8 Cimpress SQL Interview Questions (Updated 2024)
At Cimpress, SQL is often used for analyzing print production data for cost optimization, and to find bottlenecks in the manufacturing process. So, it shouldn't surprise you that Cimpress often tests SQL problems in interviews for Data Science and Data Engineering positions.
Thus, to help you prepare for the Cimpress SQL interview, we'll cover 8 Cimpress SQL interview questions – how many can you solve?
8 Cimpress SQL Interview Questions
SQL Question 1: Average Star Rating by Month for Each Product
Assume you are working as a Data Analyst for Cimpress, a tech company that provides customized, on-demand print and product designs. One of your tasks is to analyze the trends of product reviews to understand user behavior and product performance.
Given a table , where each row represents a user review for a product, write a SQL query that calculates the average star rating for each product by month.
Example Input:
Example Output:
Answer:
This query uses the EXTRACT function to get the month from the and groups the reviews first by the extracted month and then by the product ID. The AVG function is used to calculate the average star rating for each product on a monthly basis. All results are then ordered by the month and product ID for easier reading and analysis.
Pro Tip: Window functions are a popular SQL interview topic, so practice every window function problem on DataLemur
SQL Question 2: Analyzing Product Sales at Cimpress
Cimpress is a leading online supplier of high-quality printed marketing materials, digital marketing and promotional products. They are trying to understand their product sales over time to better manage their product portfolio. You are tasked with the job of designing a system to track this data.
Given two tables:
| product_id | product_name | category |
|---|---|---|
| 50001 | "Business Card" | "Printing" |
| 69852 | "Custom T-Shirt" | "Merchandising" |
| 41563 | "Promotional Pen" | "Merchandising" |
| sale_id | product_id | sale_date | quantity |
|---|---|---|---|
| 1924 | 50001 | 11/12/2021 00:00:00 | 800 |
| 9742 | 69852 | 12/05/2021 00:00:00 | 220 |
| 7583 | 50001 | 01/22/2022 00:00:00 | 700 |
| 2435 | 69852 | 02/07/2022 00:00:00 | 350 |
| 6210 | 41563 | 03/01/2022 00:00:00 | 500 |
Create a SQL query to generate a monthly report that shows total sales quantity for each product. The report should include the product_id, product_name, month of the sale_date and total_quantity. The month should be represented as the calendar month in the year (1-12).
Answer:
The above query joins the and tables on the field, then groups the data by , and the month of . It aggregates the of sales for each product per month using the function. The clause sorts the data first by the calendar month, and then in descending order of total sales quantity.
SQL Question 3: What are the different kinds of joins in SQL?
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 Cimpress's Salesforce CRM stored in a PostgreSQL database, and had access to two tables: and .
- : retrieves rows from both tables where there is a match in the shared key or keys. For example, an INNER JOIN between the Sales table and the Customers table would retrieve only the rows where the customer_id in the Sales table matches the customer_id in the table.
- : retrieves all rows from the left table (in this case, the sales table) and any matching rows from the right table (the table). If there is no match in the right table, NULL values will be returned for the right table's columns.
- : retrieves all rows from the right table (in this case, the customers table) and any matching rows from the left table (the sales table). If there is no match in the left table, NULL values will be returned for the left table's columns.
- : 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.
SQL Question 4: Average Number of Orders per Customer per Month
At Cimpress, an e-commerce company specialized in mass customization of physical products, the company could be interested in finding out the average number of orders placed by each customer per month.
Example Input:
| order_id | customer_id | order_date | product_id | quantity |
|---|---|---|---|---|
| 1001 | 201 | 2022-08-01 | 3001 | 50 |
| 1002 | 202 | 2022-08-02 | 3002 | 100 |
| 1003 | 203 | 2022-08-03 | 3003 | 75 |
| 1004 | 201 | 2022-08-04 | 3001 | 100 |
| 1005 | 202 | 2022-08-05 | 3002 | 50 |
Example Output:
| month | customer | avg_orders |
|---|---|---|
| 8 | 201 | 2 |
| 8 | 202 | 2 |
| 8 | 203 | 1 |
Answer:
This PostgreSQL query uses the EXTRACT function to get the month part from the order date. It then groups the records by month and customer_id to calculate the average number of orders per customer per month. Please note that quantity here represents the number of units ordered per order, so the average calculated is on the number of units ordered not on the number of orders. To compute average order number we would need to count distinct order ids instead. Also, data is shown only for a single month for simplification, the real-world data will span multiple months.
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for analyzing customer orders for specific items or this Walmart Histogram of Users and Purchases Question which is similar for aggregating customer's purchases monthly.
SQL Question 5: Does a typically give the same results as a ?
For all practical purposes, and do NOT produce the same results.
While both are similar in that they combine two tables, you can think of joins as increasing the width of the resulting table (you'll have more columns in the result set for a left/inner/right join), whereas a union is used to combine rows which increases the height of the result set but keeps the column count the same.
If your familiar with set operations, you can think of a as set addition, whereas a is set multiplication.
SQL Question 6: Calculate the Click-Through Conversion Rate for Each Ad Campaign
Cimpress, an expansive company with interest in marketing strategies, has carried out multiple digital ad campaigns. For each ad displayed, there's a probability that a user will click it (a click-through) and, among the users who click an ad, there's a probability they will add an advertised product to their cart (a conversion).
Consequently, Cimpress would like to analyze the Click-Through Conversion Rates (CTCR) for each ad campaign, which we define as the number of conversions (adding a product to the cart) per click-through.
Tables:
Example Input:
| campaign_id | campaign_name |
|---|---|
| 1 | Holiday Season Sale |
| 2 | Summer Special |
| 3 | Black Friday Fever |
Example Input:
| user_id | campaign_id | click_date |
|---|---|---|
| 217 | 1 | 06/07/2023 00:00:00 |
| 102 | 3 | 06/09/2023 00:00:00 |
| 309 | 1 | 06/15/2023 00:00:00 |
| 456 | 2 | 06/20/2023 00:00:00 |
| 872 | 1 | 07/07/2023 00:00:00 |
Example Input:
| user_id | campaign_id | conversion_date |
|---|---|---|
| 217 | 1 | 06/07/2023 00:00:00 |
| 309 | 1 | 06/15/2023 00:00:00 |
| 309 | 1 | 07/03/2023 00:00:00 |
| 456 | 2 | 06/22/2023 00:00:00 |
Answer:
The answer query first performs a left join of the table with to associate each campaign with corresponding user clicks. A further left join with table associates each click with a corresponding conversion, if exists. The conversion condition also checks that a conversion's date is not earlier than the click date as it's logically impossible. Grouped by , for each campaign, we count the distinct who clicked and conversion, and the click-Through Conversion Rate (CTCR) is then calculated as the number of conversions divided by the number of clicks.
Please note that in the resulting column , if no user has clicked on a particular campaign, the would be NULL due to a zero division error.
To solve a related problem on DataLemur's free interactive coding environment, try this SQL interview question asked by Facebook:
SQL Question 7: How do the 'BETWEEN' and 'IN' commands differ?
While both the and operators are used to filter data based on some criteria, selects for values within a given range, whereas for it checks if the value is in a given list of values.
For example, say you had a table called , which had the salary of the employee, along with the country in which they reside.
To find all employees who made between 120k, you could use the operator:
To find all employees that reside in the US or Canada, you could use the operator:
SQL Question 8: Analyzing Customer Purchases and Feedback
You are given two tables, and .
The table contains the customer orders with the following columns:
| order_id | customer_id | order_date | product_id | price |
|---|---|---|---|---|
| 101 | 1 | 2022-10-01 | 2005 | 10.50 |
| 102 | 2 | 2022-10-05 | 3002 | 15.75 |
| 103 | 3 | 2022-10-10 | 5003 | 5.30 |
| 104 | 4 | 2022-11-10 | 3002 | 8.50 |
| 105 | 5 | 2022-11-15 | 2005 | 11.20 |
The table stores customer feedback on their orders and contains the following columns:
| review_id | order_id | rating | review_date |
|---|---|---|---|
| 1001 | 101 | 5 | 2022-10-02 |
| 1002 | 102 | 3 | 2022-10-06 |
| 1003 | 103 | 4 | 2022-10-11 |
| 1004 | 104 | 5 | 2022-11-12 |
| 1005 | 105 | 2 | 2022-11-16 |
Write a SQL query to find out the average rating per product, the total revenue for each product, and the number of reviews each product has received, sorted by product_id.
Answer:
This query first joins the and tables using the column. It then groups the resulting joined table by , and calculates the average rating, total revenue, and the number of reviews each product has received. Finally, it orders the results by for easier interpretation. To calculate the average rating, it uses the function, along with for total revenue and for the review counts.
Because join questions come up frequently during SQL interviews, take a stab at an interactive Spotify JOIN SQL question:
How To Prepare for the Cimpress SQL Interview
The best way to prepare for a SQL interview, besides making sure you have strong SQL fundamentals, is to practice a ton of real SQL questions that were asked in recent job interviews.
Besides solving the earlier Cimpress SQL interview questions, you should also solve the 200+ tricky sql questions on DataLemur which come from companies like Facebook, Google, and VC-backed startups.
Each exercise has hints to guide you, step-by-step solutions and best of all, there is an online SQL coding environment so you can right online code up your SQL query and have it checked.
To prep for the Cimpress SQL interview you can also be useful to practice SQL questions from other tech companies like:
However, if your SQL query skills are weak, don't worry about going right into solving questions – refresh your SQL knowledge with this SQL tutorial for Data Analytics.
This tutorial covers things like handling date/timestamp data and filtering data with WHERE – both of which pop up routinely in Cimpress interviews.
Cimpress Data Science Interview Tips
What Do Cimpress Data Science Interviews Cover?
In addition to SQL query questions, the other types of questions tested in the Cimpress Data Science Interview are:
- Statistics & Probability Questions
- Python or R Programming Questions
- Product Data Science Interview Questions
- Machine Learning and Predictive Modeling Questions
- Behavioral & Resume-Based Questions
How To Prepare for Cimpress Data Science Interviews?
The best way to prepare for Cimpress Data Science interviews is by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from FAANG & startups
- A Refresher covering Product Analytics, SQL & ML
- Great Reviews (900+ reviews, 4.5-star rating)