|---|---|---|---|
| 6171 | 123 | 2022-06-08 | 50001 | 4 |
| 7802 | 265 | 2022-06-10 | 69852 | 4 |
| 5293 | 362 | 2022-06-18 | 50001 | 3 |
| 6352 | 192 | 2022-07-26 | 69852 | 3 |
| 4517 | 981 | 2022-07-05 | 69852 | 2 |
Example Output:
| month | pizza_name | avg_stars |
|---|---|---|
| 06 | Pepperoni | 3.50 |
| 06 | Margherita | 4.00 |
| 07 | Margherita | 2.50 |
Answer:
This SQL script utilizes PostgreSQL's date functions, window functions and join functionalities. The function turns the into the month. The and functions calculate the average review stars for each pizza in each month. And finally, the clausule simply associates each review with the correct pizza based off . This gives Domino's the much needed insights on how each pizza is performing each month in terms of average review stars.
Pro Tip: Window functions are a frequent SQL interview topic, so practice every window function problem on DataLemur
SQL Question 5: How does a left join differ from a right join?
In SQL, both a left and right join is used to combine rows from different tables based on a shared key or set of keys, resulting in a single merged table of data. However, here's the difference:
LEFT (OUTER) JOIN: Retrieves all the records/rows from the left and the matched records/rows from the right table.
RIGHT (OUTER) JOIN: Retrieves all the records/rows from the right and the matched records/rows from the left table.
SQL Question 6: Analyzing Pizza Order Data for Domino's
Domino's manages a pizza delivery service across multiple states. They would like to track the performance of each of their outlets and analyze customer preferences across different regions. To achieve this, they maintain the following tables:
- customers - This table contains customer information.
| customer_id | first_name | last_name | state |
|---|---|---|---|
| 101 | John | Doe | California |
| 102 | Jane | Smith | Florida |
| 103 | Jim | Brown | New York |
| 104 | Jill | Johnson | California |
- outlets - This table contains outlet information.
| outlet_id | address | state |
|---|---|---|
| 1 | 123 Main St, San Diego | California |
| 2 | 456 Orange Ave, Miami | Florida |
| 3 | 789 Broadway, New York | New York |
| 4 | 246 Walnut St, San Francisco | California |
- orders - This table contains order information.
| order_id | customer_id | outlet_id | pizza_type | order_date |
|---|---|---|---|---|
| 1 | 101 | 1 | Margarita | 2023-01-01 |
| 2 | 101 | 1 | Pepperoni | 2023-02-01 |
| 3 | 102 | 2 | Vegetarian | 2023-01-15 |
| 4 | 104 | 4 | Pepperoni | 2023-02-20 |
The task: Write a PostgreSQL query to determine the most ordered pizza type from each outlet.
The result should display the outlet's address, the most popular pizza type at that outlet, and the total number of orders for that pizza type at the outlet.
Answer:
This query joins the and tables on the column. It then groups the results by the outlet's and the , and counts the number of orders for each pizza type at each outlet. The results are then sorted by the total number of orders in descending order, and the in ascending order. This will show the most ordered pizza type from each outlet.
SQL Question 7: Can you explain what a cross-join is and the purpose of using them?
A cross-join, also known as a cartesian join, is a JOIN that produces the cross-product of two tables. In a cross-join, each row from the first table is matched with every row from the second table, resulting in a new table with a row for each possible combination of rows from the two input tables.
Suppose you were building a Neural Network ML model, that tried to score the probability of a customer buying a Domino's product. Before you started working in Python and Tensorflow, you might want to do some Exploratory Data Analysis (EDA) in SQL, and generate all pairs of customers and Domino's products.
Here's a cross-join query you could use to find all the combos:
Cross-joins are great for generating all possible combinations, but they can also create really big tables if you're not careful. For example, if you had 10,000 potential customers, and Domino's had 500 different product SKUs, you'd get 5 million rows as a result!!
SQL Question 8: Domino's Order Pattern
For a pizza delivery service such as Domino's, it's crucial to know their customers' ordering pattern. Therefore, create a query to filter out customers who have ordered more than once in the last month and whose average total order amount is over $50. The information is taken from two tables, and .
Here is the sample data:
Example Input:
| customer_id | first_name | last_name |
|---|---|---|
| 101 | John | Doe |
| 102 | Mary | Smith |
| 103 | James | Johnson |
| 104 | Emily | Ford |
| 105 | Robert | Wilson |
Example Input:
| order_id | customer_id | order_date | total_amount |
|---|---|---|---|
| 2001 | 101 | 2022-09-20 | 60 |
| 2002 | 102 | 2022-09-22 | 45 |
| 2003 | 101 | 2022-10-01 | 55 |
| 2004 | 103 | 2022-10-05 | 75 |
| 2005 | 103 | 2022-10-15 | 40 |
| 2006 | 105 | 2022-10-20 | 90 |
Answer:
This query first creates an intermediate table which includes the number of orders and average order amount for every customer over the last month. It then filters out those who made more than one order and the average order amount is above $50, and finally joins with the table to get the customer's name.
SQL Question 9: Calculate Average Pizza Delivery Time
As a data analyst at Domino's, you have been tasked to find the average delivery time for all the pizza orders for the past month. The table contains the following columns: (int), (timestamp), (timestamp), and (int). Your result should display the average delivery time (in minutes).
Please use the following sample data for your analysis:
Example Input:
| order_id | order_time | delivery_time | customer_id |
|---|---|---|---|
| 101 | 2022-09-01 18:20:00 | 2022-09-01 18:50:00 | 1001 |
| 102 | 2022-09-05 19:35:00 | 2022-09-05 20:00:00 | 1002 |
| 103 | 2022-09-12 17:45:00 | 2022-09-12 18:15:00 | 1003 |
| 104 | 2022-09-15 18:30:00 | 2022-09-15 19:05:00 | 1004 |
| 105 | 2022-09-21 19:15:00 | 2022-09-21 19:50:00 | 1005 |
Answer:
In PostgreSQL, you can utilize the function to get the minutes between the and to then calculate the average.
This query will output the average delivery time in minutes for all the pizza orders in the 'orders' table by subtracting from , extracting the duration in seconds, and then converting it to minutes.
To practice a very similar question try this interactive Alibaba Compressed Mean Question which is similar for calculating mean from a large data set or this Stripe Repeated Payments Question which is similar for dealing with timestamped transaction data.
SQL Question 10: What's the difference between relational and non-relational databases?
While both types of databases are used to store data (no duh!), relational databases and non-relational (also known as NoSQL databases) differ in a few important ways, most importantly on the way data is stored. Relational databases use a data model consisting of tables and rows, while NoSQL databases use a variety of data models, including document, key-value, columnar, and graph storage formats.
This added flexibilty makes NoSQL databases great for non-tabular data (like hierarchal data or JSON data), or data where the type/format is constantly evolving. With this added flexibility, comes one big weakness – you won't get ACID-compliance. That means, unlike relational databases which are typically adhere to the ACID properties (atomic, consistent, isolated, and durable), you don't get as strong guarentees with most non-relational databases.
SQL Question 11: Calculate Click-Through and Conversion Rates for Domino's Ads
For a company like Domino's, a relevant SQL question might involve calculating click-through and conversion rates for ad campaigns and how this varies by different pizza types. Let's suppose we have a series of marketing campaigns. Click-through rate is defined as the number of clicks on the ad divided by the number of ad impressions * 100. Conversion rate can be defined as the number of orders placed divided by the number of clicks on the ad * 100.
Example Input:
| campaign_id | pizza_type | impressions | clicks | orders |
|---|---|---|---|---|
| 1 | Pepperoni | 10000 | 500 | 50 |
| 2 | Veggie | 20000 | 1500 | 100 |
| 3 | Hawaiian | 30000 | 1800 | 90 |
| 4 | Margherita | 50000 | 2500 | 200 |
| 5 | BBQ Chicken | 40000 | 2000 | 150 |
Answer:
This SQL query executes the following steps:
- It selects all the columns from the table
- Then, it calculates the click through rate as
- Similarly, it calculates the conversion rate as
- The cast to is necessary to handle decimal points appropriately. In PostgreSQL, the division operation between two integers results in an integer, which would not be beneficial in our case.
This query would provide the click-through and conversion rates for each of the ad campaigns separated by pizza type.
To solve a similar problem about calculating rates, solve this SQL interview question from TikTok within DataLemur's interactive SQL code editor:
How To Prepare for the Domino's SQL Interview
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Domino's SQL interview is to solve as many practice SQL interview questions as you can! Beyond just solving the earlier Domino's SQL interview questions, you should also solve the 200+ SQL questions from real Data Science & Analytics interviews which come from companies like FAANG tech companies and tech startups.
Each problem on DataLemur has multiple hints, step-by-step solutions and most importantly, there is an online SQL coding environment so you can instantly run your query and have it graded.
To prep for the Domino's SQL interview you can also be useful to practice SQL problems from other hospitality and restaurant companies like:
- McDonald's SQL questions
- Yum Brands SQL questions
- Chipotle SQL questions
- Bloomin' Brands SQL questions
However, if your SQL coding skills are weak, don't worry about diving straight into solving questions – refresh your SQL knowledge with this interactive SQL tutorial.
This tutorial covers things like rank window functions and filtering groups with HAVING – both of which come up frequently during Domino's interviews.
Domino's Pizza Data Science Interview Tips
What Do Domino's Data Science Interviews Cover?
Beyond writing SQL queries, the other types of questions tested in the Domino's Data Science Interview are:
- AB Testing Questions
- Python Pandas or R Coding Questions
- Data Case Study Questions
- ML Interview Questions
- Behavioral Questions centered on Domino's culture and values
How To Prepare for Domino's Data Science Interviews?
To prepare for Domino's Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions taken from Google, Microsoft & tech startups
- a refresher covering Product Analytics, SQL & ML
- over 1000+ 5-star reviews on Amazon
Don't forget about the behavioral interview – prepare for that with this list of common Data Scientist behavioral interview questions.