9 Dow Chemical SQL Interview Questions (Updated 2024)
At Dow Chemical, SQL is used to analyze chemical reaction rates, yield optimization, and supply chain logistics by querying their large databases, which contain data on chemical properties, production volumes, and shipping schedules. That is the reason why Dow Chemical includes SQL coding questions in interviews for Data Analytics, Data Science, and Data Engineering jobs.
So, to help you study, here's 9 Dow Chemical SQL interview questions – able to answer them all?
9 Dow Chemical SQL Interview Questions
SQL Question 1: Calculate Average Sales Price Per Month Per Product
Dow Chemical is a multinational chemical corporation in the United States. Assume, you are given a sales dataset, and are asked to find the average sales price per month for each product.
You have one table, , that contains all sales transactions. Here is what sample data from the table looks like:
Example Input
| transaction_id | date | product_id | unit_price | quantity |
|---|---|---|---|---|
| 1230 | 02/25/2021 | 764 | 10 | 100 |
| 3211 | 03/12/2021 | 130 | 5 | 200 |
| 4352 | 03/25/2021 | 764 | 9 | 150 |
| 6279 | 04/21/2021 | 130 | 6 | 100 |
| 8437 | 04/29/2021 | 226 | 15 | 50 |
Your task is to write a query that returns the average unit price for each product every month. The result should have three columns: month, product_id, and avg_unit_price. Order the result by month and then by product_id.
Answer:
Here is a PostgreSQL solution using table :
The query is using the clause, which is a way to apply a function across a set of rows which are somehow related to the current row. Within the parentheses , we can specify which rows we are considering - these rows are referred to as a window, which is where window functions get their name from.
In the above query, we are using the clause to divide the rows into different windows. We are creating one window for each combination of and . Once we have our windows, we can apply the function to calculate the average unit price for each window.
We are using function to extract the month from column and using this calculated month column in the clause.
Finally, the clause is used to sort the results by month and then by .
p.s. Window functions show up super often during SQL interviews, so practice the 27+ window function questions on DataLemur
SQL Question 2: Department Salaries
You're given a table of Dow Chemical employee and department salary data. Write a SQL query to find departments where the departments average salary is higher than the company's average salary.
Write a SQL query for this interview question directly within the browser on DataLemur:
The answer is LONG – 30+ lines of SQL. You can find a step-by-step solution with hints here: Department Salaries.
SQL Question 3: Can you explain what a cross-join is and the purpose of using them?
A cross-join, also referred to as a cartesian join, is a type of JOIN that returns the cross-product of two tables. In a cross-join, each row from the first table is paired 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.
For example, say you worked on the Marketing Analytics team at Dow Chemical, and were tasked to understand what advertising copy (text) goes best with what advertising creative (the photo that gets used along with the text copy).
Here is an example of a cross-join between a table of ad_copy and a table of ad_creative:
You could this query to generate all possible combinations of ad copy and ad creative to help you create more effective ads for Dow Chemical. Just be careful: if you had 1,000 rows of ad copy and 1,000 different image creatives, the resulting cross-join would have 1 million rows! As a result, it's important to use cross-joins judiciously, and make sure that you're not generating more data than you need to.
SQL Question 4: Chemical Supplies Inventory Management
Dow Chemical is maintaining an inventory of chemical supplies. These supplies are stored in multiple warehouses. Each specific chemical supply is logged with its quantity and the respective warehouse. Occasionally, the company needs to transfer some chemical supplies from one warehouse to another.
You are asked to design a database schema to keep track of this activity as well as to run queries providing insights about available chemical supply in each warehouse, the total quantity, and the average quantity of each supplied chemical in all warehouses.
Hint: You can start with the following entities: , , , and .
Example Input:
| warehouse_id | location |
|---|---|
| 101 | Michigan |
| 102 | Texas |
| 103 | California |
Example Input:
| supply_id | supply_name |
|---|---|
| 1 | Chemical A |
| 2 | Chemical B |
| 3 | Chemical C |
Example Input:
| inventory_id | warehouse_id | supply_id | quantity | log_date |
|---|---|---|---|---|
| 4501 | 101 | 1 | 5000 | 06/25/2022 00:00:00 |
| 4502 | 102 | 2 | 3000 | 06/27/2022 00:00:00 |
| 4503 | 103 | 3 | 7000 | 06/29/2022 00:00:00 |
| 4504 | 101 | 2 | 4500 | 07/01/2022 00:00:00 |
| 4505 | 103 | 1 | 9000 | 07/02/2022 00:00:00 |
Example Input:
| transfer_id | from_warehouse_id | to_warehouse_id | supply_id | quantity | transfer_date |
|---|---|---|---|---|---|
| 9001 | 101 | 102 | 1 | 1000 | 07/01/2022 00:00:00 |
| 9002 | 102 | 103 | 2 | 500 | 07/02/2022 00:00:00 |
| 9003 | 103 | 101 | 3 | 2000 | 07/03/2022 00:00:00 |
Answer:
Here is an example query to find the total quantity of each chemical supply available in a specific warehouse (e.g., warehouse 102):
This PostgreSQL query will return the total quantity of each chemical supply currently available in the warehouse with the ID 102. It joins the and tables on the , and groups by the and from the table. After that, it sums all quantities from the table for each chemical supply in the warehouse 102.
Please note that based on the provided test tables, we are considering only the initial quantities in the table. The table is not considered in this query. In a live scenario, amounts in the table will need to be added or subtracted from the inventories in the table.
SQL Question 5: Can you explain the distinction between a clustered and a non-clustered index?
Here is an example of a clustered index on the column of a table of Dow Chemical customer transactions:
This creates a clustered index on the column, which will determine the physical order of the data rows in the table.
Here is an example of a non-clustered index on the column of the same table:
This will create a non-clustered index on the column, which will not affect the physical order of the data rows in the table.
In terms of query performance, a clustered index is usually faster for searches that return a large number of records, while a non-clustered index is faster for searches that return a small number of records. However, updates to a clustered index are slower, as they require the data rows to be physically rearranged, while updates to a non-clustered index are faster, as they only require the index data structure to be updated.
SQL Question 6: Filtering Customer Data for Dow Chemical
Dow Chemical is interested in doing a strategic analysis of its customer database. The company wants to evaluate the purchasing activity of its customers in Canada who belong to the Industrial Solutions sector and have purchased a certain volume of their top-selling product (Polyethylene) during 2022.
Using the SQL database, write a query to filter out the customers who meet the above conditions.
Example Input
| customer_id | customer_name | sector | country |
|---|---|---|---|
| 5100 | Acme Corp | Industrial Solutions | Canada |
| 5101 | Beta Industries | Infrastructure | USA |
| 5102 | Gamma Solutions | Industrial Solutions | Canada |
| 5103 | Delta Inc | Consumer Care | Bangladesh |
Example Input
| order_id | customer_id | product | volume | order_date |
|---|---|---|---|---|
| 3300 | 5100 | Polyethylene | 9000 | 12/30/2022 |
| 3301 | 5100 | Propylene | 8000 | 12/29/2022 |
| 3302 | 5103 | Polyethylene | 5000 | 10/14/2022 |
| 3303 | 5102 | Polyethylene | 12000 | 09/22/2022 |
| 3304 | 5101 | Ethylene | 6000 | 08/17/2022 |
Answer
This query joins the and tables on the field. The clause is then used to filter for customers in Canada, from the Industrial Solutions sector who has purchased Polyethylene in the year 2022.
SQL Question 7: What does it mean to perform a self-join?
A self-join is a type of join in which a table is joined to itself. To perform a self-join, you need to specify the table name twice in the FROM clause, and give each instance of the table a different alias. You can then join the two instances of the table using a JOIN clause, and use a WHERE clause to specify the relationship between the rows.
Self-joins are the go-to technique for any data analysis that involves pairs of the same thing, like identifying pairs of products that are frequently purchased together like in this Walmart SQL interview question.
For another example, say you were doing an HR analytics project and needed to analyze how much all Dow Chemical employees in the same department interact with each other. Here's a self-join query you could use to retrieve all pairs of Dow Chemical employees who work in the same department:
This query returns all pairs of Dow Chemical employees who work in the same department, and excludes pairs where the employee's id is the same (since this would represent the same Dow Chemical employee being paired with themselves).
Sure, I am happy to help. Let's create a relevant question for Dow Chemical:
SQL Question 8: Analyzing Chemical Product Sales
You have two tables, one called which lists all of the chemical products Dow Chemical sells, and the other called , which documents every sale.
Each product has a and , and
Each sale entry has a , , , and .
Find the product with the highest total sales quantity for each month in 2022.
Example Input:
| product_id | product_name |
|---|---|
| 112 | Ethylene |
| 123 | Propylene |
| 234 | Butadiene |
| 345 | Toluene |
| 456 | Xylene |
Example Input:
| sale_id | sale_date | product_id | sale_quantity |
|---|---|---|---|
| 1001 | 01/15/2022 | 123 | 200 |
| 1002 | 01/28/2022 | 234 | 300 |
| 1003 | 01/30/2022 | 123 | 50 |
| 1004 | 02/10/2022 | 345 | 500 |
| 1005 | 02/20/2022 | 345 | 600 |
Example Output:
| month | product_id | product_name | total_quantity |
|---|---|---|---|
| 1 | 234 | Butadiene | 300 |
| 2 | 345 | Toluene | 1100 |
Answer:
This query first extracts the month and year from the . It then groups by and month, and calculates the total sale quantity for each product for each month. The ORDER BY clause is used to sort the results by month and total quantity, showing the product with the highest total sales quantity at the top for each month.
SQL Question 9: Find Customers Based on Location
As a data analyst at Dow Chemical, one of your responsibilities might involve filtering out information about customers.
For instance, suppose your manager asks you to filter out all the customers whose address contains the word 'Michigan'. This is essential because the company plans to execute an environmental sustainability campaign specifically in that region and needs to identify customers living there.
Example Input:
| customer_id | first_name | last_name | address |
|---|---|---|---|
| 1245 | John | Doe | 234 Pine St, Midland, MI |
| 2346 | Jane | Smith | 625 Broadway St, New York, NY |
| 1862 | Alice | Brown | 521 Oak St, Detroit, MI |
| 3421 | Bob | Jackson | 452 Elm St, Houston, TX |
| 9873 | Charlie | Davis | 178 Maple St, Midland, MI |
Example Output:
| customer_id | first_name | last_name | address |
|---|---|---|---|
| 1245 | John | Doe | 234 Pine St, Midland, MI |
| 1862 | Alice | Brown | 521 Oak St, Detroit, MI |
| 9873 | Charlie | Davis | 178 Maple St, Midland, MI |
Answer:
This SQL query will select all columns from the table where the field contains the word 'Michigan', case insensitive. The signs are wildcards allowing for any characters to be before or after the word 'Michigan' in the address.
Dow Chemical SQL Interview Tips
The key to acing a Dow Chemical SQL interview is to practice, practice, and then practice some more!
Beyond just solving the earlier Dow Chemical SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like Meta, Google and chemical companies like Dow Chemical.
Each SQL question has hints to guide you, detailed solutions and crucially, there's an interactive coding environment so you can instantly run your SQL query and have it executed.
To prep for the Dow Chemical SQL interview it is also wise to solve interview questions from other chemical companies like:
Learn how Dow is using science and innovation to create a more sustainable future with their latest news and updates!
But if your SQL foundations are weak, don't worry about jumping right into solving questions – refresh your SQL knowledge with this SQL tutorial for Data Scientists & Analysts.
This tutorial covers topics including CTE vs. Subquery and math functions – both of which show up frequently during SQL interviews at Dow Chemical.
Dow Chemical Data Science Interview Tips
What Do Dow Chemical Data Science Interviews Cover?
Beyond writing SQL queries, the other types of problems tested in the Dow Chemical Data Science Interview include:
- Probability & Statistics Questions
- Python or R Programming Questions
- Open-Ended Data Case Studies
- Machine Learning Questions
- Behavioral Interview Questions centered on Dow Chemical culture and values
How To Prepare for Dow Chemical Data Science Interviews?
To prepare for Dow Chemical Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions taken from tech companies like Google & Microsoft
- a crash course on Stats, SQL & ML
- over 1000+ reviews on Amazon & 4.5-star rating
Don't ignore the behavioral interview – prep for that with this behavioral interview question bank.