11 Cracker Barrel SQL Interview Questions (Updated 2024)
Cracker Barrel employees uses SQL queries to analyze customer dining preferences, helping them understand which menu items are most popular and when customers are most likely to visit. It is also used to optimize supply chain management through predictive modeling, ensuring that ingredients are always fresh and available, which is why Cracker Barrel asks SQL coding questions in interviews for Data Science, Data Engineering, and Data Analytics jobs.
Thus, to help you prepare for the Cracker Barrel SQL interview, here’s 11 Cracker Barrel Old Country Store SQL interview questions in this blog.
11 Cracker Barrel Old Country Store SQL Interview Questions
SQL Question 1: Identify Power Customers in Cracker Barrel
Cracker Barrel wants to identify its power customers based on their restaurant visits and buying frequency from their Old Country Store. A 'power customer' here is defined as a customer who visits at least twice per month and spends more than $200 on average per month. Write a SQL query to identify these power customers.
Assume there are two tables - and with following columns:
Example input:
| visit_id | user_id | visit_date |
|---|---|---|
| 371 | 123 | 06/08/2022 |
| 272 | 265 | 06/10/2022 |
| 953 | 123 | 06/18/2022 |
| 582 | 362 | 07/26/2022 |
| 417 | 123 | 07/05/2022 |
Example input:
| purchase_id | user_id | purchase_date | amount |
|---|---|---|---|
| 6171 | 123 | 06/08/2022 | 100.50 |
| 7802 | 265 | 06/10/2022 | 75.30 |
| 5293 | 123 | 06/18/2022 | 150.20 |
| 6352 | 362 | 07/26/2022 | 99.90 |
| 4517 | 123 | 07/05/2022 | 250.00 |
Answer:
The problem can be solved by using separate subqueries for and and then joining them on and month.
Here's a PostgreSQL query:
This query first calculates the visit count and average purchase amount per month for each customer, and then selects those who meet the criteria of a power customer. The results will be a list of user_ids and the correspondent month(s) where the user met the criteria to be a 'power customer'.
To solve a similar power-user data analysis problem question on DataLemur's free online SQL coding environment, try this recently asked Microsoft SQL interview question:
Explore Cracker Barrel's press releases to discover how they are evolving to meet customer preferences in the restaurant industry! Keeping up with their updates can provide you with valuable insights into the strategies that contribute to their ongoing success.
SQL Question 2: Top 3 Department Salaries
Given a table of Cracker Barrel employee salary information, write a SQL query to find the top 3 highest earning employees within each department.
Cracker Barrel Example Input:
| employee_id | name | salary | department_id |
|---|---|---|---|
| 1 | Emma Thompson | 3800 | 1 |
| 2 | Daniel Rodriguez | 2230 | 1 |
| 3 | Olivia Smith | 2000 | 1 |
| 4 | Noah Johnson | 6800 | 2 |
| 5 | Sophia Martinez | 1750 | 1 |
| 8 | William Davis | 6800 | 2 |
| 10 | James Anderson | 4000 | 1 |
Example Input:
| department_id | department_name |
|---|---|
| 1 | Data Analytics |
| 2 | Data Science |
Example Output:
| department_name | name | salary |
|---|---|---|
| Data Analytics | James Anderson | 4000 |
| Data Analytics | Emma Thompson | 3800 |
| Data Analytics | Daniel Rodriguez | 2230 |
| Data Science | Noah Johnson | 6800 |
| Data Science | William Davis | 6800 |
Check your SQL query for this problem directly within the browser on DataLemur:
Answer:
We use the DENSE_RANK() window function to generate unique ranks for each employee's salary within their department, with higher salaries receiving lower ranks. Then, we wrap this up in a CTE and filter the employees with a ranking of 3 or lower.
If the solution above is confusing, you can find a detailed solution with hints here: Top 3 Department Salaries.
SQL Question 3: What's the major difference between and ?
Before we address vs. question, let's address the clause which is used in a statement to group rows into a set of summary rows. It's typically used in conjunction with aggregate functions such as:
- `MIN
The clause is used to filter the groups created by the clause. It's similar to the clause, but it is used to specify conditions on the groups created by the clause, rather than on the individual rows of the table.
For example:
This query retrieves the total salary for each department and groups the rows by department. The clause then filters the groups to include only Cracker Barrel departments where the total salary is greater than $1 million
Note that the clause must be used after the clause and can only reference columns listed in the clause or aggregated columns selected by the statement.
SQL Question 4: Calculate Monthly Average Ratings for Items
You are a data analyst at Cracker Barrel and your task is to analyze the customer satisfaction level. The product team would like to track the monthly average rating for each product in the restaurant.
You are provided with a table which contains data for each product review. Each row represents a different review.
Please write a SQL query that calculates the monthly average rating (in terms of stars) for each product in the table. The table has the following columns:
- : the ID of the review (integer)
- : the ID of the user who submitted the review (integer)
- : the date when the review was submitted (date)
- : the ID of the product being reviewed (integer)
- : the number of stars received from the review (integer, 1 to 5)
For this problem, assume that the is stored in the UTC timezone.
Example Input:
| review_id | user_id | submit_date | product_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:
| mth | product_id | avg_stars |
|---|---|---|
| 6 | 50001 | 3.50 |
| 6 | 69852 | 4.00 |
| 7 | 69852 | 2.50 |
Answer:
This SQL query uses grouping to calculate the monthly average rating for each product. The function is used to get the month from the . We then group by both the month and to get the average () stars for each product for each month. The clause is used to order the results first by the month, then by the .
For more window function practice, solve this Uber SQL Interview Question on DataLemur's online SQL code editor:
SQL Question 5: What does do, and when would you use this function?
The function is used to remove NULLs, and replace them with another value.
For example, say you were a Data Analyst at Cracker Barrel and were doing a customer analytics project to see who the most marketing-engaged customers were, and had access to the below table.
:
| customer_id | email_engagement | sms_engagement |
|---|---|---|
| 101 | very_active | not_opted_in |
| 201 | un-subscribed | NULL |
| 301 | NULL | not_opted_in |
| 401 | not_active | very_active |
| 501 | very_active | mildly_active |
| 303 | NULL | NULL |
Before you could proceed, you had to remove the NULLs, and replace them with the default value (not_active for email, and not_opted_in for SMS). To do this, you'd run the following query:
This would get you the following output:
| customer_id | email_engagement | sms_engagement |
|---|---|---|
| 101 | very_active | not_opted_in |
| 201 | un-subscribed | not_opted_in |
| 301 | not_active | not_opted_in |
| 401 | not_active | very_active |
| 501 | very_active | mildly_active |
| 303 | not_active | not_opted_in |
SQL Question 6: Popular Menu Items
The Cracker Barrel restaurant chain is looking for insights on customer preferences for their menu items. Based on the customer reviews and order data, they want to identify the most popular menu items for each month over the past year. A "popular item" is defined as the one which was not only ordered the most but also got the highest average rating.
They're considering menu modifications and promotions, so they're specifically interested in understanding the superbly-rated item each month.
The data is stored in two tables: and . Assume we're working with PostgreSQL.
Example Input:
| order_id | customer_id | order_date | menu_item_id |
|---|---|---|---|
| 1024 | 3321 | 01/01/2021 | 5 |
| 1072 | 1717 | 01/02/2021 | 6 |
| 2038 | 4562 | 01/03/2021 | 5 |
| 3021 | 7211 | 01/04/2021 | 13 |
| 4728 | 3342 | 02/02/2021 | 8 |
Example Input:
| review_id | customer_id | review_date | menu_item_id | stars |
|---|---|---|---|---|
| 6171 | 3321 | 01/01/2021 | 5 | 4 |
| 7802 | 1717 | 02/10/2021 | 6 | 3 |
| 5293 | 4562 | 01/15/2021 | 5 | 3 |
| 6352 | 7211 | 01/10/2021 | 13 | 2 |
| 4517 | 3342 | 02/07/2021 | 8 | 5 |
Answer:
This query provides the most popular menu item for each month, by first aggregating the monthly order counts and average review ratings in two separate CTEs ( and ). It then joins these two CTEs to form a combined result which is sorted by month, average star rating (in descending order) and order count, and selects the top item per month.
SQL Question 7: 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 Cracker Barrel employees:
This index would ensure that no two Cracker Barrel 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.
SQL Question 8: Average Sales of Each Product in Cracker Barrel
At Cracker Barrel, you have been tasked to find the average quantity of each product sold daily. You are given a table with columns (unique identifier for the product), (the date when the sale was made), and (the number of units sold).
Example Input:
| sale_id | sale_date | product_id | quantity |
|---|---|---|---|
| 101 | 2022-08-15 | P001 | 200 |
| 102 | 2022-08-15 | P002 | 150 |
| 103 | 2022-08-15 | P003 | 300 |
| 104 | 2022-08-16 | P001 | 220 |
| 105 | 2022-08-16 | P002 | 180 |
| 106 | 2022-08-16 | P003 | 280 |
| 107 | 2022-08-17 | P001 | 210 |
| 108 | 2022-08-17 | P002 | 160 |
| 109 | 2022-08-17 | P003 | 290 |
Answer:
Explanation:
This SQL query groups the sales table by . is used to compute the average daily quantity sold for each product. The result is a table with columns and , which respectively represents the unique identifier for each product and the average number of units sold daily.
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for identifying top products based on sales data or this Wayfair Y-on-Y Growth Rate Question which is similar for calculating metrics based on sales data.
SQL Question 9: Calculating Click-Through and Conversion Rates For Cracker Barrel
Cracker Barrel sends out weekly ad emails to subscribers. You are given data for each email sent: time it was sent, whether it was opened, and if the recipient clicked on the link to Cracker Barrel's online store. From the online store data, you also have the details regarding every visit: who visited, when, if they added a product to the cart, and if they made a purchase.
Calculate the click-through-rate (CTR) and the click-to-conversion rate (CTR to Purchase) from the ad emails in the month of July.
Example Input Tables:
:
| user_id | email_time | opened | clicked_through |
|---|---|---|---|
| 1 | 2022-07-03 09:00:00 | true | true |
| 2 | 2022-07-03 09:05:00 | true | false |
| 3 | 2022-07-03 09:10:00 | false | false |
| 4 | 2022-07-03 09:15:00 | true | true |
:
| user_id | visit_time | added_to_cart | made_purchase |
|---|---|---|---|
| 1 | 2022-07-03 09:10:00 | true | true |
| 4 | 2022-07-03 09:20:00 | false | false |
Answer:
The query calculates two rates. The first one is the click-through rate (CTR), calculated as the number of clicks on ads divided by the total number of emails sent. The second one is the click-through to purchase rate, calculated as a number of made purchases divided by the number of clicks that added to cart. These rates are useful to assess the effectiveness of ad emails and website design for CTR optimization.
To solve a related SQL interview question on DataLemur's free online SQL code editor, attempt this Meta SQL interview question:
SQL Question 10: What is database denormalization, and when is it a good idea to consider it?
Denormalization is the process of modifying a database schema in a way that deviates from the typical rules of normalization (1NF, 2NF, 3NF, etc.). There's a few reasons to denormalize a database:
Improved performance: Joins are slow AF, especially when dealing with the massive datasets that are typically used at Cracker Barrel. Denormalization can improve the performance of a database by reducing the number of joins that are required to retrieve data. This can be particularly useful when the database is being used for querying and reporting purposes, as joins can be expensive and slow.
Scalability: By reducing the amount of data that needs to be read and processed to execute a query, denormalization can enhance the scalability of a database. This can be useful when the database is anticipated to handle a large number of read-only queries (such as in OLAP use cases).
Ease of use: Denormalization can also make it easier for users to work with a database by providing them with a more intuitive and straightforward data model.
Because denormalization can create more complex update and delete operations, and pose potential data integrity issues, consider denormalization only if joins are causing performance bottlenecks.
SQL Question 11: Calculate the Average Sales of Each Menu Item Each Month
As a data analyst at Cracker Barrel, you are asked to analyze the company's sales. Specifically, your boss wants to find out the average quantity of each menu item sold for each month.
You are given a table that consists of the following columns:
- (primary key)
- (the id of the menu item)
- (the quantity of the item sold)
- (the date of the sale)
You are also given an table that consists of the following columns:
- (primary key)
- (the name of the menu item)
Example Input:
| sale_id | item_id | sale_date | quantity |
|---|---|---|---|
| 1 | 200 | 2022-03-01 | 20 |
| 2 | 200 | 2022-03-02 | 30 |
| 3 | 100 | 2022-03-03 | 15 |
| 4 | 100 | 2022-03-04 | 25 |
| 5 | 300 | 2022-04-01 | 35 |
| 6 | 300 | 2022-04-02 | 45 |
| 7 | 200 | 2022-04-03 | 20 |
| 8 | 200 | 2022-04-04 | 30 |
Example Input:
| item_id | item_name |
|---|---|
| 100 | Chicken Fried Chicken |
| 200 | Farmhouse Meatloaf |
| 300 | Country Vegetable Plate |
Answer:
The PostgreSQL query to solve this would be as follows:
This query will return a list of months, along with the names of the items and their respective average quantity sold in those months.
Example Output:
| month | item_name | average_quantity |
|---|---|---|
| 3 | Chicken Fried Chicken | 20 |
| 3 | Farmhouse Meatloaf | 25 |
| 4 | Country Vegetable Plate | 40 |
| 4 | Farmhouse Meatloaf | 25 |
As per the above sample response, for instance, average of 20 units of 'Chicken Fried Chicken' were sold in March, while average of 25 units were sold for 'Farmhouse Meatloaf'. Similarly for April, the average units sold for 'Country Vegetable Plate' and 'Farmhouse Meatloaf' were 40 and 25 respectively.
Cracker Barrel SQL Interview Tips
The key to acing a Cracker Barrel SQL interview is to practice, practice, and then practice some more! Besides solving the above Cracker Barrel SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like Amazon, Microsoft, Meta, and smaller tech companies.
Each exercise has hints to guide you, detailed solutions and most importantly, there's an interactive coding environment so you can right online code up your SQL query answer and have it executed.
To prep for the Cracker Barrel SQL interview it is also helpful to practice SQL questions from other hospitality and restaurant companies like:
- Hyatt SQL questions
- Topgolf SQL questions
- Travel + Leisure SQL questions
- Light & Wonder SQL questions
But if your SQL foundations are weak, don't worry about diving straight into solving questions – refresh your SQL knowledge with this DataLemur SQL tutorial.
This tutorial covers topics including ordering data and INTERCEPT/EXCEPT – both of these show up routinely during Cracker Barrel SQL assessments.
Cracker Barrel Old Country Store Data Science Interview Tips
What Do Cracker Barrel Data Science Interviews Cover?
Beyond writing SQL queries, the other types of questions covered in the Cracker Barrel Data Science Interview are:
- Statistics & Probability Questions
- Python Pandas or R Coding Questions
- Product Data Science Interview Questions
- Machine Learning Questions
- Behavioral Interview Questions
How To Prepare for Cracker Barrel Data Science Interviews?
I think the best way to prepare for Cracker Barrel Data Science interviews is to read the book I wrote: Ace the Data Science Interview.
It covers 201 data interview questions sourced from FAANG, tech startups, and Wall Street. The book's also got a crash course covering Python, SQL & ML. And finally it's helped thousands of people land their dream job in data, which is why it's earned 1000+ reviews on Amazon and a 4.5-star rating.
While the book is more technical, it's also key to prepare for the Cracker Barrel behavioral interview. A good place to start is by understanding the company's values and mission.