10 Wolverine Worldwide SQL Interview Questions (Updated 2024)
At Wolverine Worldwide, SQL is used day-to-day for analyzing sales trends across retail outlets and optimizing inventory management based on real-time product performance data. That's why Wolverine Worldwide often tests SQL coding questions during interviews for Data Science, Data Engineering and Data Analytics jobs.
To help prep you for the Wolverine Worldwide SQL interview, we'll cover 10 Wolverine Worldwide SQL interview questions – scroll down to start solving them!
10 Wolverine Worldwide SQL Interview Questions
SQL Question 1: Identify Top Purchasing Customers for Wolverine Worldwide
As a business analyst for Wolverine Worldwide, a global footwear manufacturer, you are interested in identifying the "whale" customers. These are the customers who have made the most number of purchases within the last year. Write a SQL query to identify the top 10 customers who have made the most purchases within the last one year.
Assume you have access to the following two tables:
Example Input:
| order_id | customer_id | purchase_date | product_id | product_price |
|---|---|---|---|---|
| 5300 | 345 | 06/08/2021 | 5001 | 150.00 |
| 6780 | 755 | 06/10/2021 | 8102 | 200.00 |
| 8930 | 345 | 06/18/2021 | 5001 | 150.00 |
| 1120 | 189 | 07/26/2021 | 8102 | 200.00 |
| 8701 | 345 | 07/05/2021 | 8102 | 200.00 |
Example Input:
| customer_id | first_name | last_name |
|---|---|---|
| 345 | John | Doe |
| 755 | Jane | Doe |
| 189 | Michael | Smith |
Answer:
The above query first performs a JOIN operation between the and tables based on the field. The clause filters the data to only include the orders made within the last year. The clause groups the records by customer's first and last name, and the function calculates the total purchases made by each customer within the time period. The result is then ordered by the number of purchases in descending order and limited to the top 10 customers.
To solve a similar power-user data analysis problem question on DataLemur's free interactive SQL code editor, try this recently asked Microsoft SQL interview question:
SQL Question 2: Department vs. Company Salary
You're given a table of Wolverine Worldwide employee and department salary data. Write a SQL query to find departments where the departments average salary is higher than the company's average salary.
Try this question interactively on DataLemur:
The answer is LONG – 30+ lines of SQL. You can find a detailed solution here: Department vs. Company Salary.
SQL Question 3: What are some ways you can identify duplicates in a table?
"One creative way is to use the window function ROW_NUMBER() and partition by whichver columns you are loooking for duplicates in. If any row has a row_number() more than 1, then it's a duplicate!
You could also use COUNT(DISTINCT col1) vs. COUNT(*) and see if they return different numbers, indicating the prescence of duplicates!
SQL Question 4: Calculating Monthly Average Review Stars for Each Product
Wolverine Worldwide sells a wide range of footwear and apparel. In order to analyze customer satisfaction, they collect product reviews from customers. Each review includes a star rating (1-5 stars) and a submission date.
Your task is to write a SQL query that will calculate and display the average star rating for each product on a monthly basis. For the purpose of this question, you will focus on product 50001 and product 69852.
You will be analyzing the table. Assume this table has the following structure:
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 |
You should output the average star rating per product, per month.
Example Output:
| mth | product_id | avg_stars |
|---|---|---|
| 6 | 50001 | 3.50 |
| 6 | 69852 | 4.00 |
| 7 | 69852 | 2.50 |
Answer:
This query will first extract the month from the using the function. It will then group the reviews by the month and to calculate the average star rating () for each product on a monthly basis. The clause is used to limit the analysis to only product 50001 and product 69852. The function is used to calculate the average star rating.
p.s. Window functions show up super frequently during SQL interviews, so practice the 27+ window function questions on DataLemur
SQL Question 5: What does it mean for a DBMS to enforce atomicity, consistency, isolation, and durability?
A transaction is a one or more SQL commands which are executed as a singular unit if the transaction -commits- (or no execution hapens if the transaction -aborts-).
For transactions, a DBMS is supposed to enforce the follwing ACID properties: Atomicity, Consistency, Isolation, & Durability.
Here's what each one means:
- Atomicity: the transaction is completed in an all-or-nothing way (no partial commits)
- Consistency: the transaction is valid and follows all constraints and restrictions
- Isolation: the transaction doesn't affect another transaction
- Durability: the committed transactions is stored permanently in the DB (it doesn't dissapear!)
As you can see, it's pretty important for the multiple databases where Wolverine Worldwide store's it's data to be ACID-compliant!
SQL Question 6: Sales Analysis of Wolverine Worldwide
As a data analyst at Wolverine Worldwide, one of your tasks is to analyse the company's sales data. Wolverine Worldwide is a global footwear manufacturer that owns many well-known shoe brands like Sperry, Merrel, and Saucony.
You are given two tables: that records all the product sales, and that provides more details for each product. Your goal is to answer the following question:
What are the top 5 most sold products by category for the year 2022?
Example Input:
| product_id | product_name | category_id | brand_id |
|---|---|---|---|
| 50001 | Merrell Moab 2 | 1 | 001 |
| 69852 | Sperry Saltwater | 2 | 002 |
| 25136 | Saucony Ride | 3 | 003 |
| 78965 | Hush Puppies Chaste | 1 | 004 |
| 45632 | Chaco ZCloud | 2 | 005 |
Example Input:
| sales_id | product_id | sales_date | quantity |
|---|---|---|---|
| 1201 | 50001 | 02/02/2022 | 10 |
| 2345 | 50001 | 05/06/2022 | 5 |
| 3456 | 69852 | 04/01/2022 | 8 |
| 4578 | 25136 | 05/06/2022 | 7 |
| 5698 | 78965 | 02/02/2022 | 4 |
| 6547 | 45632 | 04/01/2022 | 6 |
| 7325 | 50001 | 05/06/2022 | 20 |
Answer:
To retrieve the top 5 most sold products by category for the year 2022, we can join the two tables on their common column which is , group by and , sum the , and then order by this sum in the descending order.
This query breaks down the sales by category and product for the year 2022 and then selects the top 5 records based on the total quantity sold in the descending order. As a result, it provides a list of top 5 most sold products by category for the year 2022.
SQL Question 7: What is a foreign key?
A foreign key is a field in a database table that links to the primary key of another table, establishing a connection between the two tables. For example, let's analyze Wolverine Worldwide's Google Ads campaigns data:
:
+------------+------------+------------+------------+ | ad_id | campaign_id| keyword | click_count| +------------+------------+------------+------------+ | 1 | 201 | Wolverine Worldwide reviews | 120 | | 2 | 202 | Wolverine Worldwide pricing | 150 | | 3 | 101 | buy Wolverine Worldwide | 65 | | 4 | 101 | Wolverine Worldwide alternatives | 135 | +------------+------------+------------+------------+
is a foreign key that connects to the of the corresponding Google Ads campaign. This establishes a relationship between the ads and their campaigns, enabling easy querying to find which ads belong to a specific campaign or which campaigns a specific ad belongs to.
The table may also have multiple foreign keys that reference primary keys in other tables. For example, and foreign keys could be used to link each ad to its ad group and the Google Ads account that the campaigns belong to, respectively.
SQL Question 8: Calculate Click-Through-Rate
Wolverine Worldwide, a company known for its various footwear brands, has recently implemented a new digital marketing strategy. They've created digital ads and are tracking the clicks and views of these advertisements. The marketing team is working on understanding the effectiveness of these digital ads, and as part of their analysis, they want to calculate the Click-Through-Rate (CTR) for each ad.
CTR is calculated by taking the number of clicks an advertisement gets, divided by the number of views it gets, then multiplied by 100 to get it in percentage form. Please write a SQL query using PostgreSQL to calculate the CTR for each ad.
Please use the following tables:
Example Input:
| click_id | ad_id | user_id | click_time |
|---|---|---|---|
| 1001 | 1 | 345 | 06/04/2022 19:45:23 |
| 1002 | 2 | 456 | 06/15/2022 20:15:05 |
| 1003 | 3 | 345 | 07/13/2022 12:05:43 |
| 1004 | 1 | 678 | 07/04/2022 08:28:11 |
| 1005 | 2 | 890 | 06/17/2022 22:44:07 |
Example Input:
| view_id | ad_id | user_id | view_time |
|---|---|---|---|
| 2001 | 1 | 123 | 06/02/2022 15:23:17 |
| 2002 | 2 | 456 | 07/12/2022 09:30:45 |
| 2003 | 3 | 789 | 06/01/2022 11:55:40 |
| 2004 | 1 | 345 | 07/22/2022 04:34:20 |
| 2005 | 2 | 890 | 06/05/2022 00:12:11 |
Answer:
The query first counts the number of ad clicks and views for each ad separately in subqueries. Then, it joins these two derived tables on . The ensures that even if an ad does not have any clicks or views, it still gets included in the final result.
The function ensures that values (which occur if an ad does not have any clicks or views) are replaced with 0 for the counts, and 1 for the denominator of the CTR calculation to avoid division by zero. This we multiply by 100 to get the CTR in percentage form.
Finally, the results are grouped by and ordered for easier reading.
To solve a related SQL problem on DataLemur's free online SQL coding environment, solve this Facebook SQL Interview question:
SQL Question 9: Product Sales per Region
You are given two tables, and . The table has columns , , and . The table includes , , , and .
For this scenario, Wolverine Worldwide wants to analyze its product sales performance in different regions.
Your task is to write a SQL query to find the total sales amount for each product name and region, sorted by region in ascending order and total sales in descending order.
Example Input:
| product_id | product_name | brand_id |
|---|---|---|
| 001 | Mome Backpack | WWWW1 |
| 002 | Hiker Boots | WWWW2 |
| 003 | Jogger Shoes | WWWW1 |
| 004 | Waterproof Jacket | WWWW2 |
Example Input:
| sale_id | region | product_id | sale_amount |
|---|---|---|---|
| 1001 | North America | 001 | 150 |
| 1002 | Europe | 002 | 200 |
| 1003 | Asia | 003 | 250 |
| 1004 | North America | 002 | 300 |
| 1005 | Europe | 004 | 350 |
Answer:
To solve this problem, we can use a operation between the and tables on , and perform a operation on and . A aggregate function will be used to calculate the total sales.
Example Output:
| region | product_name | total_sales |
|---|---|---|
| Asia | Jogger Shoes | 250 |
| Europe | Waterproof Jacket | 350 |
| Europe | Hiker Boots | 200 |
| North America | Hiker Boots | 300 |
| North America | Mome Backpack | 150 |
These results provide a detailed breakdown of product sales by region, which could be instrumental in driving WW's marketing and sales strategy.
SQL Question 10: What distinguishes an inner join from a full outer join?
An inner join only includes rows from both tables that satisfy the join condition, whereas a full outer join includes all rows from both tables, regardless of whether they match the join condition or not.
For a concrete example, imagine you had two database tables, an advertising campaigns table which had information on Google Ads keywords and how much was bid for each keyword, and a Wolverine Worldwide sales table, which has data on how many products were sold and which Google Ads keyword drove that sale.
: retrieves rows from both tables where there is a match in the shared key or keys. For example, an INNER JOIN between the table and the table could be performed using the keyword column as the shared key. This would retrieve only the rows where the keyword in the table matches the keyword in the table.
: retrieves all rows from both tables, regardless of whether there is a match in the shared key or keys. If there is no match, values will be returned for the columns of the non-matching table.
Preparing For The Wolverine Worldwide SQL Interview
The best way to prepare for a Wolverine Worldwide SQL interview is to practice, practice, practice.
Beyond just solving the above Wolverine Worldwide SQL interview questions, you should also solve the 200+ tricky sql questions on DataLemur which come from companies like FAANG (FB, Apple, Amazon, Netflix, Google).
Each SQL question has hints to guide you, full answers and most importantly, there is an online SQL code editor so you can right online code up your query and have it executed.
To prep for the Wolverine Worldwide SQL interview it is also wise to practice SQL problems from other apparel companies like:
But if your SQL query skills are weak, forget about going right into solving questions – strengthen your SQL foundations with this free SQL for Data Analytics course.
This tutorial covers things like filtering with LIKE and RANK vs. DENSE RANK – both of which come up routinely during SQL job interviews at Wolverine Worldwide.
Wolverine Worldwide Data Science Interview Tips
What Do Wolverine Worldwide Data Science Interviews Cover?
In addition to SQL interview questions, the other types of questions to practice for the Wolverine Worldwide Data Science Interview include:
- AB Testing & Experimentation Questions
- Coding Questions in Python or R
- Product Analytics Questions
- ML Modelling Questions
- Behavioral & Resume-Based Questions
How To Prepare for Wolverine Worldwide Data Science Interviews?
The best way to prepare for Wolverine Worldwide Data Science interviews is by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from Microsoft, Amazon & startups
- A Crash Course on Python, SQL & ML
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