11 DuPont SQL Interview Questions (Updated 2024)

At DuPont, SQL is used to analyze datasets related to agricultural trends, such as crop yields, soil health, and weather patterns. It is also used to optimize manufacturing processes by streamlining production workflows and reducing waste, the reason why DuPont asks SQL problems in interviews for Data Science and Data Engineering positions.

Thus, to help you prepare for the DuPont SQL interview, we've collected 11 DuPont SQL interview questions – can you solve them?

11 DuPont SQL Interview Questions

SQL Question 1: Identify VIP Customers for DuPont

DuPont is a global company dealing with a wide range of products and services across industries. For their business, a VIP customer can be someone who purchases their products in large quantities or very frequently.

Assume you have access to DuPont's sales database, where there are two primary tables: and . The table contains information about the customers, and the table includes details about the purchases each customer makes.

Your task is to write a SQL query to identify the top 5 customers who have made the most purchases in the last year.

Example Input:

Example Input:

Answer:

This SQL query first joins the Users table with the Orders table on the . It then groups the data by the user's id and name and counts the number of orders each user has made. The WHERE clause filters out orders that were made before the last year. The results are sorted in descending order by the count of orders, and the top 5 users (customers) are returned.

To practice a similar power-user data analysis problem question on DataLemur's free interactive coding environment, try this recently asked Microsoft SQL interview question:

SQL Question 2: Top 3 Salaries

Given a table of DuPont employee salaries, write a SQL query to find the top 3 highest paid employees in each department.

DuPont Example Input:

Example Input:

Example Output:

Write a SQL query for this interview question and run your code right in DataLemur's online SQL environment:

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 hard to understand, you can find a step-by-step solution with hints here: Top 3 Department Salaries.

SQL Question 3: What's the SQL command do, and can you give an example?

When using , only rows that are identical in both sets will be returned.

For a concrete example, say you were a Data Analyst supporting the Sales Analytics team at DuPont, and data on potential sales leads lived in both Salesforce and Hubspot CRMs. To write a query to analyze leads created before 2023 started, that show up in BOTH CRMs, you would use the command:

SQL Question 4: Calculate the monthly average production of each product

DuPont, a chemical industry giant, manufactures a range of products. They would like to analyze the monthly average production volume per product to help forecast future demands and adjust their production strategies.

Suppose we have the following table:

Example Input:

DuPont wants a PostgreSQL query that will retrieve the monthly average production volume for each product. The result should be a list of the year, the month, the product_id, and the average volume produced in that month of that year for that particular product.

The output should follow the format below:

Example Output:

Answer:

Here's a SQL block for solving this problem:

This query utilizes the function to get the year and month from the . It then aggregates by , , and to compute the average using the function. The final result is sorted by , , and for better readability.

To practice a related window function SQL problem on DataLemur's free interactive SQL code editor, try this Google SQL Interview Question:

SQL Question 5: What would you do to speed up a slow SQL query?

Here's some strategies that can generally speed up a slow SQL query:

• Only query the columns you actually need
• Index the columns used in the query
• Use the right kind of JOIN (be explicit about inner vs. outer joins)
• Use the right data types (for example INT vs BIGINT can save you disk space and memory)

While there's an entire industry dedicated to database performance tuning , for Data Analyst and Data Science interviews at DuPont, knowing the above basics should be more than enough to ace the SQL interview! Data Engineers on the other hand should know more about the command and understand a bit more about a query-execution plan.

SQL Question 6: Chemical Inventory Management

DuPont, a multinational chemistry company, wants to keep track of their chemicals in their inventory. They have vast warehouses in different parts of the world. The chemical inventory system should allow them to track daily stock levels for each chemical in each warehouse, along with the delivery dates and received quantities.

Example Input:

Example Input:

Example Input:

Given the tables above, write a SQL query that shows the total quantities received for each chemical in each warehouse.

Answer:

The query joins the , and tables using the appropriate keys, then group the result set by the and . The function is used to calculate the total received quantities for each group. This gives us the total quantities received for each chemical in each warehouse.

SQL Question 7: What's a primary key?

A primary key is a column or set of columns in a table that uniquely identifies each row in the table. The primary key is used to enforce the uniqueness and non-nullability of the rows in the table.

In a SQL database, a primary key is defined using the constraint. For example, say you had a table of :

In this example, the column is the primary key of the DuPont employees table. It is defined as an integer and is marked as the primary key using the constraint.

A table can have only one primary key, but the primary key can consist of multiple columns. For example, say you had a table of DuPont customer transactions:

In the above example, the primary key of the Orders table consists of two columns: TransactionID and ProductID. This means that the combination of OrderID and ProductID must be unique for every row in the table.

SQL Question 8: DuPont's Ad Click-Through Rates.

DuPont invests significantly in digital advertising to promote its various product lines. Management wants to assess the effectiveness of these ads by looking at their click-through rates. They would also like to know the conversion rates from viewing a product to adding it to a cart. Below we will calculate the ad click-through and conversion rates for DuPont's top products.

The two tables we want to focus on are and . The table stores data about each digital ad's clicks and impressions, while the table logs when a product view results in adding the product to a cart.

Example Input:

Example Input:

Answer:

The SQL command for the above task would look like this in PostgreSQL:

This query first does a JOIN on product_id between the ads and conversions tables, then groups the data by product_id. It calculates the click-through rate as the total number of clicks divided by the total number of impressions, and the conversion rate as the total number of times a viewed product was added to the cart divided by the total number of views.

To practice a similar SQL problem on DataLemur's free online SQL code editor, try this Meta SQL interview question:

SQL Question 9: Average Supplier Delivery Time

DuPont interacts with a variety of suppliers who deliver materials necessary for their diverse range of products. They are interested in benchmarking their suppliers based on average delivery time: the average duration taken from placement of the order to the delivery of the materials.

Assume there is a table which records each supplier order ID, the supplier ID from whom the order was placed, the date when the order was placed (), and the date when the order was delivered ().

Example Input:

DuPont would like to generate a report that shows the average delivery time (in number of days) grouped by each supplier on a per-month level.

Answer:

This query uses to group the results by and the month of . The function is then used to calculate the average delivery time in days for each unique per month. The function extracts the month from the and the day from the subtraction of and to calculate the delivery time in days.

Example Output:

This output shows that for June, supplier 500 had an average delivery time of 9 days, while supplier 600 had an average deliver time of 5 days for the same month.

SQL Question 10: What does the SQL function do?

The function allows you to select the first non-NULL value from a list of values. It is often used to replace a NULL value with a default value in order to run aggregate functions like or on a column without being affected by values.

For example, if you conducted a customer satisfaction survey for a company and asked customers to rate their level of agreement with a statement on a scale of 1 to 10, some customers may not have answered all the questions, resulting in NULL values in the survey data:

To replace the NULLs in the column with the default value of 5 which corresponds to neutral on the survey, you could run the following SQL query:

You'd get the following output:

SQL Question 11: Find Customers with email domain @dupont.com

The company DuPont wants to find out the information of their employees who have registered on the company's services platform. They wish to filter out all the customers whose email ends with '@dupont.com'. You are given a 'customers' table with customer details. Determine the SQL query to filter these employees.

Example Input:

Example Output:

Answer:

The SQL command to filter out customers with the '@dupont.com' email domain will be as follows:

The symbol represents zero or more characters, and is the pattern to match. So any email with '@dupont.com' in it, regardless of what comes before '@', will be selected. This PostgreSQL query will yield the rows of all customers who have an email account under the domain '@dupont.com'.

Preparing For The DuPont SQL Interview

Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the DuPont SQL interview is to solve as many practice SQL interview questions as you can! Beyond just solving the above DuPont SQL interview questions, you should also solve the 200+ DataLemur interview questions which come from companies like tech companies and chemical companies like DuPont.

Each exercise has hints to guide you, step-by-step solutions and crucially, there's an online SQL code editor so you can right in the browser run your SQL query and have it checked.

To prep for the DuPont SQL interview you can also be a great idea to solve SQL questions from other chemical companies like:

• Dow Chemical SQL questions
• Ecolab SQL questions
• Air Products SQL questions
• Sherwin-Williams SQL questions

Stay ahead of the curve with DuPont's latest AI-powered solutions for the electronics industry!

In case your SQL skills are weak, forget about jumping right into solving questions – strengthen your SQL foundations with this free SQL for Data Analytics course.

This tutorial covers SQL concepts such as aggregate window functions and using ORDER BY – both of these show up routinely in DuPont SQL interviews.

DuPont Data Science Interview Tips

What Do DuPont Data Science Interviews Cover?

In addition to SQL query questions, the other types of problems tested in the DuPont Data Science Interview are:

• Stats Interview Questions
• Coding Questions in Python or R
• Product Analytics Questions
• ML Interview Questions
• Resume-Based Behavioral Questions

How To Prepare for DuPont Data Science Interviews?

To prepare for the DuPont Data Science interview have a firm understanding of the company's values and mission – this will be key to acing the behavioral interview. For the technical Data Science interviews, get ready by reading Ace the Data Science Interview. The book's got:

• 201 Interview Questions from Facebook, Google & startups
• A Refresher covering Python, SQL & ML
• Great Reviews (1000+ reviews, 4.5-star rating)