Data Analytics, Data Science, and Data Engineering employees at Keurig Dr Pepper write SQL queries to analyze beverage sales trends, giving them insights into which products are popular and when demand is highest. They also rely on SQL to keep supply chain running smoothly, ensuring that their products are delivered efficiently to meet consumer demand, for this reason, Keurig Dr Pepper asks interviewees SQL coding interview questions.
To help you prep for the Keurig Dr Pepper SQL interview, we've curated 11 Keurig Dr Pepper SQL interview questions in this article.
As a data analyst at Keurig Dr Pepper, your task is to analyze the product reviews data. You have been tasked with writing a SQL query that calculates the average monthly rating for each product.
For this problem, use the table provided. This table contains historical ratings data for various products, with each row representing a single review.
review_id | user_id | submit_date | product_id | stars |
---|---|---|---|---|
6172 | 123 | 01/10/2020 | 50002 | 5 |
6173 | 265 | 02/12/2020 | 69853 | 3 |
6174 | 362 | 02/18/2020 | 50002 | 4 |
6175 | 192 | 03/26/2020 | 69853 | 5 |
6176 | 981 | 04/05/2020 | 69853 | 2 |
mth | product | avg_stars |
---|---|---|
1 | 50002 | 5.0 |
2 | 50002 | 4.0 |
2 | 69853 | 3.0 |
3 | 69853 | 5.0 |
4 | 69853 | 2.0 |
The above SQL query uses the function to get the month from the column, and the function to calculate the average rating. It then groups the results by month and product, and orders the output by month and product.
The window functions aren't necessary in this case because we aren't performing any calculations over a set of rows that are somehow related to the current row, like a running total or finding the maximum value in a group. Here, we're simply grouping by and and finding the average.
To solve another window function question on DataLemur's free online SQL code editor, solve this Google SQL Interview Question:
Assume there was a table of Keurig Dr Pepper employee salary data, along with which department they were in. 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.
Write a SQL query for this interview question interactively on DataLemur:
The answer is LONG – 30+ lines of SQL. You can find a step-by-step solution with hints here: Department vs. Company Salary.
is used to select rows that match a range of values, whereas the operator checks for values in a specified list of values.
For example, say you were a Data Analyst at Keurig Dr Pepper and had a table of advertising campaign data.
To find campaigns with between 10k in spend, you could use :
To find ad campaigns that were run on Facebook and Google's Display Network, you could use :
Let's assume the company Keurig Dr Pepper needs to analyze the sales of its beverages according to regions and states, as well as customer types. They want to understand the usage of their products in these areas and the type of customers who usually buy their products.
They currently have two tables:
sales_id | customer_id | product | quantity | purchase_Date | State |
---|---|---|---|---|---|
1 | 27 | Dr Pepper | 24 | 04/10/2021 | Texas |
2 | 32 | Keurig | 3 | 05/12/2021 | California |
3 | 18 | Canada Dry | 46 | 04/07/2021 | Nevada |
4 | 120 | Sunkist | 20 | 07/15/2021 | New York |
5 | 90 | Dr. Pepper | 60 | 06/21/2021 | Texas |
customer_id | customer_type |
---|---|
27 | Retail |
32 | Wholesale |
18 | Retail |
120 | Distributor |
90 | Retail |
The SQL query should based on the above tables provide an answer to the following question: How many of each beverage were sold in each state, broken down by customer type?
You can combine the two tables and group by the required fields (, , ) to create a summary of quantities sold:
This query joins the and table on the field, and groups the result by , , and . It then sums up the quantity sold for each group. This helps the company analyze the sales of its beverages according to regions, states, and customer types. It can help reveal patterns and provide insights such as which products are more popular in which regions and among which types of customers.
The constraint is used to enforce rules on the data in a specific column. If a row is inserted or updated with data that does not follow the constraint's rule, the operation will fail. The constraint is often used with other constraints, such as or , to ensure that data meets certain conditions. You may want to use a constraint in your database if you want to maintain the quality and reliability of your data by ensuring that it meets specific criteria.
For example, you might use a constraint to ensure that a column contains only positive numbers, or that a date is within a certain range.
As an analyst for Keurig Dr Pepper, your task is to write a SQL query to filter down the total sales of each product in the last quarter of 2021. Additionally, you need to find out the number of unique customers who bought each product in this particular time period.
For your analysis, you will use the and tables. The table has the following columns:
The table has the following columns:
sales_id | product_id | customer_id | quantity | sale_date |
---|---|---|---|---|
200 | 101 | 5000 | 2 | 2021-10-15 |
201 | 102 | 5001 | 1 | 2021-12-20 |
202 | 101 | 5002 | 3 | 2021-11-30 |
203 | 103 | 5000 | 2 | 2021-12-25 |
204 | 102 | 5001 | 2 | 2021-12-30 |
product_id | product_name |
---|---|
101 | Dr Pepper |
102 | Diet Dr Pepper |
103 | Keurig K-Cup |
This query selects the name of each product from the table, and calculates the total sales and number of unique customers from the table. It filters the table to only include the sales made between October and December 2021 (last quarter). The statement groups the result by .
Joins in SQL allow you to combine data from different tables based on a shared key or set of keys.
Four JOIN types are available in SQL. For an example of each one, say you had sales data exported from Keurig Dr Pepper's Salesforce CRM stored in a PostgreSQL database, and had access to two tables: and .
You are given a table named of beverage items for Keurig Dr Pepper. Each row in the table represents a sale of a beverage item, with the columns , , , and indicating the ID of the sale, the ID of the item sold, the date of the sale, and the price at which the item was sold, respectively.
The task is to write a SQL query that calculates the average sale price for each beverage item.
sale_id | item_id | sale_date | sale_price |
---|---|---|---|
101 | 1001 | 05/14/2022 | 4.99 |
102 | 1002 | 05/14/2022 | 2.50 |
103 | 1001 | 05/15/2022 | 5.00 |
104 | 1003 | 05/16/2022 | 3.50 |
105 | 1002 | 05/15/2022 | 2.75 |
item_id | avg_sale_price |
---|---|
1001 | 4.995 |
1002 | 2.625 |
1003 | 3.5 |
This query groups the table by and calculates the average for each . It returns a table with each and its respective average .
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for calculating itemized sales statistics or this Amazon Average Review Ratings Question which is similar for focusing on average item metrics.
As a data analyst at Keurig Dr Pepper, you are tasked with analyzing the click-through-rates(CTR) of different beverage ads. CTR measures the number of click-throughs made on the ads for each beverage to the total ads impressions. Beverages include Dr Pepper, Canada Dry, and Snapple. Each row in the table represents one impression of the ad, and if the user clicked on the ad, is '1'; otherwise, it is '0'.
ad_id | beverage | impression_time | was_clicked |
---|---|---|---|
1 | Dr Pepper | 06/08/2022 00:00:00 | 1 |
2 | Canada Dry | 06/08/2022 00:05:00 | 0 |
3 | Snapple | 06/08/2022 00:10:00 | 0 |
4 | Dr Pepper | 06/08/2022 01:00:00 | 0 |
5 | Snapple | 06/08/2022 01:10:00 | 1 |
Write a SQL query that computes the click-through-rate as the percentage of being for each beverage. Round the results to the nearest two decimal places.
This PostgreSQL query first groups the beverage column. Then, for each group, it computes the sum of the column and divides it by the count of rows () in each group to obtain the click through rate. We multiply by to express the rate as a percentage and then round them to two decimal places using the function. The part is necessary to prevent integer division which would truncate the result and give incorrect data.
The data is grouped by beverage to compute the click through rate for each of them separately.
To practice another question about calculating rates, solve this TikTok SQL question on DataLemur's online SQL coding environment:
A full outer join returns all rows from both tables, including any unmatched rows, whereas an inner join only returns rows that match the join condition between the two tables.
For an example of each one, say you had sales data exported from Keurig Dr Pepper's Salesforce CRM stored in a datawarehouse which had two tables: and .
: retrieves rows from both tables where there is a match in the shared key or keys.
This query will return rows from the sales and tables that have matching customer id values. Only rows with matching values will be included in the results.
: retrieves all rows from both tables, regardless of whether there is a match in the shared key or keys. If there is no match, NULL values will be returned for the columns of the non-matching table.
Here is an example of a SQL full outer join using the sales and tables:
Keurig Dr Pepper has a wide variety of beverages and they want to analyze their sales data. From their sales transactions, calculate the total number of units sold for each product and identify the product that has the maximum sales.
sale_id | product_id | sale_date | units_sold |
---|---|---|---|
1 | 100 | 01/02/2021 | 50 |
2 | 101 | 02/03/2021 | 40 |
3 | 100 | 03/04/2021 | 70 |
4 | 102 | 04/05/2021 | 80 |
5 | 101 | 05/06/2021 | 10 |
product_id | product_name |
---|---|
100 | Dr Pepper |
101 | 7UP |
102 | A&W Root Beer |
product_name | total_units_sold |
---|---|
Dr Pepper | 120 |
7UP | 50 |
A&W Root Beer | 80 |
In this query, we are joining the and the tables based on the field. We then group the resulting table by and calculate the sum of for each group (product). We order the resulting rows by the column in decreasing order, to easily identify the product with the maximum sales.
The key to acing a Keurig Dr Pepper SQL interview is to practice, practice, and then practice some more! Beyond just solving the above Keurig Dr Pepper SQL interview questions, you should also solve the 200+ DataLemur SQL Interview Questions which come from companies like Microsoft, Google, Amazon, and tech startups.
Each exercise has multiple hints, step-by-step solutions and best of all, there is an online SQL code editor so you can right in the browser run your query and have it executed.
To prep for the Keurig Dr Pepper SQL interview it is also helpful to solve SQL questions from other food and facilities companies like:
But if your SQL foundations are weak, don't worry about jumping right into solving questions – refresh your SQL knowledge with this free SQL for Data Analytics course.
This tutorial covers topics including Subquery vs. CTE and sorting results with ORDER BY – both of these pop up routinely during Keurig Dr Pepper SQL interviews.
In addition to SQL interview questions, the other types of questions to prepare for the Keurig Dr Pepper Data Science Interview are:
To prepare for Keurig Dr Pepper Data Science interviews read the book Ace the Data Science Interview because it's got:
Don't forget about the behavioral interview – prep for that with this behavioral interview question bank.