8 Mondelez SQL Interview Questions (Updated 2024)

Updated on

October 31, 2024

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 SQL Interview Questions

8 Mondelez SQL Interview Questions

SQL Question 1: Identify Top Buying Customers at Mondelez

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:

Example Input:

transaction_iduser_idtransaction_dateproduct_idtransaction_amount
10112301/05/20216001120
20226502/15/20216502200
30312306/16/20216001180
40489107/22/20216602300
50512309/19/20216001150

Example Input:

user_iduser_name
123John Doe
265Emma Smith
891Henry Ford

Answer:


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:

Microsoft SQL Interview Question: Teams Super User

SQL Question 2: Top 3 Salaries

Given a table of Mondelez employee salary data, write a SQL query to find the top 3 highest earning employees in each department.

Mondelez Example Input:

employee_idnamesalarydepartment_id
1Emma Thompson38001
2Daniel Rodriguez22301
3Olivia Smith20001
4Noah Johnson68002
5Sophia Martinez17501
8William Davis68002
10James Anderson40001

Example Input:

department_iddepartment_name
1Data Analytics
2Data Science

Example Output:

department_namenamesalary
Data AnalyticsJames Anderson4000
Data AnalyticsEmma Thompson3800
Data AnalyticsDaniel Rodriguez2230
Data ScienceNoah Johnson6800
Data ScienceWilliam Davis6800

Solve this question interactively on DataLemur:

Top 3 Department Salaries

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 tough, you can find a detailed solution here: Top 3 Department Salaries.

SQL Question 3: What are the differences between an inner and a full outer join?

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.

Mondelez SQL Interview Questions

SQL Question 4: Analyze Monthly Sales of Products

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.

Example Input:

product_idproduct_name
1"Oreo"
2"Toblerone"
3"Cadbury Dairy Milk"
4"Milka"

Example Input:

sales_idproduct_iddatequantity
10112022-05-05100
10222022-05-06500
10312022-05-07200
10432022-05-08400
10522022-06-05150
10642022-06-06250
10732022-06-07500
10822022-07-05300

Example Output:

MonthProductTotal Quantity SoldRank
5"Toblerone"5001
5"Cadbury Dairy Milk"4002
5"Oreo"3003
6"Cadbury Dairy Milk"5001
6"Milka"2502
6"Toblerone"1503
7"Toblerone"3001

Answer:


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:

Amazon SQL Interview Question

SQL Question 5: What does it mean to denormalize a database?

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.

SQL Question 6: Filter Customer Orders Based on Different Conditions

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.

Example Input:

order_idcustomer_idproduct_nameorder_datecountry
1671235Oreo10/15/2022United States
2712486Cadbury10/30/2022United States
1321745Milka10/25/2022United Kingdom
5263376Oreo10/12/2022United States
9282974Cadbury10/08/2022Canada

Example Output:

product_nametotal_orders
Oreo2
Cadbury1

Answer:


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 .

SQL Question 7: Can you explain what a cross-join is and the purpose of using them?

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.

SQL Question 8: Locating specific customer records

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:

Example Input:

customer_idnamecityregistration_date
101Joe SmithSan Francisco2020-04-05
102Emily DavisSanta Monica2019-08-15
103Michael JohnsonDenver2021-05-11
104Sarah BrownSan Diego2022-01-21
105David WilliamsBoston2021-11-03

The query should return all records for customers who live in a city that starts with 'San'.

The resulting output should look like this:

Example Output:

customer_idnamecityregistration_date
101Joe SmithSan Francisco2020-04-05
104Sarah BrownSan Diego2022-01-21

Answer:

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'.

Mondelez SQL Interview Tips

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.

DataLemur Question Bank

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.

DataLemur SQL Tutorial for Data Science

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.

Mondelez Data Science Interview Tips

What Do Mondelez Data Science Interviews Cover?

Beyond writing SQL queries, the other types of problems tested in the Mondelez Data Science Interview include:

Mondelez Data Scientist

How To Prepare for Mondelez Data Science Interviews?

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.

Ace the Data Science Interview by Nick Singh Kevin Huo

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.

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