10 Kimberly-Clark SQL Interview Questions (Updated 2024)

Data Science, Data Engineering and Data Analytics employees at Kimberly-Clark use SQL for analyzing sales patterns across various markets to optimize product distribution and pricing, as well as optimizing supply chain logistics by analyzing inventory data to reduce stockouts and overstocking. Because of this, Kimberly-Clark often uses SQL problems during job interviews.

So, to help you prep, we've collected 10 Kimberly-Clark SQL interview questions – can you answer each one?

10 Kimberly-Clark SQL Interview Questions

SQL Question 1: Identify Top Buyers for Kimberly-Clark

Kimberly-Clark is a multinational corporation that produces mostly paper-based consumer products. As a data analyst, you're tasked to identify their VIP users who frequently purchase online from the past year. VIP users are defined as customers who made the highest total amount of purchases.

Example Input:

For this problem, assume that every record in the table signifies a completed transaction. The column specifies the dollar amount of each transaction.

Your SQL query should provide the following output:

Example Output:

Answer:

This SQL query aggregates the total purchase amount per user from orders made in the year 2021. It then sorts the results in descending order based on the total purchase amount to show the 'VIP users'. The 'user_id' and 'total_amount' are selected for appropriate output. The time filtering (2021) is done in the WHERE clause.

To practice a similar power-user data analysis problem question on DataLemur's free online SQL code editor, try this Microsoft Teams Power User SQL Interview Question:

SQL Question 2: 2nd Highest Salary

Suppose there was a table of Kimberly-Clark employee salary data. Write a SQL query to find the 2nd highest salary among all employees.

Kimberly-Clark Example Input:

Example Output:

Solve this question and run your code right in the browser:

Answer:

You can find a step-by-step solution with hints here: 2nd Highest Salary.

SQL Question 3: How are left and right joins different from each other?

A join in SQL allows you to retrieve data from multiple tables and combine it into a single set of results.

To demonstrate the difference between left vs. right join, 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 sales table, which has data on how many products were sold and which Google Ads keyword drove that sale.

: A LEFT JOIN retrieves all rows from the left table (in this case, the table) and any matching rows from the right table (the Sales table). If there is no match in the right table, values will be returned for the right table's columns.

: A RIGHT JOIN retrieves all rows from the right table (in this case, the Sales table) and any matching rows from the left table (the table). If there is no match in the left table, values will be returned for the left table's columns.

SQL Question 4: Calculate Monthly Sales for Kimberly-Clark

Kimberly-Clark, a company that predominantly manufactures sanitary paper products and surgical & medical instruments, would love to track its monthly sales. You are required to write a SQL query that calculates the total sales each product has generated on a monthly basis. For the purpose of simplicity, you can assume that each transaction represents a single unit sale.

Consider that we have the following table as data foundation:

Example Input:

Where is unique to each sale, identifies the product sold, is the date the sale was made, and is the price product was sold for.

Your task is to produce output like this:

Example Output:

Answer:

Here is a SQL query that can be used to solve the problem:

This query operates by essentially doing the following:

• Extract the month from the transaction_date using the EXTRACT function.
• Grouping the sales records by both the month and the product_id using GROUP BY statement.
• Calculating the total sales by summing the prices of records in each group using the SUM aggregate function.
• Ordering the result by month and product_id for an organized representation.

For more window function practice, try this Uber SQL problem within DataLemur's online SQL code editor:

SQL Question 5: Can you explain the concept of a constraint in SQL?

Constraints are used to specify the rules concerning data in the table. It can be applied for single or multiple fields in an SQL table during the creation of the table or after creating using the ALTER TABLE command. The constraints are:

Say you were storing sales analytyics data from Kimberly-Clark's CRM inside a database. Here's some example constraints you could use:

• PRIMARY KEY constraint: You might use a PRIMARY KEY constraint to ensure that each record in the database has a unique identifier. For example, you could use the "opportunity_id" field as the primary key in the "opportunities" table.

• FOREIGN KEY constraint: You might use a FOREIGN KEY constraint to link the data in one table to the data in another table. For example, you could use a foreign key field in the "opportunities" table to reference the "account_id" field in the "accounts" table.

• NOT NULL constraint: You might use a NOT NULL constraint to ensure that a field cannot contain a NULL value. For example, you could use a NOT NULL constraint on the "opportunity_name" field in the "opportunities" table to ensure that each opportunity has a name.

• UNIQUE constraint: You might use a UNIQUE constraint to ensure that the data in a field is unique across the entire table. For example, you could use a UNIQUE constraint on the "email" field in the "contacts" table to ensure that each contact has a unique email address.

• CHECK constraint: You might use a CHECK constraint to ensure that the data in a field meets certain conditions. For example, you could use a CHECK constraint to ensure that the "deal_probability" field in the "opportunities" table is a value between 0 and 100.

• DEFAULT constraint: You might use a DEFAULT constraint to specify a default value for a field. For example, you could use a DEFAULT constraint on the "stage" field in the "opportunities" table to set the default value to "prospecting"

SQL Question 6: Filter Customer Records Based on Purchase Frequency

As a data analyst of Kimberly-Clark, you've got access to the database and your manager asked you to identify customers who have frequently purchased paper towels in the last six months. The specified conditions are:

1. Customers who purchased more than 4 times in the last six months.
2. Among those customers, those who have spent more than $100 in total in the past six months.

Example Input:

Example Input:

Answer:

This query will first join the table with a sub-query of the table that calculates the total counts and amounts of purchases for 'Paper Towels' for each customer for the last 6 months. Following this, it uses to filter down to only include customers who satisfy the specified conditions.

SQL Question 7: In SQL, are NULLs treated the same as zero's and blank spaces?

A value represents a missing or unknown value. It is different from a zero or a blank space, which are actual values.

It's important to handle values properly in SQL because they can cause unexpected results if not treated correctly. For example, comparisons involving values always result in . Also values are not included in calculations. For example, will ignore values in the column.

SQL Question 8: Analyzing Click-through Conversion Rate for Kimberly-Clark products

Kimberly-Clark is a multinational corporation that produces mostly paper-based consumer products. Consider a scenario where they have launched a digital platform to sell their products. They want to analyze the click-through conversion rate of viewing products to the stage of adding a product to the cart can give valuable insights on user behavior and product visibility.

Here are your tasks:

1. Calculate the click-through conversion rate, for each product, defined as the number of times a product has been added to the cart after being viewed divided by the number of times it has been viewed.

2. Return each product_id with its corresponding conversion rates sorted in descending order.

Example Input:

Example Input:

Answer:

This query calculates the conversion rate by dividing the number of times each product is added to the cart () by the number of times each product is viewed (). The use of COALESCE ensures that if a product is viewed but never added to a cart (resulting in a NULL value after the LEFT JOIN), it will still be included in the final result with a of 0. The result is then sorted in descending order by the conversion rate.

To practice another question about calculating rates, solve this SQL interview question from TikTok on DataLemur's online SQL code editor:

SQL Question 9: Calculate the Average Sales by Region and by Product

You are given a table which includes sales data for Kimberly-Clark products across multiple regions. Each row represents a sale made, and logs the product_id, the region where the sale was made, and the total sale amount. The company has asked you to write an SQL query to calculate the average sales by region and by each product.

Example Input:

Example Output:

Answer:

Here's the PostgreSQL query to solve it:

This script groups the table by and , then calculates the average of within each group. The result will be the average sale amount of each product from Kimberly-Clark that has been sold in each region.

SQL Question 10: What do stored procedures do?

Stored procedures are like functions in Python – they can accept input params and return values, and are used to encapsulate complex logic.

For example, if you worked as a Data Analyst in support of the Marketing Analytics team at Kimberly-Clark, a common task might be to find the conversion rate for your ads given a specific time-frame. Instead of having to write this query over-and-over again, you could write a stored procedure like the following:

To call this stored procedure, you'd execute the following query:

Preparing For The Kimberly-Clark SQL Interview

The key to acing a Kimberly-Clark SQL interview is to practice, practice, and then practice some more! In addition to solving the above Kimberly-Clark SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like Google, Microsoft and Silicon Valley startups.

Each problem on DataLemur has multiple hints, full answers and most importantly, there is an online SQL coding environment so you can right online code up your SQL query answer and have it graded.

To prep for the Kimberly-Clark SQL interview it is also helpful to practice SQL problems from other consumer good companies like:

• Procter & Gamble SQL questions
• Spectrum Brands SQL questions
• Energizer SQL questions
• Central Garden & Pet SQL questions

Learn more about Kimberly-Clark's commitment to innovation, sustainability, and community through their latest news and updates!

However, if your SQL coding skills are weak, don't worry about going right into solving questions – strengthen your SQL foundations with this SQL tutorial for Data Analytics.

This tutorial covers topics including LEFT vs. RIGHT JOIN and filtering data with boolean operators – both of these pop up often in SQL job interviews at Kimberly-Clark.

Kimberly-Clark Data Science Interview Tips

What Do Kimberly-Clark Data Science Interviews Cover?

In addition to SQL interview questions, the other types of problems to prepare for the Kimberly-Clark Data Science Interview are:

• Statistics & Probability Questions
• Coding Questions in Python or R
• Product Analytics Questions
• ML Modelling Questions
• Resume-Based Behavioral Questions

How To Prepare for Kimberly-Clark Data Science Interviews?

To prepare for the Kimberly-Clark Data Science interview have a firm understanding of the company's values and company principles – 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 tech companies like Google & Microsoft
• A Crash Course covering SQL, Product-Sense & ML
• Amazing Reviews (1000+ reviews, 4.5-star rating)