11 TreeHouse Foods SQL Interview Questions (Updated 2024)
At TreeHouse Foods, SQL is used to analyze sales trends for different product categories, allowing the company to identify which items are popular and when they sell best. It also allows them to predict future demand based on previous purchase behaviors, ensuring they can meet customer needs effectively, this is why TreeHouse Foods includes SQL query questions in their interviews for Data Analytics, Data Science, and Data Engineering positions.
To help you practice for the TreeHouse Foods SQL interview, we've collected 11 TreeHouse Foods SQL interview questions in this blog.
11 TreeHouse Foods SQL Interview Questions
SQL Question 1: Calculate Monthly Average Stars for Each Product
Please write a SQL query to calculate the monthly average stars for each product based on the 'reviews' table. For the purpose of this question, you can consider a month to be defined by the month part of the 'submit_date' field.
The 'reviews' table is structured as follows:
Example Input:
| review_id | user_id | submit_date | product_id | stars |
|---|---|---|---|---|
| 101 | 198 | 2023-01-13 | 50001 | 3 |
| 102 | 205 | 2023-01-15 | 50001 | 4 |
| 103 | 202 | 2023-01-18 | 69852 | 2 |
| 104 | 211 | 2023-02-05 | 50001 | 5 |
| 105 | 214 | 2023-02-06 | 69852 | 4 |
| 106 | 219 | 2023-02-10 | 50001 | 3 |
The output from your query should be:
Example Output:
| month | product_id | avg_stars |
|---|---|---|
| 1 | 50001 | 3.50 |
| 1 | 69852 | 2.00 |
| 2 | 50001 | 4.00 |
| 2 | 69852 | 4.00 |
Answer:
This query calculates the monthly average number of stars for each product. The TO_CHAR function is used to extract the month from the 'submit_date' timestamp. Then the AVG function is used to calculate the average number of stars based on month and product_id groups.
Pro Tip: Window functions are a popular SQL interview topic, so practice every window function problem on DataLemur
Explore TreeHouse Foods' news and media section to discover their latest product developments and corporate initiatives in the food industry! Keeping up with TreeHouse Foods can help you appreciate how they are adapting to meet consumer demands and market trends.
SQL Question 2: 2nd Largest Salary
Imagine there was a table of TreeHouse Foods employee salary data. Write a SQL query to find the 2nd highest salary amongst all the .
TreeHouse Foods Example Input:
| employee_id | salary |
|---|---|
| 1 | 2500 |
| 2 | 800 |
| 3 | 1000 |
| 4 | 1200 |
Example Output:
| second_highest_salary |
|---|
| 1200 |
Write a SQL query for this problem and run your code right in DataLemur's online SQL environment:
Answer:
You can find a detailed solution with hints here: 2nd Highest Salary.
SQL Question 3: What does the constraint do?
A is like a secret code that unlocks the door to another table. It's a field in one table that points to the (the master key) in another table. This helps keep the data in your database organized and tidy, because it won't let you add new rows to the table unless they have the correct secret code (a corresponding entry in the table).
It's also like a special bond between the two tables - if you try to delete the data, the will be like "Whoa, hold on! I still need that information!" and prevent the deletion from happening.
SQL Question 4: Product Sales Analysis
Question: TreeHouse Foods is a manufacturer and distributor of private label packaged foods and beverages. To help with sales forecasting, they require an analysis of their product sales. Specifically, develop a SQL query that provides the product name, the total quantity sold, and the total revenue received for each product, ordered by total quantity sold in descending order.
Assume the following tables:
Example Input:
| product_id | product_name | unit_price |
|---|---|---|
| 101 | Organic Pasta | $2.00 |
| 201 | Breakfast Cereal | $3.50 |
| 301 | Apple Sauce | $1.50 |
| 401 | Salsa Medium | $2.50 |
Example Input:
| sale_id | product_id | quantity | sale_date |
|---|---|---|---|
| 1 | 101 | 20 | 01/01/2023 |
| 2 | 201 | 10 | 01/03/2023 |
| 3 | 301 | 40 | 01/10/2023 |
| 4 | 401 | 5 | 01/25/2023 |
Answer:
This query joins and on and then groups by . It calculates the total quantity sold and the total revenue for each product. The results are then ordered in descending order by the total quantity sold. This will give TreeHouse Foods an overview of their best selling products and the revenue they have generated.
SQL Question 5: What's the difference between a correlated and non-correlated sub-query?
A correlated sub-query is one that relies on the outer query and cannot be run independently. It uses the outer query to filter or transform data by referencing a column from it, while the outer query uses the results of the inner query. On the other hand, a non-correlated sub-query is not linked to the outer query and can be run on its own. It does not reference any columns from the outer query and is used to retrieve additional data for the outer query.
Here is an example of a correlated sub-query:
This query selects the and total sales of all TreeHouse Foods customers in the sales table whose are greater than the average of their own customer group. The sub-query in this case is correlated with the outer query, as it references the column from the outer query ().
Here is an example of a non-correlated sub-query:
The sub-query in this case is non-correlated, as it does not reference any columns from the outer query.
Correlated sub-queries are slower to execute, as they have to be re-evaluated for each row of the outer query, while non-correlated sub-queries are faster, as they only have to be executed once.
SQL Question 6: Filter out and Aggregate Customer Data
You are a data analyst at TreeHouse Foods Inc. The company wants a report for its recent marketing campaign. You have been tasked to extract some specific information from the database.
The task requires you to get a list of all the customers who:
- Live in either or .
- Whose value is more than $5000.
- Joined our platform after .
Moreover, you should also calculate the average for these customers.
For this problem, let's assume our table is organized as follows:
Example Input:
| customer_id | customer_name | state | join_date | total_purchase |
|---|---|---|---|---|
| 0001 | John Smith | California | 2017-05-10 | 3000 |
| 0002 | Sara Johnson | New York | 2018-02-15 | 6500 |
| 0003 | Michael Brown | California | 2018-06-01 | 7200 |
| 0004 | Michelle Davis | Texas | 2019-12-20 | 5000 |
| 0005 | David Williams | New York | 2019-07-15 | 8000 |
Answer:
To answer this question, write the following SQL query:
This query filters the database for customer records that satisfy our conditions (State is either California or New York, total_purchase amount is above $5000, and they joined the platform after January 1, 2018). We then use the clause to collect these records by state, and function to calculate the average total purchase amount across customers from each state.
SQL Question 7: What is a foreign key?
A foreign key is a column or group of columns in a table that refers to the primary key in another table. The foreign key constraint helps maintain referential integrity between the two tables. The table with the foreign key is called the child table, while the table with the candidate key is called the parent or referenced table.
For example, consider a database with two tables: and . The Customers table might have a primary key column called , while the TreeHouse Foods orders table might have a foreign key column called that references the column in TreeHouse Foods customers table.
SQL Question 8: Average Quantity of Products Sold
TreeHouse Foods is a private label food and beverage leader, focused on customer brands and custom products. Let's assume that at TreeHouse Foods, sales data is stored in a SQL database. Your task is to determine the average quantity of each product sold per month.
Consider the following example input:
Example Input:
| sale_id | product_id | sale_date | quantity |
|---|---|---|---|
| 101 | A1 | 02/01/2022 | 300 |
| 102 | B1 | 02/15/2022 | 500 |
| 103 | A1 | 03/10/2022 | 400 |
| 104 | C1 | 03/22/2022 | 200 |
| 105 | B1 | 04/04/2022 | 600 |
| 106 | A1 | 04/18/2022 | 500 |
Your SQL query will return an output that shows the average quantity of each product sold per month:
Example Output:
| month | product_id | avg_quantity |
|---|---|---|
| 2 | A1 | 300 |
| 2 | B1 | 500 |
| 3 | A1 | 400 |
| 3 | C1 | 200 |
| 4 | A1 | 500 |
| 4 | B1 | 600 |
Answer:
In PostgreSQL, we can extract the month from a date using the function. Additionally, we can group by both the product id and this extracted month, allowing us to calculate the average quantity sold per product per month.
Here is a SQL query that solves this problem:
This query groups the sales data by both the month and the product id. For each group, it calculates the average quantity sold. It returns these grouped sales along with the average quantity sold within each group.
To practice a very similar question try this interactive Amazon Average Review Ratings Question which is similar for calculating average values grouped by a specific time period or this Alibaba Compressed Mean Question which is similar for calculating the mean of quantities.
SQL Question 9: Average Sales of Products
As an analyst at TreeHouse Foods, we are interested in understanding the performance of our different products. Using the sales data available to you, can you write an SQL query to find the average sales quantity of each product per month?
For this question, assume we have a table that includes , (number of units sold), and .
Example Input:
| transaction_id | transaction_date | product_id | quantity |
|---|---|---|---|
| 6541 | 06/12/2022 00:00:00 | 1001 | 40 |
| 6742 | 06/15/2022 00:00:00 | 1002 | 35 |
| 6783 | 06/20/2022 00:00:00 | 1001 | 20 |
| 6952 | 07/01/2022 00:00:00 | 1002 | 50 |
| 7027 | 07/07/2022 00:00:00 | 1001 | 30 |
Example Output:
| month | product_id | avg_quantity |
|---|---|---|
| 6 | 1001 | 30.00 |
| 6 | 1002 | 35.00 |
| 7 | 1001 | 30.00 |
| 7 | 1002 | 50.00 |
Answer:
This query works by using the aggregate function to calculate the average quantity sold (), while grouping the sales by and with the clause, after extracting the month from the using PostgreSQL's function. The clause is used to sort the resulting rows in order of month and product_id.
SQL Question 10: What's 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 TreeHouse Foods marketing campaigns data:
In this TreeHouse Foods 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 11: Find Customers Residing In Certain Cities
For this scenario, TreeHouse Foods wants to tailor their marketing efforts in cities where they have a significant customer presence. They would like a list of customers residing in cities beginning with the letter 'A'.
Suppose we have a table which tracks important customer information.
Example Input:
| customer_id | first_name | last_name | city | |
|---|---|---|---|---|
| 1 | John | Doe | johndoe@example.com | Austin |
| 2 | Jane | Doe | janedoe@example.com | Atlanta |
| 3 | Alice | Smith | alicesmith@example.com | San Francisco |
| 4 | Bob | Smith | bobsmith@example.com | Albany |
| 5 | Charlie | Brown | charliebrown@example.com | Austin |
TreeHouse Foods is interested in all customers that reside in cities beginning with 'A'.
Example Output:
| customer_id | first_name | last_name | city | |
|---|---|---|---|---|
| 1 | John | Doe | johndoe@example.com | Austin |
| 2 | Jane | Doe | janedoe@example.com | Atlanta |
| 4 | Bob | Smith | bobsmith@example.com | Albany |
Answer:
To answer this question we can filter the table by the column using the SQL keyword and a pattern that matches any string beginning with 'A':
This query first selects all records in the table, then filters those records down to only include rows where the starts with 'A'. The '%' is a wildcard that matches any sequence of characters, allowing us to catch cities that start with 'A' followed by any other characters.
Preparing For The TreeHouse Foods SQL Interview
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the TreeHouse Foods SQL interview is to solve as many practice SQL interview questions as you can! In addition to solving the above TreeHouse Foods SQL interview questions, you should also solve the 200+ DataLemur interview questions which come from companies like Amazon, JP Morgan, and food and facilities companies like TreeHouse Foods.
Each problem on DataLemur has multiple hints, step-by-step solutions and most importantly, there is an interactive coding environment so you can right online code up your SQL query answer and have it checked.
To prep for the TreeHouse Foods SQL interview it is also helpful to practice interview questions from other food and facilities companies like:
- Darling Ingredients SQL questions
- Sanderson Farms SQL questions
- Monster Beverage SQL questions
- Coca-Cola SQL questions
However, if your SQL coding skills are weak, don't worry about diving straight into solving questions – improve your SQL foundations with this DataLemur SQL Tutorial.
This tutorial covers topics including filtering on multiple conditions using AND/OR/NOT and CASE/WHEN statements – both of which come up frequently during TreeHouse Foods SQL interviews.
TreeHouse Foods Data Science Interview Tips
What Do TreeHouse Foods Data Science Interviews Cover?
Beyond writing SQL queries, the other question categories covered in the TreeHouse Foods Data Science Interview include:
- Probability & Stats Questions
- Python or R Programming Questions
- Product Data Science Interview Questions
- Machine Learning Questions
- Behavioral Interview Questions
How To Prepare for TreeHouse Foods Data Science Interviews?
To prepare for the TreeHouse Foods Data Science interview make sure you have a strong understanding of the company's values and mission – this will be clutch for 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 Microsoft, Amazon & startups
- A Crash Course on Python, SQL & ML
- Great Reviews (1000+ 5-star reviews on Amazon)