9 US Foods SQL Interview Questions (Updated 2024)
Data Analytics, Data Science, and Data Engineering employees at US Foods use SQL queries to analyze sales trends and accurately predict demand, making sure they have the right products available when needed. They also use SQL to manage supply chain data, which helps them keep things running smoothly and track products from producer to customer, which is why US Foods often asks SQL coding questions in interviews.
So, to help you prep, we've curated 9 US Foods SQL interview questions – able to solve them?
9 US Foods SQL Interview Questions
SQL Question 1: Identify Frequent and High-Ordering Customers at US Foods
US Foods wishes to identify their power users, specifically, they want to see the customers that frequently make high-value orders. A high-value order is defined as an order with a total cost greater than $1000. They want to see a list of customers, marked as 'VIP' who have placed at least 3 high-value orders in the last 30 days.
For simplicity, assume we have access to a table and an table with the following structures:
Example Input:
| customer_id | name | location |
|---|---|---|
| 1001 | John Doe | Chicago |
| 1002 | Jane Smith | New York |
| 1003 | Bob Johnson | Los Angeles |
| 1004 | Alice Williams | Houston |
| 1005 | Charlie Brown | Miami |
Example Input:
| order_id | customer_id | order_date | total_cost |
|---|---|---|---|
| 2001 | 1001 | 2022-09-01 | 2500 |
| 2002 | 1002 | 2022-09-05 | 800 |
| 2003 | 1001 | 2022-09-08 | 1050 |
| 2004 | 1002 | 2022-09-15 | 950 |
| 2005 | 1001 | 2022-09-20 | 1200 |
Answer:
In PostgreSQL, construct a query to retrieve the required information from the table:
This query will return a list of all customers, their , , and a new column called which marks customers as 'VIP' if they have made 3 or more high-value orders in the last 30 days. If not, they are marked as 'Regular'.
Please note that returns the date 30 days ago from the current date. Here we are assuming that the column in the orders table is of type 'date' or 'datetime'.
To practice a similar VIP customer analysis question on DataLemur's free interactive coding environment, try this Microsoft SQL Interview problem:
Dive into US Foods' press releases to uncover the latest news and initiatives that are driving their success in the food service industry! Keeping an eye on US Foods' updates can help you grasp the evolving dynamics of food distribution and service.
SQL Question 2: Employees Earning More Than Managers
Imagine there was a table of US Foods employee salaries. Write a SQL query to find all employees who earn more than their direct manager.
US Foods Example Input:
| employee_id | name | salary | department_id | manager_id |
|---|---|---|---|---|
| 1 | Emma Thompson | 3800 | 1 | |
| 2 | Daniel Rodriguez | 2230 | 1 | 10 |
| 3 | Olivia Smith | 8000 | 1 | 8 |
| 4 | Noah Johnson | 6800 | 2 | 8 |
| 5 | Sophia Martinez | 1750 | 1 | 10 |
| 8 | William Davis | 7000 | 2 | NULL |
| 10 | James Anderson | 4000 | 1 | NULL |
Example Output:
| employee_id | employee_name |
|---|---|
| 3 | Olivia Smith |
This is the output because Olivia Smith earns $8,000, surpassing her manager, William Davis who earns 7,800.
Try this problem directly within the browser on DataLemur:
Answer:
First, we perform a SELF-JOIN where we treat the first table () as the managers' table and the second table () as the employees' table. Then we use a clause to filter the results, ensuring we only get employees whose salaries are higher than their manager's salary.
If the solution above is tough, you can find a detailed solution here: Employees Earning More Than Their Boss.
SQL Question 3: In SQL, are blank spaces the same as a NULL?
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 4: Calculate the Total Order Quantity Per Category
As a Data Analyst at US Foods, your manager has given you access to a dataset that shows all orders in a certain period. Your task is to write a SQL query that calculates the total order quantity for each product category over the given period. You will use a window function in your query.
In US Foods, each product belongs to a product category, and each order includes the ID of the customer who made the order, the ID of the Product, the Order Date and the Quantity of the Product.
:
| product_id | category_id | product_name |
|---|---|---|
| 345 | 100 | Apple Juice |
| 346 | 101 | Banana |
| 347 | 102 | Meat |
| 348 | 100 | Orange Juice |
| 349 | 103 | Bakery |
:
| order_id | customer_id | product_id | order_date | quantity |
|---|---|---|---|---|
| 1201 | 50 | 345 | 2023-03-01 | 10 |
| 1202 | 51 | 346 | 2023-03-01 | 20 |
| 1203 | 52 | 347 | 2023-03-02 | 5 |
| 1204 | 53 | 348 | 2023-03-03 | 15 |
| 1205 | 54 | 349 | 2023-03-03 | 12 |
Here's a query that computes the total quantity of products ordered for each category using PostgreSQL:
This query first joins the and tables on . It then uses the window function to compute the total quantity for each product category (as identified by ). The result will be a list of product categories by the total quantities of products ordered in each category over the given time period.
Finally, the results are ordered by to provide data in a structured and ordered format.
Pro Tip: Window functions are a popular SQL interview topic, so practice all the window function problems on DataLemur
SQL Question 5: Can you explain the purpose of the constraint?
A is a field in a table that references the of another table. It creates a link between the two tables and ensures that the data in the field is valid.
For example, if you have a table of US Foods customers and an orders table, the customer_id column in the orders table could be a that references the id column (which is the primary key) in the US Foods customers table.
The constraint helps maintain the integrity of the data in the database by preventing the insertion of rows in the table that do not have corresponding entries in the table. It also enforces the relationship between the two tables and prevents data from being deleted from the table if it is still being referenced in the table.
SQL Question 6: Finding the Average Quantity per Order
As a Data Analyst at US Foods, you are asked to find out the average quantity per order of each product for a certain time period. For this, you are given the table and the table.
Example Input:
| order_id | product_id | order_date | quantity |
|---|---|---|---|
| 1001 | 201 | 06/08/2022 | 10 |
| 1002 | 302 | 06/10/2022 | 12 |
| 1003 | 201 | 06/18/2022 | 15 |
| 1004 | 302 | 07/26/2022 | 8 |
| 1005 | 401 | 07/05/2022 | 20 |
Example Input:
| product_id | product_name | category |
|---|---|---|
| 201 | Apple | Fruits |
| 302 | Milk | Dairy |
| 401 | Fish | Seafood |
Answer:
This PostgreSQL query joins the and table using the as the key. It then groups the data by and calculates the average quantity per order for each product within the specified date range.
To practice a very similar question try this interactive Alibaba Compressed Mean Question which is similar for calculating averages for orders or this Wayfair Y-on-Y Growth Rate Question which is similar for analyzing data over a certain time period.
SQL Question 7: What distinguishes an inner join from a full outer join?
An inner join only includes rows from both tables that satisfy the join condition, whereas a full outer join includes all rows from both tables, regardless of whether they match the join condition or not.
For a concrete example, 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 US Foods sales table, which has data on how many products were sold and which Google Ads keyword drove that sale.
: retrieves rows from both tables where there is a match in the shared key or keys. For example, an INNER JOIN between the table and the table could be performed using the keyword column as the shared key. This would retrieve only the rows where the keyword in the table matches the keyword in the table.
: retrieves all rows from both tables, regardless of whether there is a match in the shared key or keys. If there is no match, values will be returned for the columns of the non-matching table.
SQL Question 8: Find the average quantity of each product sold per month
Given the US Foods database has a table with details about each sale made, can you write a query to get the average quantity of each product sold per month?
The table has the following fields:
- is the unique identifier for each sale
- is the date when the sale was made
- is the unique identifier for the product
- is the number of products sold in the sale
Example Input:
| sale_id | sale_date | product_id | quantity |
|---|---|---|---|
| 321 | 01/15/2022 00:00:00 | 7008 | 5 |
| 543 | 01/20/2022 00:00:00 | 7008 | 2 |
| 654 | 02/05/2022 00:00:00 | 7008 | 4 |
| 721 | 02/25/2022 00:00:00 | 7815 | 3 |
| 832 | 03/05/2022 00:00:00 | 7815 | 6 |
We want to write a query that groups by the product and month and then calculates the average quantity sold for each group.
For example, for product 7008, there were 2 sales in January with a total quantity of 7, so the average quantity sold in January is 3.5 (7/2).
Example Output:
| month | product | avg_quantity |
|---|---|---|
| 1 | 7008 | 3.5 |
| 2 | 7008 | 4.0 |
| 2 | 7815 | 3.0 |
| 3 | 7815 | 6.0 |
Answer:
This query extracts the month from the field, then groups by and . For each group, it computes the average of . The result is sorted by and .
SQL Question 9: Filter Customers in Specific States
As a data analyst at US Foods, you are asked to provide a list of customers who are located in states starting with the letter 'N'. Use the table to accomplish this task.
The table is represented as follows:
Example Input:
| customer_id | first_name | last_name | state | |
|---|---|---|---|---|
| 1001 | John | Doe | john.doe@example.com | New York |
| 1002 | Jane | Smith | jane.smith@example.com | Texas |
| 1003 | Michael | Johnson | michael.johnson@example.com | Nevada |
| 1004 | Emily | Davis | emily.davis@example.com | Alaska |
| 1005 | Daniel | Brown | daniel.brown@example.com | New Jersey |
Your task is to write an SQL query to filter out the details of customers who are located in states starting with an 'N'.
Answer:
This query would return the following results:
Example Output:
| customer_id | first_name | last_name | state | |
|---|---|---|---|---|
| 1001 | John | Doe | john.doe@example.com | New York |
| 1003 | Michael | Johnson | michael.johnson@example.com | Nevada |
| 1005 | Daniel | Brown | daniel.brown@example.com | New Jersey |
This query uses the LIKE keyword in SQL to filter the column where the name of the state starts with 'N'. The character is a wildcard that matches any sequence of characters. So, matches any string that starts with 'N'.
US Foods SQL Interview Tips
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the US Foods SQL interview is to solve as many practice SQL interview questions as you can! Beyond just solving the earlier US Foods SQL interview questions, you should also solve the 200+ tricky sql questions on DataLemur which come from companies like FAANG tech companies and tech startups.
Each problem on DataLemur has multiple hints, full answers and best of all, there's an online SQL code editor so you can right in the browser run your SQL query and have it executed.
To prep for the US Foods SQL interview it is also a great idea to practice SQL problems from other food and facilities companies like:
- Performance Food Group SQL questions
- Sysco SQL questions
- Peloton SQL questions
- SpartanNash SQL questions
In case your SQL foundations are weak, don't worry about jumping right into solving questions – go learn SQL with this SQL tutorial for Data Scientists & Analysts.
This tutorial covers SQL topics like creating summary stats with GROUP BY and finding NULLs – both of which come up routinely during US Foods interviews.
US Foods Data Science Interview Tips
What Do US Foods Data Science Interviews Cover?
In addition to SQL query questions, the other types of questions to practice for the US Foods Data Science Interview are:
- Statistics & Probability Questions
- Python or R Coding Questions
- Data Case Study Questions
- Machine Learning Questions
- Resume-Based Behavioral Questions
How To Prepare for US Foods Data Science Interviews?
To prepare for the US Foods Data Science interview make sure you have a deep understanding of the company's values and company principles – this will be clutch for acing the behavioral interview. For technical interviews prepare by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from Facebook, Google, & Amazon
- A Crash Course covering Stats, ML, & Data Case Studies
- Amazing Reviews (1000+ 5-star reviews on Amazon)