9 Hasbro SQL Interview Questions (Updated 2024)
At Hasbro, SQL is used to analyze sales data by identifying top-selling toys, tracking seasonal trends, and forecasting demand based on historical sales patterns. It is also used for querying customer databases to understand consumer behavior patterns such as purchase frequency and demographic preferences, which is why Hasbro asks SQL problems during interviews for Data Science, Analytics, and & Data Engineering jobs.
Thus, to help prep you for the Hasbro SQL interview, we've collected 9 Hasbro SQL interview questions in this blog.
9 Hasbro SQL Interview Questions
SQL Question 1: Identify "Whale Users" for Hasbro
Hasbro, a multinational conglomeration company that primarily sells toys and board games, wants to identify their "whale users". These users are considered very important to Hasbro because they purchase a large quantity of products very frequently. Write a SQL query to identify customers who have made more than 100 purchases in the last six months.
Here's the structure for your tables:
Example Input Table:
| customer_id | first_name | last_name |
|---|---|---|
| 1 | John | Doe |
| 2 | Jane | Smith |
| 3 | Jim | Brown |
Example Input Table:
| purchase_id | customer_id | product_id | purchase_date |
|---|---|---|---|
| 1001 | 1 | 50001 | 01/01/2022 |
| 1002 | 1 | 69852 | 01/02/2022 |
| 1003 | 2 | 50001 | 01/05/2022 |
| ... | ... | ... | ... |
Assume there would be more than 50 rows in each of the above tables and more than two tables.
Answer:
In this query:
- We target all purchases that occurred in the last six months.
- We join the table with the table on the common field.
- The query groups by , thereby calculating the purchase count for each customer in the six months period.
- Only results for customers with more than 100 purchases (the whale users) are selected, and these are sorted by their purchase count in descending order.
To work on another SQL customer analytics question where you can solve it interactively and have your SQL code instantly graded, try this Walmart SQL Interview Question:
SQL Question 2: Second Highest Salary
Given a table of Hasbro employee salary information, write a SQL query to find the 2nd highest salary among all employees.
Hasbro Example Input:
| employee_id | salary |
|---|---|
| 1 | 2500 |
| 2 | 800 |
| 3 | 1000 |
| 4 | 1200 |
Example Output:
| second_highest_salary |
|---|
| 1200 |
Test your SQL query for this interview question and run your code right in DataLemur's online SQL environment:
Answer:
You can find a detailed solution here: 2nd Highest Salary.
SQL Question 3: How do the window functions LEAD() and LAG() differ?
Both the and window functions are used to access a row at a specific offset from the current row.
However, the function retrieves a value from a row that follows the current row, whereas the function retrieves a value from a row that precedes the current row.
Say you had a table of salary data for Hasbro employees:
| name | salary |
|---|---|
| Amanda | 130000 |
| Brandon | 90000 |
| Carlita | 80000 |
You could use the function to output the salary of each employee, along with the next highest-paid employee:
This would yield the following output:
| name | salary | next_salary |
|---|---|---|
| Amanda | 130000 | 90000 |
| Brandon | 90000 | 80000 |
| Carlita | 80000 | NULL |
Swapping for would get you the salary of the person who made just more than you:
| name | salary | next_salary |
|---|---|---|
| Amanda | 130000 | NULL |
| Brandon | 90000 | 130000 |
| Carlita | 80000 | 90000 |
SQL Question 4: Analyze Monthly Average Star Rating for Products
Hasbro would like to better understand the monthly average star ratings for their products based on the reviews submitted by users. Please write a SQL query to analyze their reviews dataset, calculate the monthly average star rating for each product, and rank them within each month based on the average star rating.
Please note the following keys in the 'reviews' table:
- : a unique identifier for each review
- : the identifier of the user who submitted the review
- : the date when the review was submitted
- : the identifier of the product being reviewed
- : the star rating of the review (from 1 to 5)
Example Input:
| review_id | user_id | submit_date | product_id | stars |
|---|---|---|---|---|
| 6171 | 123 | 06/08/2022 00:00:00 | 50001 | 4 |
| 7802 | 265 | 06/10/2022 00:00:00 | 69852 | 4 |
| 5293 | 362 | 06/18/2022 00:00:00 | 50001 | 3 |
| 6352 | 192 | 07/26/2022 00:00:00 | 69852 | 3 |
| 4517 | 981 | 07/05/2022 00:00:00 | 69852 | 2 |
Example Output:
| mth | product_id | avg_stars | rank |
|---|---|---|---|
| 6 | 50001 | 3.50 | 1 |
| 6 | 69852 | 4.00 | 2 |
| 7 | 69852 | 2.50 | 1 |
Answer:
This query first extracts the month from the and calculates the average for each in each month. It then applies a window function to assign a rank for each product within each month based on the calculated average stars. The products with higher average stars receive lower ranks, indicating they are more favorably reviewed. Finally, the results are ordered by the month in ascending order and then by the rank in ascending order.
For more window function practice, solve this Uber SQL Interview Question on DataLemur's interactive SQL code editor:
SQL Question 5: Can you explain the distinction between a left and right join?
Both left and right joins in SQL allow you to combine data from different tables based on a shared key or set of keys. For a concrete example of the difference between these two join types, say you had sales data exported from Hasbro's Salesforce CRM stored in a PostgreSQL database, and had access to two tables: and .
-
: 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.
SQL Question 6: Calculate Product Click-through and Conversion Rates for Hasbro
As a data analyst at Hasbro, your job is to understand the behavior of users when they browse products on the Hasbro website. Specifically, you are interested in measuring the click-through rate (CTR) and conversion rate (CR) for the range of Hasbro products.
The CTR is defined as the number of clicks on a product listing divided by the number of views on the product listing. The CR is defined as the number of purchases of a product divided by the number of clicks on the product listing.
For this task, assume that we have two tables and . records every time a user views a product and records each time a user purchases a product.
Example Input:
| view_id | user_id | view_date | product_id |
|---|---|---|---|
| 4171 | 123 | 06/08/2022 00:00:00 | 51001 |
| 6802 | 265 | 06/10/2022 00:00:00 | 51522 |
| 7693 | 362 | 06/18/2022 00:00:00 | 51001 |
| 7352 | 192 | 07/26/2022 00:00:00 | 51522 |
| 9517 | 981 | 07/05/2022 00:00:00 | 51522 |
Example Input:
| purchase_id | user_id | purchase_date | product_id |
|---|---|---|---|
| 6171 | 123 | 06/08/2022 00:00:00 | 51001 |
| 7802 | 265 | 06/11/2022 00:00:00 | 51522 |
| 5293 | 362 | 06/18/2022 00:00:00 | 51001 |
| 6352 | 192 | 07/27/2022 00:00:00 | 51522 |
| 4517 | 981 | 07/05/2022 00:00:00 | 51522 |
Write a PostgreSQL query to compute the CTR and CR for each product.
Answer:
This query first aggregates the number of clicks (views) and purchases for each product_ID, then joins the results on product_id. The conversion rate is calculated by dividing the number of purchases by the number of clicks for the same product, converting the result into a percentage. The click-through rate is calculated by dividing the number of clicks on a specific product by the total number of product views, converting the result into a percentage.
To practice a similar problem about calculating rates, solve this TikTok SQL Interview Question on DataLemur's interactive SQL code editor:
SQL Question 7: What sets a cross join apart from a natural join?
A cross join is a type of JOIN operation in SQL that creates a new table by combining each row from the first table with every row from the second table. It is also known as a cartesian join.
For example, say you worked on the Marketing Analytics team at Hasbro, and needed to understand what advertising copy (text) goes best with what advertising creative (the photo that gets posted with the advertisement copy).
Here is an example of a cross-join between a table of ad_copy and a table of ad_creative:
A natural join, on the other hand, is a type of JOIN that combines rows from two or more tables based on their common columns. It is called a "natural" join because it is based on the natural relationship that exists between the common columns in the tables being joined.
For an example of each one, say you had sales data exported from Hasbro's Salesforce CRM stored in a datawarehouse which had two tables: and .
An (which is a type of natural join) combines the two tables on the common
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.
One main difference between cross joins and natural joins is that cross joins do not require any common columns between the tables being joined, while natural joins do. Another difference is that cross joins can create very large tables if the input tables have a large number of rows, while natural joins will only return a table with the number of rows equal to the number of matching rows in the input tables.
SQL Question 8: Analyzing Customer Toy Purchase and Associated Reviews Data
Your goal in this question is to write a SQL query that would analyze the customer database for a company like Hasbro. The company needs to understand the frequency and average rating of each toy sold. The two tables containing the necessary information are the and tables, where the table connects these two with each corresponding toy review.
Make a SQL query to join these tables and find out the average review score and the total count of reviews for each toy.
Table:
| customer_id | name |
|---|---|
| 1 | John Doe |
| 2 | Jane Doe |
| 3 | Mary Jane |
| 4 | Peter Parker |
Table:
| toy_id | name |
|---|---|
| 101 | Monopoly |
| 102 | Cluedo |
| 103 | Jenga |
Table:
| purchase_id | customer_id | toy_id | review_score |
|---|---|---|---|
| 201 | 1 | 101 | 4 |
| 202 | 2 | 102 | 5 |
| 203 | 2 | 101 | 3 |
| 204 | 3 | 101 | 4 |
| 205 | 4 | 103 | 5 |
Answer:
The above query would first join the and tables via a LEFT JOIN on the column. Then, for each toy (grouping by ), it calculates the total counts of purchases and the average review score, and orders the result by the count of purchases in descending order. It shows the toy id, name of the toy, total purchases and the average review score out of the data we have.
Because join questions come up frequently during SQL interviews, take a stab at an interactive SQL join question from Spotify:
SQL Question 9: Sales Analysis for Hasbro's Products
Hasbro, a company that mainly deals with toy and board game manufacturing, wants to perform in-depth analysis of their sales data. They want to determine each product's average sales amount to the closest dollar, the absolute difference between maximum and minimum sale price, and the percentage change in total sales price between two consecutive quarters (Q2 and Q3) of 2021 for each product. Ignore any product that did not have sales in both quarters.
Provide SQL queries that can help Hasbro answer these questions.
Example Input:
| sale_id | product_id | sale_date | sale_price |
|---|---|---|---|
| 790 | 1001 | 2021-04-12 | 25.19 |
| 621 | 1002 | 2021-04-22 | 35.90 |
| 811 | 1001 | 2021-05-20 | 26.55 |
| 972 | 1003 | 2021-06-30 | 15.00 |
| 547 | 1001 | 2021-07-25 | 24.10 |
| 589 | 1001 | 2021-08-14 | 27.00 |
| 471 | 1002 | 2021-09-03 | 33.60 |
Answer:
This SQL query calculates each product's average sales price rounded to the closest dollar, the absolute difference between maximum and minimum sale price, and the percentage change in total sales price between Q2 and Q3 of the year 2021. The FILTER clause is used to calculate sums for each quarter separately. Products that didn't sell in both quarters are excluded.
To practice a very similar question try this interactive Wayfair Y-on-Y Growth Rate Question which is similar for calculating percentage change in sales data over time frames or this Amazon Highest-Grossing Items Question which is similar for determining top-performing products based on sales.
Preparing For The Hasbro SQL Interview
The best way to prepare for a Hasbro SQL interview is to practice, practice, practice. Besides solving the above Hasbro SQL interview questions, you should also solve the 200+ SQL Interview Questions on DataLemur which come from companies like Amazon, JP Morgan, and consumer good companies like Hasbro.
Each exercise has hints to guide you, step-by-step solutions and best of all, there's an online SQL code editor so you can right in the browser run your SQL query answer and have it executed.
To prep for the Hasbro SQL interview it is also wise to practice SQL questions from other consumer good companies like:
Explore the latest happenings at Hasbro and discover how they're inspiring imagination and creativity!
In case your SQL coding skills are weak, forget about going right into solving questions – go learn SQL with this DataLemur SQL Tutorial.
This tutorial covers SQL concepts such as using ORDER BY and filtering data with boolean operators – both of which show up routinely during Hasbro SQL interviews.
Hasbro Data Science Interview Tips
What Do Hasbro Data Science Interviews Cover?
In addition to SQL query questions, the other topics to prepare for the Hasbro Data Science Interview include:
- Probability & Stats Questions
- Python Pandas or R Coding Questions
- Product & Business Analytics Questions
- ML Interview Questions
- Behavioral Questions centered on Hasbro culture and values
How To Prepare for Hasbro Data Science Interviews?
To prepare for Hasbro Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions taken from Google, Microsoft & tech startups
- a refresher on SQL, Product-Sense & ML
- over 1000+ 5-star reviews on Amazon
Don't ignore the behavioral interview – prepare for it using this guide on behavioral interview questions.