10 Kontoor Brands SQL Interview Questions (Updated 2024)
Kontoor Brands employees use SQL all the damn time for analyzing and managing inventory data, as well as predicting consumer behavior trends in the fashion industry. Because of this, Kontoor Brands frequently asks SQL questions during interviews for Data Science and Data Engineering positions.
Thus, to help you prep, we've collected 10 Kontoor Brands SQL interview questions – able to answer them all?
10 Kontoor Brands SQL Interview Questions
SQL Question 1: Discovering Kontoor Brands Power Customers
In Kontoor Brands, a "power customer" is defined as a user who makes purchases and leaves reviews at a frequency much higher than the average. For this exercise, find users who've made more than 20 purchases within the last month and who have also left more than 10 reviews within the same period. The frequency threshold here is adjustable according to the specific needs of Kontoor Brands.
Example Input:
| purchase_id | user_id | purchase_date | product_id |
|---|---|---|---|
| 101 | 123 | 06/15/2022 00:00:00 | 50001 |
| 102 | 265 | 06/17/2022 00:00:00 | 69852 |
| 103 | 123 | 06/19/2022 00:00:00 | 50001 |
| 104 | 192 | 06/23/2022 00:00:00 | 69852 |
| 105 | 265 | 06/24/2022 00:00:00 | 5001 |
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 |
Answer:
This query first determines the purchase and review counts for each user within the last month in separate temporary tables. It then joins these tables to determine users who both bought and reviewed more than the set amount within this time frame. The final part of the query displays the identifiers of these users.
To solve a similar VIP customer analysis question on DataLemur's free interactive coding environment, try this Microsoft Azure Cloud SQL Interview Question:
SQL Question 2: 2nd Largest Salary
Assume you had a table of Kontoor Brands employee salary data. Write a SQL query to find the 2nd highest salary amongst all the .
Kontoor Brands Example Input:
| employee_id | salary |
|---|---|
| 1 | 2500 |
| 2 | 800 |
| 3 | 1000 |
| 4 | 1200 |
Example Output:
| second_highest_salary |
|---|
| 1200 |
You can solve this interview question interactively on DataLemur:
Answer:
You can find a detailed solution here: 2nd Highest Salary.
SQL Question 3: Can you describe a cross-join and its purpose?
A cross-join, also known as a cartesian join, is like a mad scientist's laboratory experiment gone wild. It takes two tables and mixes them together to create a crazy new table with every possible combination of rows from the original tables.
Here's an example:
If you have 20 products and 10 colors, that's 200 rows right there! Cross-joins are great for generating all possible combinations, but they can also create really big tables if you're not careful. Just like a mad scientist, use your powers wisely!
SQL Question 4: Calculate Average Ratings of Products Each Month
You are given a table which keeps a record of all product reviews made by customers of Kontoor Brands. Each row records a customer's review with a rating (from 1 to 5 stars) for a certain product.
The table is structured as follows –
| review_id | user_id | submit_date | product_id | stars |
|---|---|---|---|---|
| 1038 | 513 | 2022-06-09 00:00:00 | 70001 | 3 |
| 2591 | 862 | 2022-07-18 00:00:00 | 50012 | 5 |
| 3004 | 331 | 2022-06-20 00:00:00 | 70001 | 2 |
| 1556 | 461 | 2022-07-06 00:00:00 | 35062 | 4 |
| 5973 | 298 | 2022-06-15 00:00:00 | 50012 | 4 |
| 8092 | 129 | 2022-07-25 00:00:00 | 35062 | 3 |
Write an SQL query that calculates the average rating (up to 2 decimal places) of each product by month from the table. The result should contain three columns – month (), product id () and average stars (). Order the result by month, then by product id.
The output should look something like this:
| mth | product_id | avg_stars |
|---|---|---|
| 6 | 50012 | 4.00 |
| 6 | 70001 | 2.50 |
| 7 | 35062 | 3.50 |
| 7 | 50012 | 5.00 |
Answer:
The SQL window function is used here to calculate the average stars for each product per month. This calculation is performed for each group of records having the same product_id and month. The function is then used to limit the decimal places to 2 for average stars. Ordering is done first on the basis of month, and then product id. Window functions can prove to be very useful in such analysis, providing the ability to perform complex data aggregations without the need for subqueries.
p.s. Window functions show up pretty often during SQL interviews, so practice the 27+ window function questions on DataLemur
SQL Question 5: How do relational and non-relational databases differ?
A non-relational (NoSQL) database is any database that does not use the typical tabular format of rows and columns like in relational databases.
While knowing the four different types of NoSQL databases is beyond the scope of most Data Analyst & Data Science interviews, Data Engineers at Kontoor Brands should vaguely refresh these concepts:
- Document Databases – this database is designed for storing and querying retrieving document data (where each key is associated with a flexible document)
- Key-Value Stores – these databases uses keys where each key is associated with only one value in a collection (similar to a Python dictionary data structure!)
- Wide-Column Stores – this database uses tables, rows, and columns, but unlike a relational database, the names and format of the columns can vary from row to row within the same table. Remember: "-With great flexibility comes great responsiblity-" – Batman if he was learning about NoSQL databases!
- Graph Stores – represents data in terms of the graph data structure, with nodes and edges between entities
SQL Question 6: Calculating Average Sales
As an analyst at Kontoor Brands, you have been asked to identify how well different products are selling. Specifically, your task is to calculate the average sales volume per product category for each month of the year.
This information will help leadership understand which types of products sell best at different times of the year. For this task, use the table which includes the following columns: (unique identifier of each sale), (identifier of the sold product), (identifier of the product category), (number of sold items in this sale), and (the date this sale happened).
Example Input:
| sale_id | product_id | category_id | quantity | sale_date |
|---|---|---|---|---|
| 101 | 5001 | 1 | 10 | 01/07/2022 |
| 102 | 5002 | 1 | 8 | 01/08/2022 |
| 103 | 5003 | 2 | 6 | 01/12/2022 |
| 104 | 5004 | 2 | 7 | 02/05/2022 |
| 105 | 5005 | 3 | 15 | 02/11/2022 |
| 106 | 5006 | 3 | 10 | 02/13/2022 |
| 107 | 5007 | 1 | 9 | 02/25/2022 |
Answer:
You can calculate the average sales volume per product category for each month of the year using the function within a clause in your SQL query. The function can be used to get the month from the .
This query first separates the records into groups based on the month of and . Then it calculates the average sales volume (average quantity) for each of these groups. Finally, it returns the month, category id and the calculated average sales volume for each group, sorted by the month and category id.
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for product category-based sales analysis or this Amazon Average Review Ratings Question which is similar for <monthly grouping and average calculation.
SQL Question 7: What are the three different normal forms?
The three levels of database normalization (also known as normal forms) are:
First Normal Form (1NF):
- Each column should contain the same type of data (no mixing strings vs. integers)
- Each column in a table contains a single value (no lists or containers of data)
- Each row in the table is unique
Second Normal Form (2NF)
- It's in 1st Normal Form
- All non-key attributes are dependent on the primary key
Said another way, to achieve 2NF, besides following the 1NF criteria, the columns should also be dependent only on that table's primary key.
Third normal form (3NF) if it meets the following criteria:
- It's in 2nd Normal Form
- There are no transitive dependencies in the table.
A transitive dependency means values in one column are derived from data in another column. For example, it wouldn't make sense to keep a column called ""vehicle's age"" and ""vehicle's manufacture date" (because the vehicle's age can be derived from the date it was manufactured).
Although there is a 4NF and 5NF, you probably don't need to know it for the Kontoor Brands interview.
SQL Question 8: Calculate the Highest Selling Product for Each Month
As a data analyst at Kontoor Brands which is known for its apparel products, you are asked to keep track of how each product is doing in terms of sales. Write a SQL query that identifies the highest selling product for each month.
Example Input:
| sale_id | date | product_id | quantity |
|---|---|---|---|
| 1 | 01/01/2021 | 101 | 7 |
| 2 | 01/15/2021 | 102 | 5 |
| 3 | 01/30/2021 | 101 | 9 |
| 4 | 02/11/2021 | 103 | 6 |
| 5 | 02/20/2021 | 101 | 3 |
| 6 | 02/28/2021 | 103 | 8 |
| 7 | 03/15/2021 | 102 | 7 |
| 8 | 03/30/2021 | 101 | 2 |
Example Output:
| month | best_selling_product | total_quantity |
|---|---|---|
| 1 | 101 | 16 |
| 2 | 103 | 14 |
| 3 | 102 | 7 |
Answer:
This PostgreSQL query works by first adding a new column, "month", to the data, which is the month extracted from the date of each sale. Then it groups the data by both month and product_id. The GROUP BY statement allows the SUM function to sum up the quantity of each product sold in each month separately. The ORDER BY statement finally sorts the data first by month then by total_quantity in descending order, which puts the best selling product (i.e., the product with the highest total_quantity) of each month at the top.
SQL Question 9: Find Kontoor Brands' customers with a specific pattern in their email
As part of Kontoor Brands, you need to filter out customer records according to a specific pattern in their email addresses. This task helps us to segment customers based on their email domain, and it's critical as email campaigns are an integral part of our marketing. We maintain a customer records database that includes the email addresses of our customers.
Suppose you are required to find the customers whose email addresses end with 'gmail.com'. Write an SQL query to filter these customers from the database.
Sample Data:
| customer_id | first_name | last_name | email_address |
|---|---|---|---|
| 6171 | Julia | Roberts | julia.roberts@gmail.com |
| 7802 | Tom | Cruise | tom.cruise@yahoo.com |
| 5293 | Angelina | Jolie | angelina.jolie@gmail.com |
| 6352 | Brad | Pitt | brad.pitt@hotmail.com |
| 4517 | Meryl | Streep | meryl.streep@gmail.com |
Answer:
This query will return all the records from the table where the ends with 'gmail.com'. It uses the keyword with the wildcard character, which represents zero, one or multiple characters. The use of before 'gmail.com' means that there can be any characters before 'gmail.com' in the email address.
Example Output:
| customer_id | first_name | last_name | email_address |
|---|---|---|---|
| 6171 | Julia | Roberts | julia.roberts@gmail.com |
| 5293 | Angelina | Jolie | angelina.jolie@gmail.com |
| 4517 | Meryl | Streep | meryl.streep@gmail.com |
SQL Question 10: What do stored procedures do?
Stored procedures are a lot like functions in programming. They're used to encapsulate and organize business logic into one unit of code, and they can accept multiple input parameters and return multiple output values.
For example, if you were a Data Analyst at Kontoor Brands working on a HR analytics project, you might create a stored procedure to calculate the average salary for a given department:
To call this stored procedure and find the average salary for the Data Analytics department you'd write the following query:
How To Prepare for the Kontoor Brands 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 above Kontoor Brands SQL interview questions, you should also solve the 200+ SQL Interview Questions on DataLemur which come from companies like Microsoft, Google, Amazon, and tech startups.
Each SQL question has multiple hints, step-by-step solutions and crucially, there's an interactive SQL code editor so you can right online code up your SQL query and have it graded.
To prep for the Kontoor Brands SQL interview you can also be a great idea to solve SQL problems from other apparel companies like:
In case your SQL skills are weak, forget about diving straight into solving questions – improve your SQL foundations with this SQL tutorial for Data Scientists & Analysts.
This tutorial covers SQL topics like filtering data with boolean operators and aggregate functions like SUM()/COUNT()/AVG() – both of these show up frequently during SQL interviews at Kontoor Brands.
Kontoor Brands Data Science Interview Tips
What Do Kontoor Brands Data Science Interviews Cover?
Beyond writing SQL queries, the other topics to practice for the Kontoor Brands Data Science Interview are:
- Probability & Statistics Questions
- Python Pandas or R Coding Questions
- Business Sense and Product-Sense Questions
- Machine Learning Questions
- Behavioral & Resume-Based Questions
How To Prepare for Kontoor Brands Data Science Interviews?
To prepare for Kontoor Brands Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions taken from FAANG (FB, Apple, Amazon, Netflix, Google)
- a refresher on Python, SQL & ML
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