10 Texas Roadhouse SQL Interview Questions (Updated 2024)
Data Scientists, Analysts, and Data Engineers at Texas Roadhouse use SQL to analyze customer dining patterns, which helps them identify peak times and popular menu items, allowing for better staffing and menu adjustments. They also use it to optimize inventory management based on sales data, ensuring that each restaurant is stocked with just the right amount of ingredients to meet customer demand, this is the reason why Texas Roadhouse asks SQL interview questions.
As such, to help you prepare for the Texas Roadhouse SQL interview, we'll cover 10 Texas Roadhouse SQL interview questions – scroll down to start solving them!
10 Texas Roadhouse SQL Interview Questions
SQL Question 1: Top Selling Menu Items with SQL Window Functions
One common query that a restaurant chain like Texas Roadhouse might want to run on their sales data is figuring out the top selling dishes for each month of the year in different branches. This requires using SQL window functions such as , , or over partitioned data along with the typical aggregate function.
Example Input:
| sale_id | branch_id | sale_date | menu_item_id | quantity |
|---|---|---|---|---|
| 1 | 101 | 04/14/2022 | 1001 | 5 |
| 2 | 102 | 04/18/2022 | 2001 | 3 |
| 3 | 101 | 04/20/2022 | 2002 | 1 |
| 4 | 101 | 05/14/2022 | 1001 | 4 |
| 5 | 101 | 05/14/2022 | 2002 | 2 |
| 6 | 102 | 05/18/2022 | 2001 | 5 |
| 7 | 102 | 04/20/2022 | 1001 | 7 |
Answer:
The above SQL query first groups the sales data by , , and month of the . It counts the number of sales for each group. The window function is then used to rank these groups within their respective branches and months by the number of sales, in descending order.
The outermost query then selects only those rows where the rank is 1, i.e., only the top selling menu item for each branch for each month.
To practice a similar window function question on DataLemur's free interactive SQL code editor, try this Google SQL Interview Question:
Explore Texas Roadhouse's press room to uncover the latest news and exciting developments that are making waves in the restaurant industry! Keeping up with Texas Roadhouse's updates can give you a deeper appreciation for their commitment to quality and customer satisfaction.
SQL Question 2: Department Salaries
Imagine there was a table of Texas Roadhouse 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.
Solve this problem directly within the browser on DataLemur:
The answer is LONG – 30+ lines of SQL. You can find a detailed solution here: Department Salaries.
SQL Question 3: Could you explain the differences between an inner and full outer join?
An inner join only includes rows from both tables that satisfy the join condition, whereas a full outer join includes all rows from both tables, regardless of whether they match the join condition or not.
For a concrete example, imagine you had two database tables, an advertising campaigns table which had information on Google Ads keywords and how much was bid for each keyword, and a Texas Roadhouse sales table, which has data on how many products were sold and which Google Ads keyword drove that sale.
: retrieves rows from both tables where there is a match in the shared key or keys. For example, an INNER JOIN between the table and the table could be performed using the keyword column as the shared key. This would retrieve only the rows where the keyword in the table matches the keyword in the table.
: retrieves all rows from both tables, regardless of whether there is a match in the shared key or keys. If there is no match, values will be returned for the columns of the non-matching table.
SQL Question 4: Filter Customer Data For Texas Roadhouse
Given a database of Texas Roadhouse customer visits, can you write a SQL query to filter down to only the records of customers who visited the restaurant in Austin, TX anytime after January 1, 2022, and rated their visit 4 stars or above?
Provide the output in the format of , , , .
Sort the result based on in descending order.
Example Input:
| visit_id | customer_id | visit_date | visit_city | stars |
|---|---|---|---|---|
| 3871 | 564 | 01/02/2022 | Austin, TX | 5 |
| 4842 | 198 | 02/18/2022 | Dallas, TX | 3 |
| 5793 | 462 | 03/12/2022 | Houston, TX | 4 |
| 6912 | 383 | 02/22/2022 | Austin, TX | 5 |
| 7537 | 982 | 01/20/2022 | Austin, TX | 3 |
| 8027 | 492 | 02/25/2022 | Austin, TX | 4 |
| 8652 | 655 | 03/15/2022 | San Antonio, TX | 5 |
| 9052 | 277 | 01/10/2022 | Austin, TX | 4 |
Example Output:
| customer_id | visit_date | visit_city | stars |
|---|---|---|---|
| 462 | 03/12/2022 | Austin, TX | 4 |
| 492 | 02/25/2022 | Austin, TX | 4 |
| 383 | 02/22/2022 | Austin, TX | 5 |
| 277 | 01/10/2022 | Austin, TX | 4 |
| 564 | 01/02/2022 | Austin, TX | 5 |
Answer:
The given query filters out the records where the is 'Austin, TX', the is after '2022-01-01', and are 4 or above. It then returns these records sorted by in a descending order.
SQL Question 5: Why is normalizing a database helpful?
Normalization can reduce redundancy and improve performance and flexibility in a database.
By dividing larger tables into smaller, more specific ones linked by foreign keys, it is often possible to improve query speed. Note: it is not always the case that query speed will be improved due to costly nature of joins. Still, we think normalization is a good idea because it can also increase flexibility by making it easier to alter individual tables without affecting others, which can be useful for adapting Texas Roadhouse's database to ever-changing business needs.
SQL Question 6: Analysis of Click-Through-Rate for Texas Roadhouse Online Orders
Texas Roadhouse wants to analyze the effectiveness of their online ordering platform. They are specifically interested in the click-through conversion rates from viewing a product (menu item) to adding the product to the cart.
To solve this, you have been given two tables: , which represents each time a unique user views a menu item, and , which represents each time a unique user adds a menu item to their cart.
Example Input
| view_id | user_id | view_date | menu_item_id |
|---|---|---|---|
| 5648 | 111 | 05/12/2022 | 30001 |
| 9815 | 222 | 05/12/2022 | 30002 |
| 7386 | 333 | 05/14/2022 | 30003 |
| 6531 | 111 | 05/15/2022 | 30001 |
| 3391 | 444 | 05/16/2022 | 30002 |
Example Input
| add_id | user_id | add_date | menu_item_id |
|---|---|---|---|
| 7834 | 111 | 05/15/2022 | 30001 |
| 6245 | 222 | 05/13/2022 | 30002 |
| 2759 | 555 | 05/16/2022 | 30003 |
| 4227 | 111 | 05/16/2022 | 30001 |
| 9327 | 444 | 05/17/2022 | 30002 |
The objective is to write a SQL query which calculates the Click-Through-Rate (CTR) for each menu item. This is defined as the ratio of the number of unique users who add a menu item to their cart after viewing it to the the number of unique users who viewed this menu item.
Answer:
This query first joins with on and with the condition that should be less than or equal to . This is to ensure that the view happened before the addition to cart.
Then, it groups by and calculates the CTR as the number of unique users who added the item to their cart divided by the number of unique users who viewed this item. If no one viewed the item, the result will be NULL (prevented by in the denominator).
To practice another question about calculating rates, solve this TikTok SQL Interview Question within DataLemur's interactive SQL code editor:
SQL Question 7: How does a cross join differ from a natural join?
Imagine you are organizing a party and have two database tables: one table of people you want to invite and another list of food items you want to serve.
A cross join would be like inviting every person on your list to the party and serving them every food item on the menu, regardless of whether they like the food or not. So, if you had 10 people on your invite list and 5 food items on the menu, you would generate all 50 different combinations of people and food (10 x 5 = 50).
On the other hand, a natural join would be like inviting only the people who like the food items on the menu (based on doing a inner/left/right/outer JOIN on a common key like ).
SQL Question 8: Average Restaurant Rating By Month
Given the restaurant ratings data for Texas Roadhouse, your task is to write a Postgres SQL query to find the average rating of each restaurant by month.
Example Input:
| rating_id | user_id | submit_date | restaurant_id | stars |
|---|---|---|---|---|
| 1111 | 523 | 2022/01/12 | 4567 | 5 |
| 1112 | 852 | 2022/01/13 | 4567 | 3 |
| 1113 | 618 | 2022/02/14 | 4569 | 4 |
| 1114 | 999 | 2022/03/15 | 4569 | 2 |
| 1115 | 714 | 2022/03/16 | 4567 | 4 |
Answer:
The above SQL block will group the reviews based on the month number and due to our clause. Then, for each of these groups, it computes the average rating () and rounds the result to 2 decimal places using the function. The results are then ordered by month and average rating in descending order (with higher average ratings appearing first). This will give us the average rating of each restaurant for each month in the dataset.
SQL Question 9: Filtering Customer Records at Texas Roadhouse
As a data analyst at Texas Roadhouse, a popular restaurant, you have been tasked to identify customers whose first names start with an 'M' and who have visited the restaurant in the year 2022. This task is crucial as it is part of a promotional campaign activity meant to reward loyal customers.
Given a table and a table formatted as below:
Example Input:
| customer_id | first_name | last_name | |
|---|---|---|---|
| 101 | Michael | Henley | michael.henley@email.com |
| 102 | Martin | Lopez | martin.lopez@email.com |
| 103 | Rachel | Johnson | rachel.johnson@email.com |
| 104 | Marie | Pierce | marie.pierce@email.com |
| 105 | Liam | Moore | liam.moore@email.com |
Example Input:
| visit_id | customer_id | visit_date |
|---|---|---|
| 2001 | 101 | 01/10/2022 |
| 2002 | 102 | 03/15/2022 |
| 2003 | 103 | 02/19/2022 |
| 2004 | 104 | 04/22/2022 |
| 2005 | 105 | 05/18/2022 |
Expected outputs from your query should look like:
Example Output:
| first_name | last_name | visit_date |
|---|---|---|
| Michael | Henley | 01/10/2022 |
| Martin | Lopez | 03/15/2022 |
| Marie | Pierce | 04/22/2022 |
Answer:
The above SQL query works by joining the table and table on the field. It then filters the results to include only customers whose first names start with 'M' (using ) and have visited the restaurant in the year 2022 (using ). The output from this query would show the first name, last name, and visit dates of the customers satisfying these conditions.
SQL Question 10: What's the purpose of the the command?
Similar to the and / operators, the PostgreSQL operator combines result sets of two or more statements into a single result set. However, only returns the rows that are in BOTH select statements.
For a concrete example, say you were on the Sales Analytics team at Texas Roadhouse, and had data on sales leads exported from both HubSpot and Salesforce CRMs in two different tables. To write a query to analyze leads created after 2023 started, that show up in both CRMs, you would use the command:
Texas Roadhouse SQL Interview Tips
The best way to prepare for a Texas Roadhouse SQL interview is to practice, practice, practice. Besides solving the earlier Texas Roadhouse SQL interview questions, you should also solve the 200+ DataLemur interview questions which come from companies like Microsoft, Google, and Meta.
Each DataLemur SQL question has hints to guide you, step-by-step solutions and most importantly, there's an online SQL coding environment so you can easily right in the browser your query and have it checked.
To prep for the Texas Roadhouse SQL interview it is also useful to solve SQL questions from other hospitality and restaurant companies like:
- Bloomin' Brands SQL questions
- Marriott SQL questions
- Cracker Barrel SQL questions
- Hyatt SQL questions
However, if your SQL coding skills are weak, don't worry about jumping right into solving questions – strengthen your SQL foundations with this interactive SQL tutorial.
This tutorial covers SQL concepts such as filtering data with WHERE and CASE/WHEN/ELSE statements – both of these show up often in SQL job interviews at Texas Roadhouse.
Texas Roadhouse Data Science Interview Tips
What Do Texas Roadhouse Data Science Interviews Cover?
Beyond writing SQL queries, the other types of questions tested in the Texas Roadhouse Data Science Interview include:
- Statistics and Probability Questions
- Python or R Programming Questions
- Product-Sense Questions
- ML Modelling Questions
- Behavioral Interview Questions focussed on Texas Roadhouse company values
How To Prepare for Texas Roadhouse Data Science Interviews?
To prepare for Texas Roadhouse Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions taken from FAANG, tech startups, and Wall Street
- a refresher covering SQL, AB Testing & ML
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Don't forget about the behavioral interview – prepare for it using this list of common Data Scientist behavioral interview questions.