10 International Flavors & Fragrances SQL Interview Questions (Updated 2024)
At International Flavors & Fragrances, SQL is essential for analyzing fragrance and flavor ingredients data, including chemical compositions and sensory profiles, as well as for managing customer preference datasets for product personalization, such as recommending customized scents. Because of this, International Flavors & Fragrances often asks International Flavors & Fragrances during interviews for Data Analytics, Data Science, and Data Engineering jobs.
Thus, to help you prepare, here’s 10 International Flavors & Fragrances SQL interview questions – how many can you solve?
10 International Flavors & Fragrances SQL Interview Questions
SQL Question 1: Identify Most Valuable Customers
International Flavors & Fragrances is a company that creates unique scents and tastes for customers in various industries. Your task as a data assistant is to provide a SQL query that allows us to identify the top customers – "power users" or "whale" users – who make the most frequent purchases of our products.
You'll be working with two tables - and .
:
| user_id | first_name | last_name | register_date |
|---|---|---|---|
| 1 | John | Doe | 2020-01-01 |
| 2 | Jane | Smith | 2020-02-15 |
| 3 | Mary | Johnson | 2020-03-20 |
| 4 | James | Brown | 2020-04-25 |
| 5 | Patricia | Williams | 2020-05-30 |
:
| purchase_id | user_id | product_id | purchase_date | quantity |
|---|---|---|---|---|
| 1 | 1 | 10001 | 2022-06-01 | 5 |
| 2 | 1 | 10002 | 2022-06-15 | 3 |
| 3 | 2 | 10001 | 2022-06-20 | 2 |
| 4 | 2 | 10003 | 2022-07-10 | 1 |
| 5 | 3 | 10002 | 2022-07-15 | 10 |
| 6 | 4 | 10001 | 2022-07-20 | 4 |
| 7 | 5 | 10003 | 2022-08-05 | 6 |
| 8 | 1 | 10002 | 2022-08-10 | 5 |
Answer:
With the above query, we first join the and table on the column. We then aggregate the data by , and from users table. We also count the number of per (as ) and sum the purchased per (as ). This gives us data on the frequency and volume of purchases per user. Finally we order the data by in descending order and limit the results to the top 10, in order to identify the top 10 customers who purchase the highest quantities of products.
To solve a related super-user data analysis question on DataLemur's free online SQL code editor, try this Microsoft Teams Power User SQL Interview Question:
SQL Question 2: Top Department Salaries
Given a table of International Flavors & Fragrances employee salary data, write a SQL query to find the top three highest paid employees in each department.
International Flavors & Fragrances 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 |
You can solve this problem directly within the browser 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 solution above is confusing, you can find a detailed solution here: Top 3 Department Salaries.
SQL Question 3: What's the difference between a clustered and non-clustered index?
Clustered indexes have a special characteristic in that the order of the rows in the database corresponds to the order of the rows in the index. This is why a table can only have one clustered index, but it can have multiple non-clustered indexes.
The main difference between clustered and non-clustered indexes is that the database tries to maintain the order of the data in the database to match the order of the corresponding keys in the clustered index. This can improve query performance as it provides a linear-access path to the data stored in the database.
SQL Question 4: Calculate the most liked fragrance in each region
International Flavors & Fragrances uses a database table, , to keep track of each sale made. This table includes the fragrance_id of the fragrance purchased, the country where the sale was made, and a customer rating of the fragrance (1-5, with 5 being the best).
Furthermore, there is another table, , with details about each fragrance, including the fragrance_id and the name of the fragrance.
The company would like you to write a SQL query to identify the most liked fragrance, in terms of average customer rating, in each country for the year 2022. If there is a tie for the highest average rating, choose the fragrance with the most reviews.
Example Input:
| sale_id | country | sale_date | fragrance_id | rating |
|---|---|---|---|---|
| 1 | USA | 01/05/2022 | 101 | 5 |
| 2 | USA | 01/07/2022 | 102 | 5 |
| 3 | USA | 01/12/2022 | 101 | 4 |
| 4 | UK | 01/05/2022 | 101 | 4 |
| 5 | UK | 01/10/2022 | 102 | 5 |
| 6 | UK | 01/20/2022 | 102 | 5 |
Example Input:
| fragrance_id | name |
|---|---|
| 101 | Vanilla Blossom |
| 102 | Lavender Dream |
Example Output:
| country | best_fragrance | average_rating |
|---|---|---|
| USA | Vanilla Blossom | 4.5 |
| UK | Lavender Dream | 5.0 |
Answer:
This query first joins the table with the table on the field. Then, it only considers the rows in the table from the year 2022. It calculates the average rating for each fragrance in each country, and orders the result by country and average rating (in descending order), thereby retrieving the most liked fragrance in each country for the specified year.
To practice another window function question on DataLemur's free online SQL coding environment, try this Amazon SQL Interview Question:
SQL Question 5: Could you explain what a self-join is?
A self-join is a type of join in which a table is joined to itself. To perform a self-join, you need to specify the table name twice in the FROM clause, and give each instance of the table a different alias. You can then join the two instances of the table using a JOIN clause, and use a WHERE clause to specify the relationship between the rows.
Self-joins are the go-to technique for any data analysis that involves pairs of the same thing, like identifying pairs of products that are frequently purchased together like in this Walmart SQL interview question.
For another example, say you were doing an HR analytics project and needed to analyze how much all International Flavors & Fragrances employees in the same department interact with each other. Here's a self-join query you could use to retrieve all pairs of International Flavors & Fragrances employees who work in the same department:
This query returns all pairs of International Flavors & Fragrances employees who work in the same department, and excludes pairs where the employee's id is the same (since this would represent the same International Flavors & Fragrances employee being paired with themselves).
SQL Question 6: Find the Average Fragrance Rating
Given a database of customer feedback for the range of International Flavors & Fragrances, you are required to find the average product rating for each fragrance category. The feedback is based on an integer scale rating from 1 to 5, with 1 being the lowest and 5 being the highest.
Here is the structure of the fragrance and feedback tables:
Example Input:
| fragrance_id | fragrance_name | category |
|---|---|---|
| 101 | Vanilla Bliss | Gourmet |
| 102 | Ocean Breeze | Fresh |
| 103 | Lavender Fields | Floral |
| 104 | Citrus Burst | Fruit |
| 105 | Mountain Pine | Woody |
Example Input:
| feedback_id | user_id | fragrance_id | rating |
|---|---|---|---|
| 1 | 123 | 101 | 3 |
| 2 | 265 | 103 | 5 |
| 3 | 362 | 101 | 4 |
| 4 | 192 | 104 | 2 |
| 5 | 981 | 105 | 3 |
Answer:
Using PostgreSQL, you can compute the average fragrance rating for each category with the following SQL statement:
This SQL query joins the and tables on the field. The clause is applied on the field, and the function is used to calculate the average fragrance rating in each category. The keyword is used to rename the average column as .
Therefore, the output of this query would show the average rating for each category in the fragrance table.
To practice a very similar question try this interactive Amazon Average Review Ratings Question which is similar for averaging ratings or this Wayfair Y-on-Y Growth Rate Question which is similar for < grouped calculations.
SQL Question 7: What's the operator do, and can you give an example?
The UNION operator combines the results from multiple SELECT statements into a single result set.
Here's an example of using the UNION operator to combine the results of two SELECT statements that retrieve data from tables of International Flavors & Fragrances's Facebook ads and their Google ads:
This statement would return a combined result set of both Facebook and Google ads that have a spend greater than $200. The result set would include the following columns: ad_id, ad_name, ad_type, impressions, clicks, and spend.
Note that the two SELECT statements within the UNION must have the same number of columns and the columns must have similar data types. In this example, both SELECT statements have the same number of columns and the data types of the columns are all compatible.
SQL Question 8: Determining Click-Through Rates of Digital Fragrances
International Flavors & Fragrances (IFF) has recently launched a digital experience, where users can explore and order fragrances online. As a data analyst, your task is to determine the click-through rates from users viewing the digital fragrance products to adding a product to the cart.
To determine this rate, we will need access to two data sets: , which contains all the product views, and , which contains all the instances where a product is added to a cart.
Example Input:
| view_id | user_id | view_date | fragrance_id |
|---|---|---|---|
| 5371 | 123 | 07/15/2022 00:00:00 | 30001 |
| 2401 | 265 | 07/10/2022 00:00:00 | 71202 |
| 1313 | 362 | 07/18/2022 00:00:00 | 30001 |
| 7890 | 192 | 07/20/2022 00:00:00 | 71202 |
| 6580 | 981 | 07/05/2022 00:00:00 | 71202 |
Example Input:
| cart_id | user_id | add_date | fragrance_id |
|---|---|---|---|
| 8431 | 123 | 07/15/2022 00:00:00 | 30001 |
| 3701 | 265 | 07/10/2022 00:00:00 | 71202 |
| 8283 | 192 | 07/21/2022 00:00:00 | 71202 |
Answer:
Here is the SQL query for obtaining the click-through rates:
This PostgreSQL query will create two tables - one counts the number of views for each fragrance, and the other counts the number of those views that resulted in the fragrance being added to a cart. These two tables are then joined and the click-through rate is computed for each fragrance by dividing the number of successful additions to cart by the number of views.
To practice a similar problem about calculating rates, solve this TikTok SQL Interview Question on DataLemur's online SQL coding environment:
SQL Question 9: Highest Sold Fragrance
In International Flavors & Fragrances, we sell hundreds of fragrances globally. For inventory panagement, we need to know which fragrance had the maximum sales each month. Write a SQL query that gives us the fragrance with the maximum sales each month.
Example Input:
| sale_id | fragrance_id | sale_quantity | sale_date |
|---|---|---|---|
| 101 | 25 | 150 | 05/01/2022 |
| 102 | 36 | 365 | 05/15/2022 |
| 103 | 78 | 90 | 06/02/2022 |
| 104 | 25 | 250 | 06/10/2022 |
| 105 | 36 | 170 | 06/12/2022 |
| 106 | 78 | 300 | 06/15/2022 |
| 107 | 25 | 80 | 07/01/2022 |
| 108 | 36 | 200 | 07/10/2022 |
| 109 | 78 | 400 | 07/20/2022 |
Example Output:
| month | fragrance_id | total_sales |
|---|---|---|
| 5 | 36 | 365 |
| 6 | 78 | 390 |
| 7 | 78 | 400 |
Answer:
The SQL query above first extracts the month from the sale_date by using the DATE_PART function. Then it groups the sales records by month and fragrance_id. It then selects the maximum sale_quantity for each group which represents the highest number of sales for each fragrance per month. Finally, it orders the result by month.
SQL Question 10: How do the and window functions differ from each other?
While both and are used to rank rows, the key difference is in how they deal with ties.
RANK(): When there's a tie, leaves a gap in the ranking. For example, if three rows are tied for 2nd place, the RANK() function will assign a rank of 2 to the first of these rows, a rank of 3 to the 2nd row in the tie, and a rank of 4 to the the 3rd tie.
DENSE_RANK(): For ties, does not leave a gap in the ranking. Instead, it assigns the same rank to all tied rows, and then makes the next row 1 bigger. Confusing, I know, but here's an example to make it more clear: if three rows are tied for 3rd place, the function will assign a rank of 3 to all three rows, and then assign a rank of 4 to the next row.
Suppose we had data on how many deals different salespeople at International Flavors & Fragrances:
To rank these salespeople, we could execute the following query:
The result of this query would be:
| name | deals_closed | rank | dense_rank |
|---|---|---|---|
| Akash | 50 | 1 | 1 |
| Brittany | 50 | 2 | 1 |
| Carlos | 40 | 3 | 2 |
| Dave | 40 | 4 | 3 |
| Eve | 30 | 5 | 3 |
| Farhad | 10 | 6 | 4 |
As you can see, the function assigns a rank of 1 to the first row (Akash), a rank of 2 to the second row (Brittany), and so on. In contrast, the , gives both Akash and Brittany a rank of 1, since they are tied, then moves on to rank 2 for Carlos.
How To Prepare for the International Flavors & Fragrances 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. In addition to solving the above International Flavors & Fragrances SQL interview questions, you should also solve the 200+ DataLemur interview questions which come from companies like FAANG (FB, Apple, Amazon, Netflix, Google).
Each SQL question has hints to guide you, fully explained answers along with a discussion board to see how others solved it and most importantly, there's an interactive coding environment so you can easily right in the browser your SQL query answer and have it graded.
To prep for the International Flavors & Fragrances SQL interview it is also helpful to solve SQL problems from other chemical companies like:
Dive into the world of flavors and fragrances with IFF's latest press releases!
However, if your SQL skills are weak, don't worry about jumping right into solving questions – refresh your SQL knowledge with this DataLemur SQL tutorial.
This tutorial covers topics including sorting results with ORDER BY and filtering with LIKE – both of these show up frequently in SQL job interviews at International Flavors & Fragrances.
International Flavors & Fragrances Data Science Interview Tips
What Do International Flavors & Fragrances Data Science Interviews Cover?
Besides SQL interview questions, the other question categories tested in the International Flavors & Fragrances Data Science Interview are:
- Statistics & AB Testing Questions
- Coding Questions in Python or R
- Product & Business Analytics Questions
- ML Modelling Questions
- Behavioral Based Interview Questions
How To Prepare for International Flavors & Fragrances Data Science Interviews?
To prepare for the International Flavors & Fragrances Data Science interview make sure you have a strong understanding of the company's culture and values – this will be clutch for acing the behavioral interview. For technical interviews get ready by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from FAANG tech companies
- A Refresher on SQL, Product-Sense & ML
- Amazing Reviews (1000+ 5-star reviews on Amazon)