Data Scientists, Analysts, and Data Engineers at Mondelez use SQL to analyze sales data, helping them identify which products are gaining popularity in the market. It is also essential for managing supply chain databases to ensure smooth product distribution and minimize delay, this is the reason why Mondelez includes SQL questions during interviews.
So, to help you ace the Mondelez SQL interview, here’s 8 Mondelez SQL interview questions in this blog.
Mondelez is a global company specializing in confectionery, food, and beverages. One of their most important business metrics is the sales volume achieved by frequent customers who buy in large volume. In this task, we want to identify these “whale customers”. Write a SQL query to get the top 5 users who bought the most from Mondelez in the year 2021. We will base "most" not just on the number of purchases, but the total amount spent on the transactions.
Below is the sample data:
transaction_id | user_id | transaction_date | product_id | transaction_amount |
---|---|---|---|---|
101 | 123 | 01/05/2021 | 6001 | 120 |
202 | 265 | 02/15/2021 | 6502 | 200 |
303 | 123 | 06/16/2021 | 6001 | 180 |
404 | 891 | 07/22/2021 | 6602 | 300 |
505 | 123 | 09/19/2021 | 6001 | 150 |
user_id | user_name |
---|---|
123 | John Doe |
265 | Emma Smith |
891 | Henry Ford |
This PostgreSQL query joins the 'users' table with the table on the column. It then filters for transactions made in the year 2021. The spent by each user is calculated using the SQL aggregate function SUM. The clause enables us to calculate this quantity per user. We then order the results by in descending order and limit the output to the top 5 users. This gives us a list of the top 5 "whale customers" at Mondelez in 2021.
To practice a related customer analytics question on DataLemur's free interactive SQL code editor, try this recently asked Microsoft SQL interview question:
Given a table of Mondelez employee salary data, write a SQL query to find the top 3 highest earning employees in each department.
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 |
department_id | department_name |
---|---|
1 | Data Analytics |
2 | Data Science |
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 |
Solve this question interactively on DataLemur:
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 tough, you can find a detailed solution here: Top 3 Department Salaries.
An inner join only includes rows from both tables that satisfy the join condition, whereas a full outer join includes all rows from both tables, regardless of whether they match the join condition or not.
For a concrete example, imagine you had two database tables, an advertising campaigns table which had information on Google Ads keywords and how much was bid for each keyword, and a Mondelez sales table, which has data on how many products were sold and which Google Ads keyword drove that sale.
: retrieves rows from both tables where there is a match in the shared key or keys. For example, an between the table and the table could be performed using the keyword column as the shared key. This would retrieve only the rows where the keyword in the table matches the keyword in the table.
: retrieves all rows from both tables, regardless of whether there is a match in the shared key or keys. If there is no match, values will be returned for the columns of the non-matching table.
Suppose you're working for Mondelez International and you've been given two datasets. One is a catalog table that contains all the Mondelez products with their names and unique product ID, and the other is a sales table that logs each sale made, including the product ID, quantity sold, and the date of the sale.
The head of sales wants to know the top 3 best-selling products for each month over the past year. So, your task is to write a SQL query which calculates the total quantity sold per product per month and ranks the products within each month based on their total quantity sold.
product_id | product_name |
---|---|
1 | "Oreo" |
2 | "Toblerone" |
3 | "Cadbury Dairy Milk" |
4 | "Milka" |
sales_id | product_id | date | quantity |
---|---|---|---|
101 | 1 | 2022-05-05 | 100 |
102 | 2 | 2022-05-06 | 500 |
103 | 1 | 2022-05-07 | 200 |
104 | 3 | 2022-05-08 | 400 |
105 | 2 | 2022-06-05 | 150 |
106 | 4 | 2022-06-06 | 250 |
107 | 3 | 2022-06-07 | 500 |
108 | 2 | 2022-07-05 | 300 |
Month | Product | Total Quantity Sold | Rank |
---|---|---|---|
5 | "Toblerone" | 500 | 1 |
5 | "Cadbury Dairy Milk" | 400 | 2 |
5 | "Oreo" | 300 | 3 |
6 | "Cadbury Dairy Milk" | 500 | 1 |
6 | "Milka" | 250 | 2 |
6 | "Toblerone" | 150 | 3 |
7 | "Toblerone" | 300 | 1 |
The query starts by creating a common table expression (CTE) named . This CTE uses the function to obtain the month of each sale, joins the sales and products tables on and computes their monthly total quantity sold.
The main query then uses this CTE to calculate the rank of each product within each month using the window function ordered by total quantity sold descending. Therefore, the product with the highest quantity sold gets a rank of 1. The clause keeps only those results in the top 3.
To practice a related window function SQL problem on DataLemur's free interactive coding environment, try this Amazon SQL Interview Question:
Denormalization is the practice of altering a database schema in a way that breaks the normalization rules (1st, 2nd, 3rd normal forms).
Denormalization is 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.
For these OLAP use cases, you're bottleneck frequently is joining multiple tables, but de-normalizing your database cuts these outs.
Denormalization can improve query performance by reducing the number of expensive joins required to retrieve data, but it comes with its 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.
Given the database of Mondelēz, a multinational confectionery, food, holding and beverage conglomerate, as a Data Analyst, you need to filter the orders based on the , , and the . Specifically, write a SQL query to find the total number of orders for 'Oreo' and 'Cadbury' placed in the month of October 2022 from the United States.
order_id | customer_id | product_name | order_date | country |
---|---|---|---|---|
1671 | 235 | Oreo | 10/15/2022 | United States |
2712 | 486 | Cadbury | 10/30/2022 | United States |
1321 | 745 | Milka | 10/25/2022 | United Kingdom |
5263 | 376 | Oreo | 10/12/2022 | United States |
9282 | 974 | Cadbury | 10/08/2022 | Canada |
product_name | total_orders |
---|---|
Oreo | 2 |
Cadbury | 1 |
This query filters the table for orders placed in October 2022 and specifically from the United States. It further filters the records for the products 'Oreo' and 'Cadbury'. Finally, it counts the number of orders for each of these products and return the count as .
A cross-join, also known as a cartesian join, is a type of JOIN operation in SQL that creates a new table by combining each row from the first table with every row from the second table. This results in a table with a row for every possible combination of rows from the two input tables.
An example of when this might be useful is if you wanted to first make a dataset containing all possible pairs of customers and products data, in order to later build a Machine Learning model to predict the probability of a customer purchasing a particular product.
However, it is important to note that cross-joins can create very large tables, especially if the input tables have a large number of rows. For example, if you had 10,000 customers and 5,000 different product SKUs, the resulting cross-join would have 50 million rows.
Mondelez is a multinational company specialized in confectionery, food, and beverages. They have a table that stores their customers' data. For this exercise, you are asked to find all customers who live in a city whose name starts with 'San'.
Consider the following table:
customer_id | name | city | registration_date |
---|---|---|---|
101 | Joe Smith | San Francisco | 2020-04-05 |
102 | Emily Davis | Santa Monica | 2019-08-15 |
103 | Michael Johnson | Denver | 2021-05-11 |
104 | Sarah Brown | San Diego | 2022-01-21 |
105 | David Williams | Boston | 2021-11-03 |
The query should return all records for customers who live in a city that starts with 'San'.
The resulting output should look like this:
customer_id | name | city | registration_date |
---|---|---|---|
101 | Joe Smith | San Francisco | 2020-04-05 |
104 | Sarah Brown | San Diego | 2022-01-21 |
You can achieve this using the keyword in SQL. Here's how:
This SQL query selects and returns all records from the table where the city name starts with 'San'. The '%' character in the clause is a wildcard that matches any sequence of characters. As it is placed after 'San', it will match any city name that begins with 'San'.
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Mondelez SQL interview is to solve as many practice SQL interview questions as you can! Beyond just solving the earlier Mondelez SQL interview questions, you should also solve the 200+ SQL questions on DataLemur which come from companies like Microsoft, Google, and Meta.
Each interview question has multiple hints, fully explained answers along with a discussion board to see how others solved it and most importantly, there's an online SQL coding environment so you can right online code up your SQL query answer and have it executed.
To prep for the Mondelez SQL interview it is also wise to solve interview questions from other food and facilities companies like:
However, if your SQL skills are weak, don't worry about diving straight into solving questions – go learn SQL with this SQL interview tutorial.
This tutorial covers SQL topics like UNION vs. joins and turning a subquery into a CTE – both of which show up routinely during SQL job interviews at Mondelez.
Beyond writing SQL queries, the other types of problems tested in the Mondelez Data Science Interview include:
I believe the optimal way to prepare for Mondelez Data Science interviews is to read the book I wrote: Ace the Data Science Interview.
It covers 201 interview questions sourced from Facebook, Google, & Amazon. The book's also got a refresher covering Stats, SQL & ML. And finally it's vouched for by the data community, which is how it's gotten to be an Amazon best-seller with over 1000+ 5-star reviews.
While the book is more technical, it's also important to prepare for the Mondelez behavioral interview. Start by understanding the company's values and mission.