11 Crocs SQL Interview Questions (Updated 2024)

Data Science, Data Engineering and Data Analytics employees at Crocs write SQL queries all the damn time for work. They use SQL for analyzing customer purchasing behavior and optimizing inventory management based on sales trends. For this reason Crocs asks prospective hires SQL interview problems.

To help prep you for the Crocs SQL interview, here’s 11 Crocs SQL interview questions – scroll down to start solving them!

11 Crocs SQL Interview Questions

Alright, let's frame a SQL interview question that is tailored for a "Crocs" business context.

"Crocs" is a company that sells footwear. For their business, a "whale user" might be defined as a user who frequently purchases a lot of items. So, we could construct a question like this:

SQL Question 1: Identify High-Value Customers of Crocs

Given the table with the following columns:

• (unique identifier for each order)
• (unique identifier for each customer)
• (date when the purchase was made)
• (number of items ordered)
• (total dollar amount of the order)

Example Input:

Write a SQL query to identify customers who can be classified as 'Whale Users'. In this case, define 'Whale Users' as customers who have made purchases of total amount over $500 in the past month.

Answer:

This query groups all orders by customer and sums the total dollar amount spent for each customer. It then uses the clause to filter out customers whose total spending does not exceed $500. The clause ensures that we are only considering purchases from the last month.

To practice a related customer analytics question on DataLemur's free interactive coding environment, try this Microsoft Teams Power User SQL Interview Question:

SQL Question 2: Department vs. Company Salary

Imagine you had a table of Crocs employee salaries, along with which department they belonged to. Write a query to compare the average salary of employees in each department to the company's average salary. Return the comparison result as 'higher', 'lower', or 'same' for each department. Display the department ID, payment month (in MM-YYYY format), and the comparison.

Try this interview question directly within the browser on DataLemur:

The solution is LONG – 30+ lines of SQL. You can find a step-by-step solution with hints here: Department Salaries.

SQL Question 3: Why is normalizing a database helpful?

Database normalization is helpful because it improves the performance, flexibility, and scalability of a database. Some specific benefits of normalization include:

• Reducing Redundancy: Normalization helps to minimize redundancy by breaking down a larger general table into smaller, more specific tables. This can often reduce the amount of data that needs to be accessed for particular queries.

• Improving Data Integrity: Normalization helps to ensure the integrity of the data by minimizing the risk of data inconsistencies and anomalies. It does this by establishing clear relationships between the tables via primary and foreign keys.

• Increasing Flexibility: Normalization makes it easier to modify the structure of the database, as there is less redundancy, so it allows you to make changes to one table without affecting others. This makes it easier to adapt the database to changing business needs (and change is basically constant at Crocs!)

• Improving Performance: Normalization can improve the performance of the database by reducing the amount of data that needs to be stored and accessed in a single table. This can result in faster query times and better overall performance.

• Easier Data Modeling: Normalization can make it easier to model the data in a logical and intuitive way, since less random data is commingled together, which improves the overall database design.

SQL Question 4: Calculate the Average Monthly Review Ratings Per Product

As a data analyst working for Crocs, you are asked to perform monthly analysis of the products' ratings given by the users. Write a SQL query to find out the average stars (rounded to two decimal places) each product received each month. Please note that the month is derived from the submit_date column in the table.

The dataset has the following columns:

reviews (review_id, user_id, submit_date, product_id, stars)

Example Input:

Example Output:

Answer:

Here is the PostgreSQL query to solve this problem:

This query first uses to derive the month of review submission from the column. It then groups by and to calculate the average stars each product received each month. The command is used to round the average stars to two decimal places. The result is returned in the ascending order of month and product.

p.s. Window functions show up pretty often during SQL interviews, so practice the 27+ window function questions on DataLemur

SQL Question 5: What is a cross-join, and when would you use one?

A cross-join, also known as a cartesian join, is a JOIN that produces the cross-product of two tables. In a cross-join, each row from the first table is matched with every row from the second table, resulting in a new table with a row for each possible combination of rows from the two input tables.

Let's say you were building a Machine Learning model that attempts to score the probability of a customer purchasing a Crocs product. Before working in Pandas and Tensorflow, you might want to do some Exploratory Data Analysis (EDA) in SQL, and generate all pairs of customers and Crocs products.

Here's a cross-join query you could run:

Cross-joins are useful for generating all possible combinations, but they can also create huge tables if you're not careful. For instance, if you had 10,000 potential customers and Crocs had 500 different product SKUs, the resulting cross-join would have 5 million rows!

SQL Question 6: Analyze Sales of Different Shoe Models by Region

Crocs, being a multinational shoe company, has a wide variety of designs that are sold in different regions across the world. They want to analyze the sales of different shoe models by region. The data is recorded in two tables - and .

The table consists of the following columns -

• (unique identifier for each sale)
• (identifier of the shoe model that was sold)
• (region where the sale took place)
• (date of sale)

And the table consists of the following columns -

• (unique identifier for each shoe model)
• (name of the shoe model)

Write a PostgreSQL query that returns the total sales for each shoe model, sorted by region and total sales in descending order.

The output of the query should contain the following columns -

Example Input:

Example Input:

Answer:

The PostgreSQL query would look as follows:

This query first joins the Sales and ShoeModels table on the column. The GROUP BY statement is then used with the region and model_name columns, and the COUNT function is used to calculate the total sales for each shoe model. The result is sorted in order of region first, and then by total sales in descending order within each region. This provides a region-wise analysis of the total sales of each shoe model.

SQL Question 7: What does the SQL command do?

If you have two tables and want to retrieve only the rows that are present in both tables, just use the operator!

For example, let's use to find all of Crocs's Facebook video ads that are also being run on YouTube:

SQL Question 8: Find Customers who Bought Pink Crocs in Summer 2021

Given the tables and , write a SQL query to find all customers who bought pink Crocs in the summer of 2021(June - August). Assume that the 'product_name' field in the 'orders' table contains the product color and name(e.g., 'Pink Crocs').

Here are the sample and tables:

Answer:

This query combines the and table using a JOIN on the common field . The WHERE clause filters the resulting dataset for orders where the product was 'Pink Crocs' and the order date is within summer 2021 (June to August). This will return the customers(full details) who bought 'Pink Crocs' during summer 2021.

SQL Question 9: Analyzing Click-Through-Rate For Crocs' Products

Given a database of Crocs' website visits and conversions, calculate the click-through-rate (CTR) from viewing a product to adding that product to the cart for each product.

We'll be using two tables, and .

table logs the user visits, with each row representing a user viewing a product.

Example Input:

table logs the events of users adding products to their cart.

Example Input:

Answer:

Here's how you can solve this using SQL:

This query first joins the and tables on and , and then filters for cases where the product was added to the cart on the same day it was viewed. It then groups by and calculates the click-through-rate by dividing the count of records by the count of records for each . Note that we handle the division by zero case (when there were views but no adds to cart) by casting the counts to float before the division.

To practice a similar problem about calculating rates, solve this TikTok SQL question on DataLemur's interactive SQL code editor:

SQL Question 10: What's the purpose of the function in SQL?

The function can take in multiple paramaters, and returns the first input paramater that is not null. If all arguments are null, the COALESCE function will return null too.

Suppose you have a table of Crocs salespeople and the number of deals they closed. If a salesperson didn't close any deals, the data from the 3rd-party CRM system exports a NULL value.

To change these NULLs to zeros, you can use the function in the following way:

This would result in the following data:

SQL Question 11: Average ratings for Crocs footwear variants

Crocs manufactures different styles of their iconic footwear. Your task is to determine the average rating for each variant of Crocs shoes sold online from their customer reviews database. We want to find the average rating per shoe variant for the month of June 2022.

Here's a markdown-formatted table with the following columns: (ID of the review), (ID of the user who submitted the review), (date the review was submitted), (ID of the product being reviewed), and (the rating given by the user, from 1 to 5).

Example Input:

Example Output:

Answer:

This SQL query extracts the month from the field, groups the reviews by the extracted month and . It calculates the average rating () for each group and then filters the results for only those entries from the month of June 2022. The result is a table that shows the average rating for each shoe variant for the month of June in the year 2022.

Preparing For The Crocs SQL Interview

The key to acing a Crocs SQL interview is to practice, practice, and then practice some more! Besides solving the earlier Crocs SQL interview questions, you should also solve the 200+ tricky sql questions on DataLemur which come from companies like Google, Facebook, Microsoft and Amazon.

Each interview question has hints to guide you, detailed solutions and most importantly, there's an interactive SQL code editor so you can easily right in the browser your SQL query answer and have it checked.

To prep for the Crocs SQL interview you can also be wise to solve SQL questions from other apparel companies like:

• Hanes SQL questions

• PVH Corp SQL questions

• Levi Strauss SQL questions

• Columbia Sportswear SQL questions

However, if your SQL coding skills are weak, don't worry about diving straight into solving questions – refresh your SQL knowledge with this SQL tutorial for Data Scientists & Analysts.

This tutorial covers things like LAG window function and sorting results with ORDER BY – both of which pop up routinely during Crocs SQL assessments.

Crocs Data Science Interview Tips

What Do Crocs Data Science Interviews Cover?

In addition to SQL interview questions, the other types of problems to practice for the Crocs Data Science Interview include:

• Statistics and Probability Questions
• Coding Questions in Python or R
• Business Sense and Product-Sense Questions
• Machine Learning and Predictive Modeling Questions
• Behavioral Based Interview Questions

How To Prepare for Crocs Data Science Interviews?

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

• 201 interview questions sourced from FAANG tech companies
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