10 G-III Apparel Group SQL Interview Questions (Updated 2024)
Data Analytics, Data Science, and Data Engineering employees at G-III Apparel Group code up SQL queries as a big part of their job. They use SQL for analyzing sales data and managing inventory databases for supply chain operations. That's why G-III Apparel Group asks interviewees SQL interview questions.
So, to help you prepare, here’s 10 G-III Apparel Group SQL interview questions – able to solve them?
10 G-III Apparel Group SQL Interview Questions
SQL Question 1: Average Monthly Sales of Each Brand
As a company dealing in apparel, G-III Apparel Group has multiple brands under its portfolio. Each brand has a collection of unique items. In order to make informed business decisions, executives at G-III Apparel Group need to examine the monthly sales of each brand.
Write a SQL query that calculates the average sales, in terms of quantity, for each brand on a monthly basis. The given month should be in format.
Assuming there is a table with the following schema:
Example Input:
| sale_id | sale_date | brand | item | quantity |
|---|---|---|---|---|
| 001 | 2022-03-01 | BrandA | ItemA1 | 4 |
| 002 | 2022-03-03 | BrandA | ItemA2 | 2 |
| 003 | 2022-03-10 | BrandB | ItemB1 | 5 |
| 004 | 2022-04-02 | BrandA | ItemA2 | 3 |
| 005 | 2022-04-11 | BrandB | ItemB3 | 6 |
Your output should have the month, brand, and average quantity. So previous example data should return:
Example Output:
| mth | brand | avg_quantity |
|---|---|---|
| 2022-03 | BrandA | 3.00 |
| 2022-03 | BrandB | 5.00 |
| 2022-04 | BrandA | 3.00 |
| 2022-04 | BrandB | 6.00 |
Answer:
This PostgreSQL query extracts the year and month from the then groups the data by this string and . It calculates the average (in terms of quantity sold) of each group. The function returns the average value of a numeric column. The function is used to format the date to 'YYYY-MM' format.
For more window function practice, try this Uber SQL Interview Question on DataLemur's online SQL coding environment:
SQL Question 2: Highly-Paid Employees
Assume you had a table of G-III Apparel Group employee salary data. Write a SQL query to find all employees who earn more than their direct manager.
G-III Apparel Group Example Input:
| employee_id | name | salary | department_id | manager_id |
|---|---|---|---|---|
| 1 | Emma Thompson | 3800 | 1 | |
| 2 | Daniel Rodriguez | 2230 | 1 | 10 |
| 3 | Olivia Smith | 8000 | 1 | 8 |
| 4 | Noah Johnson | 6800 | 2 | 8 |
| 5 | Sophia Martinez | 1750 | 1 | 10 |
| 8 | William Davis | 7000 | 2 | NULL |
| 10 | James Anderson | 4000 | 1 | NULL |
Example Output:
| employee_id | employee_name |
|---|---|
| 3 | Olivia Smith |
This is the output because Olivia Smith earns $8,000, surpassing her manager, William Davis who earns 7,800.
Solve this interview question and run your code right in DataLemur's online SQL environment:
Answer:
First, we perform a SELF-JOIN where we treat the first table () as the managers' table and the second table () as the employees' table. Then we use a clause to filter the results, ensuring we only get employees whose salaries are higher than their manager's salary.
If the solution above is hard to understand, you can find a detailed solution here: Well Paid Employees.
Check out all the brands under G-III, you might be surprised who is under their umbrella.
SQL Question 3: What's a database view?
Database views are created to provide customized, read-only versions of your data that you can query just like a regular table. So why even use one if they're so similar to a regular table?
Views are advantageous for several reasons:
- views allow you to create a simpler versions of your data for specific users (such as hiding extraneous columns/rows from business analysts since they're relics of the Data Engineering pipelines setup)
- views help you comply with data security requirements by hiding sensitive data from certain users (important for regulated industries like govermnet and healthcare!)
- views can improve performance for complicated queries by pre-computing the results and caching them in a view (which is often faster than re-executing the original query)
SQL Question 4: Calculate Total Sales for Each Product
G-III Apparel Group specializes in designing, manufacturing, and marketing women's apparel. As a database designer at the company, you need to develop a database that keeps track of all the products and sales. You have two tables, and . The table has a column, , that uniquely identifies each product and a column. The table has a column, a column that refers to the product sold, and a column that defines how many units of the product were sold in each sale. The company wants to know the total quantity of each product sold. Design a SQL query that can calculate this.
Example Input:
| product_id | product_name |
|---|---|
| 1 | Dress |
| 2 | Skirt |
| 3 | T-Shirt |
Example Input:
| sales_id | product_id | quantity |
|---|---|---|
| 1001 | 1 | 13 |
| 1002 | 1 | 7 |
| 1003 | 2 | 8 |
| 1004 | 2 | 2 |
| 1005 | 3 | 1 |
Your SQL query should return the following table:
Example Output:
| product_name | total_quantity |
|---|---|
| Dress | 20 |
| Skirt | 10 |
| T-Shirt | 1 |
Answer:
To get total quantity for each product, we need to join and tables on and then group the result by . We can then sum the for each group to obtain the total quantity sold for each product. The PostgreSQL query can be written as follows:
This query first joins and on the column. It then groups the result by and calculates the total for each product. The result is a list of products along with the total quantity of each product sold.
SQL Question 5: What's the difference between a unique and non-unique index?
Some similarities between unique and non-unique indexes include:
- Both indexes improve the performance of SQL queries by providing a faster way to lookup the desired data.
- Both indexes use an additional data which requires more storage space which impacts write performance.
- Both indexes can be created on one or more columns of a table
Some differences between unique and non-unique indexes include:
- A unique index enforces the uniqueness of the indexed columns, meaning that no duplicate values are allowed in the indexed columns. A non-unique index allows duplicate values in the indexed columns.
- A unique index can be used to enforce the primary key of a table, but a non-unique index cannot.
- A unique index can have a maximum of one NULL value in the indexed columns, but a non-unique index can have multiple NULLs
SQL Question 6: Filter Customer Records based on Purchasing Patterns
Given the table that log purchase details of customers, write a SQL query to filter out all the customers whose total spending on 'Leather Jackets' is above $5000 and who have made purchases in 3 or more unique months during the year 2021.
Example Input:
| order_id | customer_id | product_name | purchase_date | amount |
|---|---|---|---|---|
| 101 | 500 | Leather Jacket | 2021-01-12 | 650 |
| 202 | 600 | Leather Jacket | 2021-02-15 | 700 |
| 204 | 600 | Leather Jacket | 2021-05-19 | 700 |
| 301 | 700 | Leather Jacket | 2021-04-20 | 900 |
| 402 | 500 | Leather Jacket | 2021-02-11 | 750 |
| 502 | 600 | Leather Jacket | 2021-09-19 | 700 |
| 601 | 500 | Leather Jacket | 2021-05-21 | 750 |
Example Output:
| customer_id | total_spent_on_leather_jackets | unique_months_of_purchase |
|---|---|---|
| 500 | 2150 | 3 |
| 600 | 2100 | 3 |
Answer:
In this query, we first filter out the records for the product 'Leather Jacket' purchased in the year 2021. We then group these by , calculate the total amount spent on 'Leather Jacket' and the total unique months of purchases. The clause is used to further filter out customers who have spent more than $5000 and have made purchases in 3 or more unique months in the year 2021.
SQL Question 7: What is a database index, and what are the different types of indexes?
A database index is a data structure that provides a quick lookup of data in a column or columns of a table.
There are several types of indexes that can be used in a database:
- Primary index: a unique identifier is used to access the row directly.
- Unique index: used to enforce the uniqueness of the indexed columns in a table.
- Composite index: created on multiple columns of a table, is used to speed up the search process for multiple columns
- Clustered index: determines the physical order of the data in a table
SQL Question 8: Click-Through Rate for Products in G-III Apparel Group
Assume that G-III Apparel Group conducts an intense online marketing campaign through digital ads. The company has shared some data about the advertisements displayed and the user interaction for analysis.
Each row in the table represents a single display of a specific product Ad to a user. If the column indicates , that means the user clicked on the ad and was directed to the product viewing page. The column indicates if the user has added the product to the cart after viewing.
The task is to write a SQL query to calculate the Click-through rate, i.e., the percentage of ad displays that resulted in clicks, and Click-through conversion rate, i.e., the percentage of ad clicks that resulted in adding the product to the cart.
example input
| ad_id | display_date | product_id | click | add_to_cart |
|---|---|---|---|---|
| 1 | 06/08/2022 00:00:00 | A100 | True | True |
| 2 | 06/10/2022 00:00:00 | A200 | False | False |
| 3 | 06/18/2022 00:00:00 | A100 | True | False |
| 4 | 07/26/2022 00:00:00 | A300 | True | True |
| 5 | 07/05/2022 00:00:00 | A200 | True | False |
| 6 | 07/29/2022 00:00:00 | A300 | False | False |
Answer:
Here is an SQL query that can solve this problem:
This query calculates Click-through rate and Click-through conversion rate for each product. It uses SUM aggregate function with CASE statement in the calculation. The CASE statement is used to count the number of clicks and conversions. Then these counts are divided by the total number of impressions (rows in the table) or number of clicks and multiplied by 100 to convert it into a percentage. The ROUND function is used to keep only two decimal places.
Example Output:
| product_id | click_through_rate | conversion_rate |
|---|---|---|
| A100 | 100.00 | 50.00 |
| A200 | 50.00 | 0.00 |
| A300 | 50.00 | 100.00 |
The result table shows the Click-through rate and Click-through conversion rate for each product. For example, Product 'A100' was clicked every time it was displayed (100% CTR), but added to the cart only half of the times it was clicked (50% conversion rate).
To practice a similar problem on DataLemur's free interactive coding environment, attempt this Facebook SQL Interview question:
SQL Question 9: Calculate the average production cost by year and product
Given a table with production details for the apparel group, calculate the average production cost per unit by year for each product.
Example Input:
| product_id | production_date | production_cost | units_produced |
|---|---|---|---|
| 901 | 2018-01-15 | 50000 | 10000 |
| 902 | 2018-03-20 | 80000 | 20000 |
| 903 | 2018-05-25 | 30000 | 8000 |
| 901 | 2019-02-11 | 65000 | 12000 |
| 902 | 2019-04-17 | 95000 | 21000 |
| 903 | 2019-06-22 | 35000 | 8500 |
Answer:
This SQL query groups the records by year and product id. Then it calculates the average cost per unit for each year-product combination by dividing the production cost by the number of units produced. Finally, it orders the results by year and product id.
SQL Question 10: What is a primary key?
The primary key of a table is a column or set of columns that serves as a unique identifier for each row. It ensures that all rows are distinct and does not allow null values.
For example, say you had stored some Facebook ad campaign data that G-III Apparel Group ran:
The column uniquely identifies each row in the table, and the PRIMARY KEY constraint ensures that no two rows have the same . This helps to maintain the integrity of the data in the table by preventing duplicate rows.
The primary key is also an important part of the table because it allows you to easily identify and reference specific campaigns in your Facebook Ad data. You can use it to join to other tables in the database, such as a table containing data on the results of the campaigns.
Preparing For The G-III Apparel Group SQL Interview
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the G-III Apparel Group SQL interview is to solve as many practice SQL interview questions as you can!
Besides solving the earlier G-III Apparel Group SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like Amazon, Microsoft, Meta, and smaller tech companies.
Each exercise has multiple hints, full answers and crucially, there is an online SQL code editor so you can right in the browser run your SQL query answer and have it checked.
To prep for the G-III Apparel Group SQL interview you can also be wise to solve interview questions from other apparel companies like:
But if your SQL skills are weak, don't worry about diving straight into solving questions – improve your SQL foundations with this free SQL tutorial.
This tutorial covers things like filtering data with WHERE and GROUP BY – both of which show up often in G-III Apparel Group SQL assessments.
G-III Apparel Group Data Science Interview Tips
What Do G-III Apparel Group Data Science Interviews Cover?
Besides SQL interview questions, the other question categories to prepare for the G-III Apparel Group Data Science Interview include:
- Experimental Design & AB Testing Questions
- Python Pandas or R Coding Questions
- Business Sense and Product-Sense Questions
- Machine Learning Questions
- Behavioral & Resume-Based Questions
How To Prepare for G-III Apparel Group Data Science Interviews?
The best way to prepare for G-III Apparel Group Data Science interviews is by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from FAANG (FB, Apple, Amazon, Netflix, Google)
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