9 Coty SQL Interview Questions (Updated 2024)
Coty employees write SQL queries to analyze customer behavior patterns by examining purchase history, product preferences, and shopping frequency, as well as optimize supply chain operations by identifying bottlenecks, tracking inventory levels, and streamlining logistics for efficient inventory management. For this reason, Coty often usesSQL questions during interviews for Data Science, Analytics, and & Data Engineering jobs.
So, to help you prep, we've curated 9 Coty SQL interview questions – how many can you solve?
9 Coty SQL Interview Questions
SQL Question 1: Calculate Monthly Average Rating for Each Product
Given a table which records each product review from customers at Coty, write a SQL query to calculate the monthly average star rating for each product.
Assume the table has the following schema:
| Column Name | Type | Description |
|---|---|---|
| review_id | Integer | Unique ID of the review |
| user_id | Integer | Unique ID of the customer |
| submit_date | Datetime | Date when the review was submitted |
| product_id | Integer | Unique ID of the product |
| stars | Integer | Rating given by the customer between 1 to 5 |
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 |
Example Output:
| month | product | avg_stars |
|---|---|---|
| 6 | 50001 | 3.50 |
| 6 | 69852 | 4.00 |
| 7 | 69852 | 2.50 |
Answer:
The following PostgreSQL query makes use of the and functions to achieve the task:
This query first groups the data by month and product. It then calculates the average star rating for each group and finalizes the results in ascending order of month and product.
To solve another window function question on DataLemur's free interactive SQL code editor, solve this Google SQL Interview Question:
SQL Question 2: Employees Earning More Than Their Boss
Given a table of Coty employee salaries, write a SQL query to find employees who earn more money than their own boss.
Coty 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.
You can solve this interview question directly within the browser on DataLemur:
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 code above is confusing, you can find a detailed solution with hints here: Employee Salaries Higher Than Their Manager.
SQL Question 3: Why would you use the SQL constraint?
The constraint is used to specify a condition that the data in a column must meet. If a row is inserted or updated and the data in the column doesn't meet the condition specified by the CHECK constraint, the operation will sadly fail.
For example, you might use a constraint to ensure that a column contains only positive numbers, or that a date is within a certain range.
For example, if you had a table of Coty employees, here's an example of how to use the CHECK constraint in a CREATE TABLE statement:
SQL Question 4: Filter Customer Records
You are given a table containing a list of all the customers of Coty, a beauty products company. The table contains their ID, name, age, city, annual spend, and whether they're a part of the loyalty program.
Your task is to write a SQL query that filters the customers satisfying the following conditions:
- They are located in the cities 'London' or 'Paris'
- They are part of the loyalty program
- Their annual spend is more than 5000
You should return the list of customers who satisfy all the conditions. Order the result in ascending order of customer IDs.
Example Input:
| customer_id | name | age | city | annual_spend | is_loyal |
|---|---|---|---|---|---|
| 1345 | John Doe | 32 | London | 6000 | true |
| 1234 | Jane Smith | 29 | Paris | 8000 | false |
| 2245 | Mark Johnson | 45 | Berlin | 7000 | true |
| 3451 | Emily Clark | 37 | Paris | 9000 | true |
| 4312 | Fred White | 41 | London | 4500 | true |
Example Output:
| customer_id | name | age | city | annual_spend | is_loyal |
|---|---|---|---|---|---|
| 1345 | John Doe | 32 | London | 6000 | true |
| 3451 | Emily Clark | 37 | Paris | 9000 | true |
Answer:
This query filters out the customer records where their city is either 'London' or 'Paris', their annual spend is more than 5000, and they are a part of the loyalty program. It then returns the list of such customers ordered by their customer ID in ascending order.
SQL Question 5: What are the three different normal forms?
There are several normal forms that define the rules for normalizing a database:
A database is in first normal form (1NF) if it meets the following criteria:
- Each column in a table contains a single value (no lists or containers of data)
- Each column should contain the same type of data (no mixing strings vs. integers)
- Each row in the table is unique
A database is in second normal form (2NF) if it meets the following criteria:
- It is in first normal form.
- All non-key attributes in a table are fully dependent on the primary key.
Said another way, to achieve 2NF, besides following all the rules from 1NF all the columns in a given table should be dependent only on that table's primary key.
A database is in third normal form (3NF) if it meets the following criteria:
- It is in second normal form.
- There are no transitive dependencies in the table.
A transitive dependency means that a piece of data in one column is derived from another column. For example, it wouldn't make sense to keep a column called "user's age" and "user's birthdate" (because age can be derived from birthdate.
While there's also a 4th and 5th normal form, it's too pedantic and hence out-of-scope to know for the Coty SQL interview.
SQL Question 6: Average Sales per Product
Coty is a big cosmetics company. One of their main concerns would be to track the sales happening per product. Can you find the average number of units of products sold on a daily basis by Coty?
Example Input:
|sale_id|product_id|sale_date|units_sold|
|:----|:----|:----|:----| |1001|654|01/01/2021|500| |1002|123|01/01/2021|150| |1003|654|01/02/2021|350| |1004|123|01/02/2021|200| |1005|987|01/02/2021|300|
Example Output:
| date | product | avg_units_sold |
|---|---|---|
| 01/01/2021 | 654 | 500.00 |
| 01/01/2021 | 123 | 150.00 |
| 01/02/2021 | 654 | 350.00 |
| 01/02/2021 | 123 | 200.00 |
| 01/02/2021 | 987 | 300.00 |
Answer:
This query calculates the average number of units sold per day for each product. The GROUP BY clause is used to segment data into groups of sale_date and product_id, over which the AVG() function is applied.
To practice a very similar question try this interactive Wayfair Y-on-Y Growth Rate Question which is similar for calculating sales data or this Amazon Highest-Grossing Items Question which is similar for working with product data.
SQL Question 7: What's a cross-join, and why are they used?
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 8: Average Sales by Product per Month
Given the sales data for Coty, a multinational beauty company that manufactures fragrances, cosmetics, skincare and hair color, we want to know which product had the highest average sales each month.
Sales data will be represented in SQL as the table, and product data will be represented in SQL as the table.
Example Input:
| sale_id | date | product_id | quantity_sold |
|---|---|---|---|
| 93871 | 2022-05-01 | 897 | 50 |
| 78452 | 2022-05-01 | 535 | 33 |
| 92716 | 2022-06-02 | 897 | 45 |
| 86723 | 2022-06-06 | 535 | 66 |
| 92837 | 2022-07-01 | 897 | 20 |
Example Input:
| product_id | product_name |
|---|---|
| 897 | Coty Fragrance |
| 535 | Coty Lipstick |
We would like you to write a SQL query that will find the product with the highest average sales per month.
Example Output:
| month | product_name | average_sales |
|---|---|---|
| 5 | Coty Fragrance | 50.0 |
| 6 | Coty Lipstick | 66.0 |
| 7 | Coty Fragrance | 20.0 |
Answer:
This query first joins the and tables on the field so we can get the product name. Then it applies the clause on the month part of the sales date and the product name. The function is used to calculate the average sales for each product every month. The result is sorted first by month and then by average sales in descending order.
SQL Question 9: Analyzing Customer Purchases and Reviews
Given two tables, and , the task is to perform a query which will allow us to obtain the average product ratings given by customers of a certain city. The table has information about the user_id and their city, while the has information about the reviews given by each customer to specific products.
The table is structured as follows:
Example Input:
| user_id | first_name | last_name | city |
|---|---|---|---|
| 123 | John | Doe | New York |
| 265 | Jane | Smith | Los Angeles |
| 362 | Mike | Johnson | New York |
| 192 | Emma | Williams | Dallas |
| 981 | Olivia | Brown | Dallas |
The table is structured as follows:
Example Input:
| review_id | user_id | product_id | stars |
|---|---|---|---|
| 6171 | 123 | 50001 | 4 |
| 7802 | 265 | 69852 | 4 |
| 5293 | 362 | 50001 | 3 |
| 6352 | 192 | 69852 | 3 |
| 4517 | 981 | 69852 | 2 |
The query should provide the following output for the above input, assuming we're looking for average ratings for customers from "New York".
Example Output:
| city | product_id | avg_stars |
|---|---|---|
| New York | 50001 | 3.5 |
Answer:
Here's a PostgreSQL query that will find the average product ratings given by customers from a specific city ("New York" in this case):
This PostgreSQL query joins the and tables based on , and then it groups the result by and to calculate the average star rating (). The clause is used to filter results for the city of interest, "New York".
Because join questions come up so often during SQL interviews, try an interactive Spotify JOIN SQL question:
Coty SQL Interview Tips
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Coty SQL interview is to solve as many practice SQL interview questions as you can! Beyond just solving the earlier Coty SQL interview questions, you should also solve the 200+ SQL coding questions which come from companies like FAANG (FB, Apple, Amazon, Netflix, Google).
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 instantly run your SQL query answer and have it checked.
To prep for the Coty SQL interview it is also helpful to solve SQL questions from other consumer good companies like:
But if your SQL coding skills are weak, forget about diving straight into solving questions – strengthen your SQL foundations with this SQL interview tutorial.
This tutorial covers things like using ORDER BY and CASE/WHEN statements – both of which pop up often during Coty SQL interviews.
Coty Data Science Interview Tips
What Do Coty Data Science Interviews Cover?
In addition to SQL interview questions, the other types of questions tested in the Coty Data Science Interview are:
- Statistics & AB Testing Questions
- Python or R Programming Questions
- Product-Sense Questions
- ML Modelling Questions
- Behavioral Interview Questions focussed on Coty cultural values
Dive into Coty's latest updates and discover how they're shaping the future of beauty and cosmetics!
How To Prepare for Coty Data Science Interviews?
To prepare for Coty Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions taken from tech companies like Netflix, Google, & Airbnb
- a crash course on Python, SQL & ML
- over 1000+ reviews on Amazon & 4.5-star rating
Don't ignore the behavioral interview – prep for that with this guide on acing behavioral interviews.