10 Hanes SQL Interview Questions (Updated 2024)
At HanesBrands, SQL is used for sales patterns, and for managing inventory to efficiently supply their global retail stores. That's the reason behind why Hanes often tests SQL questions in interviews for Data Science, Analytics, and & Data Engineering jobs.
As such, to help you practice for the Hanes SQL interview, we've collected 10 HanesBrands SQL interview questions can you solve them?
10 HanesBrands SQL Interview Questions
SQL Question 1: Calculate the average monthly sales per product
Hanes is a large company that sells a variety of clothing items. They would like your help in understanding the sales dynamic of their products on a monthly basis.
Your task is to write a SQL query to calculate the average monthly sales for each product. Specifically, you will need to use window function to calculate the total number of units sold for each product in each month, and then calculate the monthly average.
Example Input:
| sale_id | date | product_id | units_sold |
|---|---|---|---|
| 501 | 2022-01-15 | 1001 | 15 |
| 502 | 2022-01-18 | 1002 | 13 |
| 503 | 2022-01-20 | 1001 | 20 |
| 504 | 2022-02-10 | 1002 | 22 |
| 505 | 2022-02-15 | 1002 | 18 |
| 506 | 2022-02-18 | 1001 | 25 |
Example Output:
| month | product_id | avg_units_sold |
|---|---|---|
| 1 | 1001 | 17.5 |
| 1 | 1002 | 13.0 |
| 2 | 1001 | 25.0 |
| 2 | 1002 | 20.0 |
Answer:
This PostgreSQL query first extracts the month from the date column and uses it alongside the product_id to partition the data. The AVG window function is then applied on the units_sold for each partition to calculate the average monthly sales per product. Results are ordered by month and product_id for easy interpretation. The use of the window function allows us to perform this calculation in a single SQL statement.
Pro Tip: Window functions are a popular SQL interview topic, so practice every window function problem on DataLemur
SQL Question 2: Top 3 Department Salaries
Given a table of Hanes employee salary information, write a SQL query to find the top 3 highest paid employees within each department.
Hanes Example Input:
| employee_id | name | salary | department_id |
|---|---|---|---|
| 1 | Emma Thompson | 3800 | 1 |
| 2 | Daniel Rodriguez | 2230 | 1 |
| 3 | Olivia Smith | 2000 | 1 |
| 4 | Noah Johnson | 6800 | 2 |
| 5 | Sophia Martinez | 1750 | 1 |
| 8 | William Davis | 6800 | 2 |
| 10 | James Anderson | 4000 | 1 |
Example Input:
| department_id | department_name |
|---|---|
| 1 | Data Analytics |
| 2 | Data Science |
Example Output:
| department_name | name | salary |
|---|---|---|
| Data Analytics | James Anderson | 4000 |
| Data Analytics | Emma Thompson | 3800 |
| Data Analytics | Daniel Rodriguez | 2230 |
| Data Science | Noah Johnson | 6800 |
| Data Science | William Davis | 6800 |
Check your SQL query for this interview question interactively on DataLemur:
Answer:
We use the DENSE_RANK() window function to generate unique ranks for each employee's salary within their department, with higher salaries receiving lower ranks. Then, we wrap this up in a CTE and filter the employees with a ranking of 3 or lower.
If the code above is hard to understand, you can find a detailed solution with hints here: Top 3 Department Salaries.
SQL Question 3: What is database denormalization?
Denormalization is the practice of altering a database schema in a way that breaks the normalization rules (1NF, 2NF, 3NF, etc.).
This is typically done to improve the performance of the database when it is being used for online analytics processing (OLAP), rather than online transaction processing (OLTP) use cases.
Denormalization can improve query performance by reducing the number of expensive joins required to retrieve data, but it comes with it's own drawbacks too. By adding redundant columns, you incur more data storage costs, and there's need for more complex update and delete operations in order to maintain data integrity across all the duplicated data. Thus, it's important to carefully consider the trade-offs involved before implementing denormalization.
SQL Question 4: Filter Customers Based on Their Purchasing Behavior
Hanes wants to identify the male customers who are neither from New York nor have purchased any products over $500 in value in the last 2 years. The company also wants to see what are the most popular product categories among these customers.
You are provided with two tables: and .
Example Input:
| customer_id | name | gender | city |
|---|---|---|---|
| 123 | John | M | New York |
| 265 | Peter | M | Los Angeles |
| 362 | James | M | Boston |
| 192 | Mike | M | Chicago |
| 981 | Steve | M | Arl (Texas) |
Example Input:
| purchase_id | customer_id | purchase_date | product_category | product_value |
|---|---|---|---|---|
| 1 | 123 | 2020-08-12 | Underwear | 20.00 |
| 2 | 265 | 2021-06-25 | Socks | 10.00 |
| 3 | 362 | 2021-07-26 | Shirt | 30.00 |
| 4 | 192 | 2021-09-15 | Underwear | 20.00 |
| 5 | 981 | 2021-12-05 | Shirt | 600.00 |
Based on these two tables, construct an SQL query to solve this.
Answer:
This query first joins the customers and purchases tables on . It then filters for male customers not located in New York, who have made purchases within last 2 years and none of their purchases are over $500 in value. The query concludes by returning the , , and , grouped by and , and ordered by the frequency of the product_category, showing the most popular product categories at the top.
SQL Question 5: Can you explain the concept of a constraint in SQL?
Constraints are just rules your DBMS has to follow when updating/inserting/deleting data.
Say you had a table of Hanes products and a table of Hanes customers. Here's some example SQL constraints you'd use:
NOT NULL: This constraint could be used to ensure that certain columns in the product and customer tables, such as the product name and customer email address, cannot contain NULL values.
UNIQUE: This constraint could be used to ensure that the product IDs and customer IDs are unique. This would prevent duplicate entries in the respective tables.
PRIMARY KEY: This constraint could be used to combine the NOT NULL and UNIQUE constraints to create a primary key for each table. The product ID or customer ID could serve as the primary key.
FOREIGN KEY: This constraint could be used to establish relationships between the Hanes product and customer tables. For example, you could use a foreign key to link the customer ID in the customer table to the customer ID in the product table to track which products each customer has purchased.
CHECK: This constraint could be used to ensure that certain data meets specific conditions. For example, you could use a CHECK constraint to ensure that Hanes product prices are always positive numbers.
DEFAULT: This constraint could be used to specify default values for certain columns. For example, you could use a DEFAULT constraint to set the customer registration date to the current date if no value is provided when a new customer is added to the database.
SQL Question 6: Calculate Click-Through-Rates for Hanes' Products
Hanes is a company that sells a variety of clothing items online. They are interested in understanding the click-through rates of their customers with respect to product items viewed and then eventually added to cart. Let's imagine Hanes has two tables: and .
Design a SQL query to calculate the click-through rates (CTR) as the number of times a product was added to cart after being viewed divided by the total number of times the product was viewed. The click-through rate should be calculated for each product. A higher CTR indicates a higher rate of users adding the product to the cart after viewing it.
Example Input:
| view_id | user_id | product_id | view_date |
|---|---|---|---|
| 101 | 123 | 40001 | 06/07/2022 00:00:00 |
| 102 | 124 | 30002 | 06/07/2022 00:00:00 |
| 103 | 125 | 40001 | 06/8/2022 00:00:00 |
| 104 | 126 | 30002 | 06/10/2022 00:00:00 |
| 105 | 127 | 40001 | 06/10/2022 00:00:00 |
Example Input:
| add_id | user_id | product_id | add_date |
|---|---|---|---|
| 201 | 123 | 40001 | 06/07/2022 00:00:00 |
| 202 | 124 | 30002 | 06/07/2022 00:00:00 |
| 203 | 125 | 40001 | 06/8/2022 00:00:00 |
| 204 | 128 | 30002 | 06/11/2022 00:00:00 |
Answer:
This PostgreSQL query joins the table with the table on both and . It then calculates the click-through rate by dividing the distinct counts of (from the table) by the distinct counts of (from the table) for each . The is used to preserve all views even if there were no corresponding adds to the cart, which might not be possible with an . Dividing integers can result in an integer in some SQL dialects, and to preserve precision, it's good to cast the denominator to a float with .
To solve a similar SQL problem on DataLemur's free online SQL coding environment, solve this Facebook SQL Interview question:
SQL Question 7: What does the SQL function do?
The COALESCE() function can be used to replace NULL values with a specified value. For instance, if you are a Data Analyst at a company and are working on a customer analytics project to identify the most engaged customers, you may have access to the following data in the table:
| customer_id | email_engagement | sms_engagement |
|---|---|---|
| 101 | moderately_active | not_opted_in |
| 201 | un-subscribed | NULL |
| 301 | NULL | not_opted_in |
| 401 | not_active | very_active |
| 501 | very_active | very_active |
| 601 | NULL | NULL |
Before you could procede with your analysis, you would need to remove the NULLs, and replace them with the default value for email engagement (not_active), and the default sms_engagement level (not_opted_in).
To do this, you'd run the following query:
This would get you the following output:
| customer_id | email_engagement | sms_engagement |
|---|---|---|
| 101 | moderately_active | not_opted_in |
| 102 | un-subscribed | not_opted_in |
| 103 | not_active | not_opted_in |
| 104 | not_active | very_active |
| 105 | very_active | very_active |
| 106 | not_active | not_opted_in |
SQL Question 8: Average Review of Each Product by Month
As a data analyst for Hanes, you are tasked to track the performance of each product on a monthly basis. Your goal is to find the average review rating of each product for each month. Assume you have a ‘reviews’ table with columns as ‘review_id’, ‘user_id’, ‘submit_date’, ‘product_id’, ‘stars’. Write a SQL query that will return a list of products, the month of the reviews, and the average star rating for that product for each month.
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 |
Answer:
The provided query will extract the month part from the 'submit_date' using DATE_PART function. We use the GROUP BY clause to perform aggregation by the month and product_id. Finally, AVG function is used to get the average reviews. ORDER BY is used to sort the results by month and then by product_id.
Example Output:
| mth | product_id | avg_stars |
|---|---|---|
| 6 | 50001 | 3.50 |
| 6 | 69852 | 4.00 |
| 7 | 69852 | 2.50 |
SQL Question 9: Find Customers from a Specific City
As a data analyst at Hanes, your manager has asked you to identify all customers who live in the city of "Durham". The company hopes to use this information to plan a marketing event in the area. Please provide the SQL query that would allow you to retrieve this information from the "Customers" database.
Example Input:
| customer_id | first_name | last_name | address | city | state | zip_code |
|---|---|---|---|---|---|---|
| 1234 | John | Doe | 123 Maple Street | Durham | North Carolina | 27701 |
| 5678 | Jane | Smith | 456 Oak Street | Raleigh | North Carolina | 27513 |
| 9012 | Alice | Johnson | 789 Pine Street | Durham | North Carolina | 27703 |
| 3456 | Bob | Williams | 321 Elm Street | Chapel Hill | North Carolina | 27517 |
| 7890 | Charlie | Brown | 654 Birch Street | Durham | North Carolina | 27703 |
Answer:
This query uses the operator in the clause to filter the "Customers" table, returning only the records of customers who live in the city of "Durham". The symbol in the statement indicates that all columns from these records should be included in the result.
Also read about Hanes 2024 sustainibility goals!
SQL Question 10: What is normalization?
Normalization involves breaking up your tables into smaller, more specialized ones and using primary and foreign keys to define relationships between them. Not only does this make your database more flexible and scalable, it also makes it easier to maintain. Plus, normalization helps to keep your data accurate by reducing the chance of inconsistencies and errors.
The only downside is now is that your queries will involve more joins, which are slow AF and often a DB performance botteleneck.
How To Prepare for the Hanes SQL Interview
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Hanes SQL interview is to solve as many practice SQL interview questions as you can!
In addition to solving the earlier Hanes SQL interview questions, you should also solve the 200+ SQL coding questions which come from companies like Facebook, Google and unicorn tech startups.
Each DataLemur SQL question has hints to guide you, fully explained answers along with a discussion board to see how others solved it and crucially, there's an online SQL code editor so you can easily right in the browser your SQL query answer and have it checked.
To prep for the Hanes SQL interview you can also be helpful to practice SQL problems from other apparel companies like:
However, if your SQL query skills are weak, forget about diving straight into solving questions – strengthen your SQL foundations with this SQL tutorial for Data Scientists & Analysts.
This tutorial covers SQL concepts such as Subqueries and grouping by multiple columns – both of which pop up frequently during Hanes interviews.
HanesBrands Data Science Interview Tips
What Do Hanes Data Science Interviews Cover?
Besides SQL interview questions, the other question categories covered in the Hanes Data Science Interview include:
- Statistics & AB Testing Questions
- Coding Questions in Python or R
- Product Metrics Interview Questions
- Machine Learning Questions
- Behavioral & Resume-Based Questions
Data Science and Retail are more intertwined than you may think... Read about these 6 retailer use cases involving Data Science.
How To Prepare for Hanes Data Science Interviews?
I'm a bit biased, but I think the optimal way to prepare for Hanes Data Science interviews is to read the book I wrote: Ace the Data Science Interview.
The book covers 201 data interview questions sourced from tech companies like Google & Microsoft. It also has a crash course on SQL, AB Testing & ML. And finally it's vouched for by the data community, which is why it's got over 1000+ 5-star reviews on Amazon.