11 Monster Beverage SQL Interview Questions (Updated 2024)

Monster Beverage employees write SQL queries to analyze sales data across different regions, helping the company understand which products are doing well in specific markets. They also use SQL to manage inventory records, ensuring that production planning aligns with demand, this is why Monster Beverage asks SQL query questions during interviews for Data Analyst, Data Science, and BI jobs.

So, to help you study for the Monster Beverage SQL interview, we've collected 11 Monster Beverage SQL interview questions in this blog.

11 Monster Beverage SQL Interview Questions

SQL Question 1: Analyzing Power Consumers of Monster Beverages

Given two tables, and , write a SQL query to identify the top 5 users ("power users") who have bought the most number of cases of Monster Beverages in 2022.

The table has the following schema:

• purchase_id: Unique identifier for each purchase (integer)
• user_id: Unique identifier for the user (integer)
• purchase_date: The date of purchase (date)
• quantity: The number of cases purchased (integer)

Example Input:

The table has the following schema:

• user_id: Unique identifier for the user (integer)
• name: The user's name (string)
• email: The user's email address (string)

Example Input:

Answer:

This query joins the table with the table on the user_id. It then filters for purchases made in the year 2022. The query then sums up the quantity of cases purchased for each user in the table. In the end, the query orders the users by the total number of cases purchased in descending order and limits the results to the top 5. We arguably define these as our "power users". These would be the users who purchase the most beverages frequently and might be most important to Monster Beverage.

To practice a super-customer analysis question on DataLemur's free online SQL code editor, try this Microsoft Azure Cloud SQL Interview Question:

Visit Monster Beverage Corporation's press releases to learn about their latest developments and growth strategies in the energy drink market! Keeping an eye on Monster's progress can provide valuable insights into how they are positioning themselves in a rapidly evolving industry.

SQL Question 2: 2nd Highest Salary

Imagine you had a table of Monster Beverage employee salary data. Write a SQL query to find the 2nd highest salary at the company.

Monster Beverage Example Input:

Example Output:

Check your SQL query for this problem and run your code right in the browser:

Answer:

You can find a detailed solution with hints here: 2nd Highest Salary.

SQL Question 3: In SQL, Are NULL values the same as a zero or blank space?

A NULL value represents a missing or unknown value. It is different from a zero or a blank space, which are actual values. It is important to handle NULL values properly in SQL because they can cause unexpected results if not treated correctly.

SQL Question 4: Analyzing Sales Data per Region for Monster Beverage

Monster Beverage Corporation is looking to understand their sales data better. They want a query that aggregates the total sales per region for each year, starting from 2017 and compares the current year's sales to the previous year. They have a sales table tracking all sales transactions, and a regions table which defines which city belongs to which region. Here are the tables' structures:

Example Input:

Example Input:

Example Output:

Answer:

Here is the SQL query using window function to answer this question::

This query first calculates the total sales for each region for each year via the subquery, only considering sales records from 2017. Then it utilizes the window function , which takes the value of from the previous row in the ordered (by ) partition of rows with the same . This generates the column. The result is ordered by and .

Pro Tip: Window functions are a frequent SQL interview topic, so practice every window function problem on DataLemur

SQL Question 5: What's the purpose of a primary key?

A primary key is a column or group of columns that uniquely identifies a row in a table. For example, say you had a database of Monster Beverage marketing campaigns data:

In this Monster Beverage example, the CampaignID column is the primary key of the MarketingCampaigns table. The constraint ensures that no two rows have the same CampaignID. This helps to maintain the integrity of the data in the table by preventing duplicate rows.

SQL Question 6: Monster Beverage Sales Analysis

As a data analyst in Monster Beverage Corporation, your task is to design a database to store different versions of Monster Beverage products, their sales across different regions, and premium retailer partnerships. With the data stored in your database, you need to write a PostgreSQL query to calculate the total sales of each product per region and the total sales from premium retailers.

Sample Input:

Sample Input:

Sample Input:

Sample Input:

Answer:

After executing the above queries, one would get the total sales of each product per region and the total sales from premium retailers.

SQL Question 7: How do the 'BETWEEN' and 'IN' commands differ?

The operator is used to select rows that fall within a certain range of values, while the operator is used to select rows that match values in a specified list.

For example, suppose you are a data analyst at Monster Beverage and have a table of advertising campaign data. To find campaigns with a spend between $1k and$5k, you could use BETWEEN:

To find advertising campaigns that were video and image based (as opposed to text or billboard ads), you could use the operator:

SQL Question 8: Average sales per month for Monster Beverage Products

Suppose you are given two tables - table which tracks each sale by a unique sale id, product id, number of cans sold, and date of sale and table which has information about each product including its product_id, name and cost.

The sales table looks as such:

Example Input:

And the products table looks as such:

Example Input:

Your task is to write a SQL query that finds the average number of cans sold for each product per month.

Answer:

Here is how you can achieve that with PostgreSQL:

This SQL query first joins the sales and products tables on the product_id. It then groups the result by month and product_name and uses the AVG function to compute the average number of cans sold per month for each product.

To practice a very similar question try this interactive Wayfair Y-on-Y Growth Rate Question which is similar for calculation of sales metrics or this Amazon Average Review Ratings Question which is similar for calculating average based on product id.

SQL Question 9: Analyze Click-through and Conversion Rates for Monster Beverage Campaigns

Problem Statement:

The marketing team at Monster Beverage runs many digital ad campaigns. For each campaign, they analyze both click-through rates (CTR) and click-through conversion rates (CTCR), i.e., how many of those who clicked on an ad actually proceed to purchase a product.

You are provided with two tables - and .

• The table keeps track of each ad campaign conducted by the company. For each ad, it records the campaign_id, the number of times the ad was viewed and the number of times the ad was clicked.
• The table logs the purchases made by customers. For each purchase, it records the campaign_id associated with the ad that the customer clicked on before making the purchase.

Write an SQL query that calculates the CTR and CTCR for each campaign, grouped by product.

Sample Input:

Sample Input:

Answer:

This query first groups the data by . For each product, the (click-through rate) is calculated as the total number of divided by the total number of , multiplied by 100 to convert it into a percentage. The (click-through conversion rate) is calculated as the total number of purchases divided by the total number of , also multiplied by 100 to get a percentage. The resulting table provides the and for each product.

To solve a related SQL problem on DataLemur's free interactive SQL code editor, try this Facebook SQL Interview question:

SQL Question 10: How does differ from just ?

The operator merges the output of two or more statements into a single result set. It ignores duplicated rows, and makes sure each row in the result set is unique.

For a concrete example, say you were a Data Analyst at Monster Beverage working on a Marketing Analytics project. If you needed to get the combined result set of both Monster Beverage's Google and Facebook ads you could execute this SQL query:

The operator works in a similar way to combine data from multiple statements, but it differs from the operator when it comes to handling duplicate rows. Whereas filters out duplicates (so if the same ad_name is run on both Facebook and Google, it only shows up once), outputs duplicate rows.

SQL Question 11: Max beverage sales by region

A company named Monster Beverage sells different types of beverages across various regions. Each beverage has a unique id. For a given time period, the number of sales of each beverage by region are recorded. Write a SQL query that provides the beverage_id of the beverage with the maximum total sales per region.

Example Input:

Example Output:

Answer:

This PostgreSQL query groups the data in the table by and , sums the number_sold for each group, and orders the groups by the total sales in descending order. The query then takes only the record with the highest total sales from each region.

Monster Beverage SQL Interview Tips

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 Monster Beverage SQL interview questions, you should also solve the 200+ DataLemur interview questions which come from companies like tech companies and food and facilities companies like Monster Beverage.

Each DataLemur SQL question has multiple hints, full answers and most importantly, there's an interactive coding environment so you can instantly run your SQL query and have it checked.

To prep for the Monster Beverage SQL interview you can also be helpful to practice SQL problems from other food and facilities companies like:

• Coca-Cola SQL questions
• PepsiCo SQL questions
• McCormick SQL questions
• Ingredion SQL questions

But if your SQL coding skills are weak, don't worry about jumping right into solving questions – strengthen your SQL foundations with this free SQL for Data Analytics course.

This tutorial covers topics including advantages of CTEs vs. subqueries and finding NULLs – both of these come up frequently in Monster Beverage interviews.

Monster Beverage Data Science Interview Tips

What Do Monster Beverage Data Science Interviews Cover?

Besides SQL interview questions, the other types of problems tested in the Monster Beverage Data Science Interview include:

• Probability & Statistics Questions
• Python or R Programming Questions
• Product Data Science Interview Questions
• ML Modelling Questions
• Behavioral Questions centered on Monster Beverage company values

How To Prepare for Monster Beverage Data Science Interviews?

To prepare for Monster Beverage Data Science interviews read the book Ace the Data Science Interview because it's got:

• 201 interview questions sourced from companies like Google, Tesla, & Goldman Sachs
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