9 U.S. Bancorp SQL Interview Questions (Updated 2024)
Data Scientists, Analysts, and Data Engineers at U.S. Bancorp write SQL queries daily as part of their job. They use SQL for extracting financial data to generate insights and managing databases to ensure smooth transactions and operations. That's why U.S. Bancorp asks interviewees SQL coding interview questions.
So, to help you ace the U.S. Bancorp SQL interview, here’s 9 U.S. Bancorp SQL interview questions in this blog.
9 U.S. Bancorp SQL Interview Questions
SQL Question 1: Determine Whale Customers at "U.S. Bancorp"
For U.S. Bancorp, a major financial services company, an example of a 'whale' user might be a customer who is frequently transferring huge sum of money. Let's say, the company considers users who transfer an average of more than $10,000 per transaction in the last month, as VIP users.
Write a SQL query to identify all VIP users for the past month.
Example Input:
| transaction_id | customer_id | transaction_date | amount |
|---|---|---|---|
| 4582 | 101 | 08/15/2022 | 14000.00 |
| 7351 | 102 | 08/19/2022 | 6500.00 |
| 9837 | 103 | 08/12/2022 | 11000.00 |
| 6528 | 101 | 08/28/2022 | 16000.00 |
| 3475 | 104 | 08/30/2022 | 8000.00 |
Example Input:
| customer_id | name |
|---|---|
| 101 | John Doe |
| 102 | Jane Smith |
| 103 | Jim Brown |
| 104 | Jack Danny |
Answer:
The above SQL query joins the transaction and customer tables, and then filters for transactions that were made in the past month. For each customer, it calculates the average transaction amount and filters to only include customers with an average transaction amount of more than $10,000 - these are our 'whale' customers for the past month.
To practice a related customer analytics question on DataLemur's free interactive coding environment, try this recently asked Microsoft SQL interview question:
SQL Question 2: Top 3 Department Salaries
Given a table of U.S. Bancorp employee salary information, write a SQL query to find the top 3 highest earning employees within each department.
U.S. Bancorp Example Input:
| employee_id | name | salary | department_id |
|---|---|---|---|
| 1 | Emma Thompson | 3800 | 1 |
| 2 | Daniel Rodriguez | 2230 | 1 |
| 3 | Olivia Smith | 2000 | 1 |
| 4 | Noah Johnson | 6800 | 2 |
| 5 | Sophia Martinez | 1750 | 1 |
| 8 | William Davis | 6800 | 2 |
| 10 | James Anderson | 4000 | 1 |
Example Input:
| department_id | department_name |
|---|---|
| 1 | Data Analytics |
| 2 | Data Science |
Example Output:
| department_name | name | salary |
|---|---|---|
| Data Analytics | James Anderson | 4000 |
| Data Analytics | Emma Thompson | 3800 |
| Data Analytics | Daniel Rodriguez | 2230 |
| Data Science | Noah Johnson | 6800 |
| Data Science | William Davis | 6800 |
Write a SQL query for this interview question directly within the browser on DataLemur:
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 detailed solution here: Top 3 Department Salaries.
SQL Question 3: What do the SQL commands / do?
The / operator is used to remove to return all rows from the first SELECT statement that are not returned by the second SELECT statement.
Note that is available in PostgreSQL and SQL Server, while MINUS is available in MySQL and Oracle (but don't stress about knowing which DBMS supports what exact commands since the interviewers at U.S. Bancorp should be lenient!).
Here's a PostgreSQL example of using EXCEPT to find all of U.S. Bancorp's Facebook video ads with more than 10k views that aren't also being run on YouTube:
If you want to retain duplicates, you can use the EXCEPT ALL operator instead of EXCEPT. The EXCEPT ALL operator will return all rows, including duplicates.
SQL Question 4: Evaluate Average Transaction Amount by Month for Each Product
You are provided with a sample of transaction records from U.S. Bancorp. Each record includes the , , , , and . Write a SQL query to calculate the average monthly transaction amount per product.
Use partition by and over clauses in window function to solve this problem.
Assuming the transaction data is stored in the table, structured as follows:
Example Input:
| transaction_id | customer_id | transaction_date | product_id | transaction_amount |
|---|---|---|---|---|
| 1001 | 100 | 06/08/2022 00:00:00 | 2001 | 2500 |
| 1002 | 200 | 06/10/2022 00:00:00 | 3001 | 3000 |
| 1003 | 300 | 06/10/2022 00:00:00 | 3001 | 3500 |
| 1004 | 400 | 07/02/2022 00:00:00 | 2001 | 4000 |
| 1005 | 500 | 07/05/2022 00:00:00 | 3001 | 3000 |
Your answer would be useful for the bank to analyze the product performance on a monthly basis.
Answer:
This query first extracts the month from the transaction_date using the function. It then calculates the average transaction amount for each product for every month using the function as a window function. The window is defined by partitioning the data by month and product_id. The result is a table listing each month, product_id, and the corresponding average transaction amount.
Pro Tip: Window functions are a frequent SQL interview topic, so practice all the window function problems on DataLemur
SQL Question 5: What is the purpose of a primary key in a database?
The primary key of a table is a column or set of columns that serves as a unique identifier for each row. It ensures that all rows are distinct and does not allow null values.
For example, say you had stored some Facebook ad campaign data that U.S. Bancorp ran:
The CampaignID column is used to uniquely identify each row in the table, and the constraint ensures that there are no duplicate CampaignID values. This helps to maintain the accuracy of the data by preventing duplicate rows. The primary key is also an important part of the table because it enables you to easily identify and reference specific campaigns in your Facebook Ad data. You can use it to join to other tables in the database, such as a table with data on the results of the campaigns.
SQL Question 6: Average Transaction Amount
As a Business Analyst at U.S. Bancorp, you are tasked to find out the average transaction amount of different types of transactions (Deposit, Withdraw, Transfer, etc.) per month for the year 2022.
For this question, let's assume a table with the following structure:
Example Input:
| transaction_id | customer_id | transaction_date | transaction_type | amount |
|---|---|---|---|---|
| 7894 | 90 | 02/11/2022 00:00:00 | Deposit | 8000 |
| 2659 | 32 | 02/23/2022 00:00:00 | Withdraw | 1500 |
| 3567 | 786 | 05/05/2022 00:00:00 | Deposit | 4500 |
| 4581 | 12 | 05/15/2022 00:00:00 | Transfer | 2000 |
| 3198 | 14 | 05/15/2022 00:00:00 | Transfer | 1000 |
You are required to write a SQL query using the AVG function to find this information.
Answer:
Here is a PostgreSQL query that will answer the above problem:
This query first extracts the month and year from the transaction date. It then groups the data by and and calculates the average of the transaction for each group. The clause is used to filter data to include only transactions from the year 2022. Finally, the result is ordered in descending order by .
To practice a very similar question try this interactive Uber User's Third Transaction Question which is similar for working with transaction data or this Stripe Repeated Payments Question which is similar for analyzing transaction amounts and timeframes.
SQL Question 7: When would you use the function?
The COALESCE() function can be used to replace NULL values with a specified value. For instance, if you are a Data Analyst at a company and are working on a customer analytics project to identify the most engaged customers, you may have access to the following data in the table:
| customer_id | email_engagement | sms_engagement |
|---|---|---|
| 101 | moderately_active | not_opted_in |
| 201 | un-subscribed | NULL |
| 301 | NULL | not_opted_in |
| 401 | not_active | very_active |
| 501 | very_active | very_active |
| 601 | NULL | NULL |
Before you could procede with your analysis, you would need to remove the NULLs, and replace them with the default value for email engagement (not_active), and the default sms_engagement level (not_opted_in).
To do this, you'd run the following query:
This would get you the following output:
| customer_id | email_engagement | sms_engagement |
|---|---|---|
| 101 | moderately_active | not_opted_in |
| 102 | un-subscribed | not_opted_in |
| 103 | not_active | not_opted_in |
| 104 | not_active | very_active |
| 105 | very_active | very_active |
| 106 | not_active | not_opted_in |
SQL Question 8: Calculate Click-through-rates for U.S. Bancorp digital marketing campaign
As part of your role at U.S. Bancorp, you are tasked with analyzing the success of a recent digital marketing campaign. Specifically, management wants to understand the click-through rates for ads that were posted on various platforms.
Assume that for each ad, you know how many times it was shown (impressions) and how many times it was clicked (clicks). An ad's click-through rate (CTR) is defined as (clicks/impressions)*100. Write a SQL query to calculate the CTR for each ad.
Here are the tables provided and the corresponding data:
Example Input:
| ad_id | platform | impressions | clicks |
|---|---|---|---|
| 101 | 5000 | 250 | |
| 102 | 4000 | 200 | |
| 103 | 6000 | 300 | |
| 104 | 7000 | 350 | |
| 105 | 5500 | 275 |
Now, write a PostgreSQL query to return a table with the ad_id and its corresponding CTR, ordered by CTR in descending order.
Answer:
You can use the following PostgreSQL query:
This query calculates the CTR for each ad by dividing the number of clicks by impressions and multiplying by 100. The results are then ordered in descending order of CTR to get the ad with the highest CTR at the top of the output. The *100.0 ensures floating point division, which is important to get accurate CTR values.
To practice a similar SQL problem on DataLemur's free interactive SQL code editor, attempt this Facebook SQL Interview question:
Check out the US Bancorp career page and see what type of role might be the best fit for you!
SQL Question 9: Retrieve Customer and Account Details
As a data analyst at U.S. Bancorp, you are asked to get the details for customers and their respective account details with the total dollar amount in each account. The tables and contains the necessary data.
The table has columns , and .
The table has , , and .
Write a SQL query to join these tables and present the data in the following format: , , and .
Example Input:
| customer_id | customer_name | address |
|---|---|---|
| 1 | John Doe | 123 Street, City, State |
| 2 | Jane Smith | 456 Lane, City, State |
| 3 | Steve Davis | 789 Road, City, State |
Example Input:
| account_id | customer_id | total_dollar_amount | account_type |
|---|---|---|---|
| 111 | 1 | 10000 | Savings |
| 222 | 2 | 20000 | Checking |
| 333 | 1 | 30000 | Checking |
| 444 | 3 | 40000 | Savings |
Answer:
In this query, we're using an inner join to combine the table ('c') and the table ('a') based on matching values. The specific columns , , and are then selected from the combined table.
Since joins come up routinely during SQL interviews, take a stab at this interactive Snapchat Join SQL question:
How To Prepare for the U.S. Bancorp SQL Interview
The key to acing a U.S. Bancorp SQL interview is to practice, practice, and then practice some more!
Besides solving the above U.S. Bancorp SQL interview questions, you should also solve the 200+ SQL coding questions which come from companies like FAANG (FB, Apple, Amazon, Netflix, Google).
Each DataLemur SQL question has multiple hints, fully explained answers along with a discussion board to see how others solved it and crucially, there's an online SQL code editor so you can instantly run your SQL query answer and have it executed.
To prep for the U.S. Bancorp SQL interview it is also useful to practice SQL problems from other banking & finanacial services companies like:
In case your SQL foundations are weak, don't worry about jumping right into solving questions – improve your SQL foundations with this SQL tutorial for Data Scientists & Analysts.
This tutorial covers things like SUM/AVG window functions and ordering data – both of these show up routinely during U.S. Bancorp SQL assessments.
U.S. Bancorp Data Science Interview Tips
What Do U.S. Bancorp Data Science Interviews Cover?
Besides SQL interview questions, the other types of questions to practice for the U.S. Bancorp Data Science Interview are:
- Statistics and Probability Questions
- Python or R Coding Questions
- Product & Business Analytics Questions
- ML Modelling Questions
- Resume-Based Behavioral Questions
How To Prepare for U.S. Bancorp Data Science Interviews?
I'm a bit biased, but I think the optimal way to study for U.S. Bancorp Data Science interviews is to read my book Ace the Data Science Interview.
The book solves 201 data interview questions sourced from FAANG tech companies. It also has a crash course on Stats, SQL & ML. And finally it's helped thousands of people land their dream job in data, which is why it's got over 1000+ 5-star reviews on Amazon.