At Central Garden & Pet, SQL is utilized for analyzing pet and garden sales data to identify seasonal trends and correlations between product categories, as well as managing product inventory across multiple retail platforms to optimize stock levels and minimize stockouts. This is the reason why Central Garden & Pet often asks SQL coding questions during interviews for Data Science and Data Engineering positions.
Thus, to help you prep, we've collected 11 Central Garden & Pet SQL interview questions – can you solve them?
In Central Garden & Pet, product reviews are collected and stored in a database as follows:
review_id | user_id | submit_date | product_id | stars |
---|---|---|---|---|
1 | 123 | 2021-06-01 | 1 | 4 |
2 | 265 | 2021-06-05 | 2 | 4 |
3 | 362 | 2021-06-25 | 1 | 3 |
4 | 192 | 2021-07-10 | 2 | 3 |
5 | 981 | 2021-07-15 | 2 | 2 |
Each review has a unique , and is associated with a (the user who submitted the review), a (the date the review was submitted), a (the product the review is for), and (the star rating of the review, from 1 to 5).
Your task is to write a SQL query that computes the average star rating for each product, for each month. You need to use window functions to achieve this.
This query uses the function to truncate the to the first day of its month, effectively grouping all dates in the same month together. It then computes the average for each in each month using the function with a window defined by , meaning the average is computed within the window of all rows having the same and month. Finally, it orders the result by and .
To solve a related window function SQL problem on DataLemur's free online SQL code editor, solve this Google SQL Interview Question:
Given a table of Central Garden & Pet employee salaries, write a SQL query to find all employees who make more than their own manager.
employee_id | name | salary | department_id | manager_id |
---|---|---|---|---|
1 | Emma Thompson | 3800 | 1 | |
2 | Daniel Rodriguez | 2230 | 1 | 10 |
3 | Olivia Smith | 8000 | 1 | 8 |
4 | Noah Johnson | 6800 | 2 | 8 |
5 | Sophia Martinez | 1750 | 1 | 10 |
8 | William Davis | 7000 | 2 | NULL |
10 | James Anderson | 4000 | 1 | NULL |
employee_id | employee_name |
---|---|
3 | Olivia Smith |
This is the output because Olivia Smith earns $8,000, surpassing her manager, William Davis who earns 7,800.
Test your SQL query for this interview question directly within the browser on DataLemur:
First, we perform a SELF-JOIN where we treat the first table () as the managers' table and the second table () as the employees' table. Then we use a clause to filter the results, ensuring we only get employees whose salaries are higher than their manager's salary.
If the code above is confusing, you can find a step-by-step solution with hints here: Well Paid Employees.
The function accepts an unlimited number of input arguments, and returns the first argument from that list which isn't null.If all input arguments are null, will return null too.
For example, suppose you had data on Central Garden & Pet salespeople, and the amount of deals they closed. This data was exported from a 3rd-party system CRM, which exports a value if the salesperson didn't close any deals.
sales_person | closed_deals |
---|---|
Jason Wright | 4 |
Drew Jackson | NULL |
Chris Ho | 2 |
Adam Cohen | NULL |
Samantha Perez | 3 |
To get rid of these NULLs, and replace them with zero's (so you can do some further analytics like find the average number of closed deals), you would use the function as follows:
You'd get the following output:
sales_person | closed_deals |
---|---|
Jason Wright | 4 |
Drew Jackson | 0 |
Chris Ho | 2 |
Adam Cohen | 0 |
Samantha Perez | 3 |
Central Garden & Pet is interested in understanding the purchasing habits of their customers from different states. Some relevant details to consider include the type of pet the customer owns, the full name of the customer and their state of residence, the total amount they've spent and the date of their most recent purchase.
Given the following tables for , , and :
customer_id | first_name | last_name | state |
---|---|---|---|
1001 | John | Doe | California |
1002 | Jane | Doe | New York |
1003 | Jim | Smith | Texas |
1004 | Jessica | Jones | Florida |
pet_id | customer_id | pet_type |
---|---|---|
2001 | 1001 | Dog |
2002 | 1001 | Cat |
2003 | 1002 | Bird |
2004 | 1003 | Fish |
2005 | 1004 | Reptile |
purchase_id | customer_id | total_amount | purchase_date |
---|---|---|---|
3001 | 1001 | 20.00 | 06/01/2022 |
3002 | 1002 | 30.00 | 06/05/2022 |
3003 | 1001 | 40.00 | 06/07/2022 |
3004 | 1003 | 50.00 | 06/10/2022 |
3005 | 1004 | 60.00 | 06/12/2022 |
Write a PostgreSQL query to filter the table for customers who have a total purchase amount greater than 50.00 and who live in either California or New York. The result should also mention the type of pet they have and the date of their most recent purchase.
This SQL query starts with the table and joins both the and tables based on the . It then filters for customers who live in either California or New York and who have made purchases totaling more than 50.00. It ends with a GROUP BY statement to ensure unique customers are returned and aggregates the latest purchase date for each customer.
Both types of joins in SQL help you retrieve data from multiple tables and merge the results into a single table.
To demonstrate the difference between a left join versus a right join, imagine you had two database tables: an table that contains data on Google Ads keywords and their bid amounts, and a table with information on product sales and the Google Ads keywords that drove those sales.
A retrieves all rows from the left table (in this case, the table) and any matching rows from the right table (the Sales table). If there is no match in the right table, values will be returned for the right table's columns.
A retrieves all rows from the right table (in this case, the Sales table) and any matching rows from the left table (the table). If there is no match in the left table, values will be returned for the left table's columns.
You have been hired as a Data Analyst at Central Garden & Pet, a leading innovator, marketer, and producer of quality branded products for the consumer pet supplies markets. Your manager wants you to identify the average sales of each product in all the stores in order to streamline the supply chain. Using the available data, you are required to find the average sales of each pet supply product across all stores.
sale_id | store_id | product_id | product_name | sale_date | units_sold |
---|---|---|---|---|---|
1012 | 1 | "P001" | "Dog Food" | 06/10/2021 | 10 |
2018 | 2 | "P002" | "Cat Litter" | 06/11/2021 | 8 |
3291 | 1 | "P001" | "Dog Food" | 07/14/2021 | 20 |
4082 | 2 | "P002" | "Cat Litter" | 07/19/2021 | 12 |
5092 | 1 | "P003" | "Bird Seeds" | 07/26/2021 | 15 |
product_id | product_name | avg_unit_sale |
---|---|---|
"P001" | "Dog Food" | 15.00 |
"P002" | "Cat Litter" | 10.00 |
"P003" | "Bird Seeds" | 15.00 |
In this query, we group the sales data by product id and product name. The AVG function is then used to calculate the average units_sold for each group, which gives us the average sale of each pet supply product across all stores.
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for finding top products or this Amazon Average Review Ratings Question which is similar for < calculating averages.
The clause is used to remove all duplicate records from a query.
For example, if you had a table of open jobs Central Garden & Pet was hiring for, and wanted to see what are all the unique job titles that were currently available at the company, you could write the following query:
You work for Central Garden & Pet, a company that specializes in marketing and selling pet and garden-related products. The company heavily invests in digital marketing and tracks the click-through rates of their digital ads.
Your Marketing team wants to understand the clickthrough conversion rate of the customers from viewing a product to adding a product to their shopping cart for the past month. Create a SQL query to determine the product's clickthrough conversion rate.
click_id | user_id | click_time | product_id |
---|---|---|---|
101 | 984 | 08/08/2022 01:00:00 | 60001 |
202 | 236 | 08/10/2022 02:00:00 | 67852 |
303 | 542 | 08/18/2022 09:00:00 | 60001 |
404 | 292 | 08/26/2022 11:00:00 | 67852 |
505 | 121 | 08/30/2022 04:00:00 | 60001 |
add_to_cart_id | user_id | add_to_cart_time | product_id |
---|---|---|---|
602 | 984 | 08/08/2022 01:30:00 | 60001 |
701 | 542 | 08/18/2022 09:30:00 | 60001 |
803 | 121 | 08/30/2022 04:30:00 | 60001 |
product_id | total_clicks | added_to_cart | conversion_rate |
---|---|---|---|
60001 | 3 | 3 | 100.00% |
67852 | 2 | 0 | 0.00% |
This query first calculates the total number of clicks per product and the total number of add to cart actions per product for a given period (August 2022). Then, it joins these two tables together by and calculates the clickthrough conversion rate. If there are no clicks for a given product, it returns 0.00% as the conversion rate.
To practice another question about calculating rates, solve this TikTok SQL question within DataLemur's online SQL coding environment:
Central Garden & Pet Company wants to assess the monthly performance of their product categories in terms of their sales. Write an SQL query to find the average monthly sales amount for each product category for the year 2022.
sale_id | product_id | category_id | sale_date | sale_amount |
---|---|---|---|---|
101 | 1 | 10 | 01/07/2022 | 140 |
102 | 2 | 20 | 01/02/2022 | 200 |
103 | 1 | 10 | 01/28/2022 | 300 |
104 | 3 | 20 | 02/11/2022 | 250 |
105 | 2 | 20 | 02/25/2022 | 200 |
category_id | category_name |
---|---|
10 | Pet Food |
20 | Garden Supplies |
mnth | category_name | avg_sale_amount |
---|---|---|
1 | Pet Food | 220 |
1 | Garden Supplies | 200 |
2 | Garden Supplies | 225 |
In this answer, we join the table to the table based on the . We segregate sales data by month, and then calculate the average sale amount for each month for each category. We restrict the data to the year 2022 using a WHERE clause. The final result is ordered by month.
Denormalization is the practice of altering a database schema in a way that breaks the normalization rules (1st, 2nd, 3rd normal forms).
Here's 3 reasons to de-normalize a database at Central Garden & Pet:
Improved performance: Denormalization can often improve performance by reducing the number of expensive join operations required to retrieve data. This is particularly useful when the database is being used for online analytical processing (OLAP) as frequent joins across multiple tables can be slow and costly.
Scalability: Denormalization can increase the scalability of a database by requiring less data to be read and processed when executing a query. This can be beneficial when the database is expected to handle a high volume of queries.
Simplification: One way to simplify the design of a database is by using denormalization to reduce the number of tables and relationships that need to be managed. This can make the database easier to understand and maintain.
Central Garden & Pet maintains a database of customers, including their email addresses. You are asked to retrieve all customer records whose email address is registered on a specific domain, say 'yahoo.com'. Write a SQL query to perform this task on a table.
Please note that the table schema for is as follows:
customer_id | firstname | lastname | |
---|---|---|---|
421 | John | Doe | johndoe@yahoo.com |
512 | Jane | Smith | janesmith@gmail.com |
783 | Martin | Clark | martinclark@yahoo.com |
254 | Liv | Moe | livmoe@hotmail.com |
835 | Amy | Kane | amykane@yahoo.com |
Create an SQL query to extract details of customers with the email domain 'yahoo.com'.
customer_id | firstname | lastname | |
---|---|---|---|
421 | John | Doe | johndoe@yahoo.com |
783 | Martin | Clark | martinclark@yahoo.com |
835 | Amy | Kane | amykane@yahoo.com |
This PostgreSQL query uses the LIKE clause along with a pattern that checks for any string followed by '@yahoo.com' in the 'email' field. As a result, you will get all records from the table where the email field ends with '@yahoo.com', meaning that those customers who have an email address hosted on the Yahoo domain.
The key to acing a Central Garden & Pet SQL interview is to practice, practice, and then practice some more! Beyond just solving the earlier Central Garden & Pet SQL interview questions, you should also solve the 200+ SQL coding questions which come from companies like Netflix, Google, and Amazon.
Each SQL question has multiple hints, fully explained answers along with a discussion board to see how others solved it and most importantly, there is an interactive coding environment so you can easily right in the browser your SQL query answer and have it graded.
To prep for the Central Garden & Pet SQL interview you can also be helpful to solve interview questions from other consumer good companies like:
In case your SQL skills are weak, forget about going right into solving questions – improve your SQL foundations with this SQL tutorial for Data Scientists & Analysts.
This tutorial covers things like AND/OR/NOT and working with string/text data – both of which show up frequently in SQL interviews at Central Garden & Pet.
Read the latest news and press releases from Central Garden & Pet to stay ahead of the curve!
In addition to SQL interview questions, the other types of problems tested in the Central Garden & Pet Data Science Interview include:
To prepare for the Central Garden & Pet Data Science interview make sure you have a strong understanding of the company's cultural values – this will be key to acing the behavioral interview. For technical interviews get ready by reading Ace the Data Science Interview. The book's got: