11 Mr. Cooper Group SQL Interview Questions (Updated 2024)
Mr. Cooper Group employees use SQL all the time for analyzing mortgage and loan data, and predicting payment delinquencies. That's the reason behind why Mr. Cooper Group almost always evaluates jobseekers on SQL query questions during interviews for Data Analyst, Data Science, and BI jobs.
Thus, to help you practice for the Mr. Cooper Group SQL interview, we've curated 11 Mr. Cooper Group SQL interview questions – scroll down to start solving them!
11 Mr. Cooper Group SQL Interview Questions
SQL Question 1: Identify the VIP customers
Mr. Cooper Group is a mortgage loan company. They want to identify their top, or 'VIP', customers. A VIP customer is defined as a customer who has taken more than 10 loans in a year and whose average loan amount is over $100,000. Write a SQL statement to identify these VIP customers.
Assume we have two tables, and
Example Input:
Example Input:
Answer:
Assuming PostgreSQL syntax:
The inner query selects the customer ID, the number of loans, and the average loan amount of each customer for year 2021, grouping by the customer ID. The results are placed in a table 't'.
The outer query then joins this table with the customer table on the customer ID. It filters rows based on the condition of having more than 10 loans and an average loan amount over 100,000 is the given condition for a VIP customer.
To practice a related super-user data analysis question on DataLemur's free interactive SQL code editor, try this recently asked Microsoft SQL interview question:
SQL Question 2: Second Highest Salary
Given a table of Mr. Cooper Group employee salaries, write a SQL query to find the 2nd highest salary amongst all the employees.
Mr. Cooper Group 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 here: 2nd Highest Salary.
SQL Question 3: What is a database index, and what are the different types of indexes?
An index in a database is a data structure that helps to quickly find and access specific records in a table.
For example, if you had a database of Mr. Cooper Group customers, you could create a primary index on the column.
Having a primary index on the column can speed up performance in several ways. For example, if you want to retrieve a specific customer record based on their , the database can use the primary index to quickly locate and retrieve the desired record. The primary index acts like a map, allowing the database to quickly find the location of the desired record without having to search through the entire table.
Additionally, a primary index can also be used to enforce the uniqueness of the column, ensuring that no duplicate values are inserted into the table. This can help to prevent errors and maintain the integrity of the data in the table.
SQL Question 4: Analyze Loan Performance
Suppose that you work in the analytics team for Mr. Cooper Group, a leader in the home loan industry. Your manager just handed you a database that contains information about loans and payments. Your task is to write a query that calculates the total amount of unpaid principal per region, per month, over the last 12 months.
For each region and month, you want to see the total original loan amount, the total amount of payments, and the remaining unpaid principal (original loan amount - total payments).
You should use a SQL window function to compute the remaining principal which must be done by keeping track of all the previous payments up to the current month for each loan and statistic by month on each region.
The data is stored in two tables:
- table which each row represents a loan.
- table which each row represents a payment for a specific loan.
Please note that some loans may not have payments.
Here are the tables:
Example Input
| loan_id | region | customer_id | loan_amnt | loan_date |
|---|---|---|---|---|
| 1 | West | 1001 | 500000 | 2021-01-05 |
| 2 | East | 1002 | 400000 | 2021-02-15 |
| 3 | West | 1003 | 600000 | 2021-03-10 |
| 4 | South | 1005 | 700000 | 2021-02-25 |
Example Input
| payment_id | loan_id | payment_amnt | payment_date |
|---|---|---|---|
| 1 | 1 | 50000 | 2021-02-05 |
| 2 | 1 | 40000 | 2021-02-19 |
| 3 | 2 | 30000 | 2021-03-20 |
| 4 | 2 | 60000 | 2021-04-15 |
| 5 | 3 | 50000 | 2021-04-21 |
Answer:
This query uses window function is to compute the running total of payments for each loan. The ensures we include loans that have no payments in the result. The and clauses are used to calculate aggregates per region, per month and to limit the calculation to the last 12 months respectively. The remaining principal is obtained by subtracting the total of all previous payments from the original loan amount.
Pro Tip: Window functions are a frequent SQL interview topic, so practice every window function problem on DataLemur
SQL Question 5: Why would you use the SQL constraint?
The constraint is used to specify a condition that the data in a column must meet. If a row is inserted or updated and the data in the column doesn't meet the condition specified by the CHECK constraint, the operation will sadly fail.
For example, you might use a constraint to ensure that a column contains only positive numbers, or that a date is within a certain range.
For example, if you had a table of Mr. Cooper Group employees, here's an example of how to use the CHECK constraint in a CREATE TABLE statement:
SQL Question 6: Retrieve customers based on received services
The customer service team of Mr. Cooper Group wants to conduct a survey for the customers who have taken a home loan and home insurance service in the last year (2021). They want the list of all customers who have taken both these services (not just one of them) sorted in ascending order by the customer name for better readability. The required fields are customer id, customer name, email, and phone number.
Below are the tables for the , and data.
Example Input:
| customer_id | name | phone_number | |
|---|---|---|---|
| 101 | John Doe | johndoe@example.com | (123) 456-7890 |
| 102 | Jane Smith | janesmith@example.com | (456) 789-0123 |
| 103 | Robert Johnson | rjohnson@example.com | (789) 012-3456 |
Example Input:
| loan_id | customer_id | loan_date |
|---|---|---|
| 3051 | 101 | 05/08/2021 |
| 4025 | 102 | 11/02/2021 |
| 7894 | 103 | 15/06/2020 |
Example Input:
| insurance_id | customer_id | start_date |
|---|---|---|
| 6171 | 101 | 01/01/2021 |
| 7802 | 103 | 12/12/2019 |
| 5293 | 102 | 10/02/2021 |
Answer:
This query first joins the table with and tables using inner join on , this way we get only those customers who have both home loan and home insurance. Then it filters out customers who had these services in 2021 using the clause along with . Finally, it sorts by the customer name in ascending order using .
BTW Mr. Cooper Group was recently named a best company to work for in India!
SQL Question 7: What's the difference between a unique index and non-unique index?
While both types of indexes improve the performance of SQL queries by providing a faster way to lookup rows of data, a unique index enforces the uniqueness of the indexed columns while a non-unique index allows duplicate values in the indexed columns.
Suppose you had a table of Mr. Cooper Group employees. Here's an example of a unique index on the column:
This index would ensure that no two Mr. Cooper Group employees have the same , which could be used as a unique identifier for each employee.
Here's a non-unique index example example on the column:
This index would not enforce uniqueness, but it could be used to improve the performance of queries that filter or sort the data based on the column. For example, if you want to quicklly retreive all Data Scientists, the database can use the index to efficiently locate and retrieve the desired records without having to do a full table scan on all Mr. Cooper Group employees.
SQL Question 8: Calculate the Average Loan Amount per State
Mr. Cooper Group, a mortgage loan company, is looking to analyze their loans' data. They are particularly interested in understanding the average loan amount issued in each state to help with their business planning and strategy. Write a SQL query to find the average loan amount per state.
Given the table structured as:
Example Input:
| loan_id | client_id | state | issue_date | loan_amount |
|---|---|---|---|---|
| 981 | 254 | Texas | 01/08/2020 | 150000 |
| 874 | 314 | California | 02/12/2020 | 200000 |
| 651 | 198 | Texas | 03/15/2020 | 180000 |
| 569 | 761 | Florida | 03/20/2020 | 120000 |
| 345 | 564 | Texas | 06/26/2020 | 300000 |
Your task is to generate an output that shows the average loan amount for each state. For example:
Example Output:
| state | avg_loan_amount |
|---|---|
| Texas | 210000 |
| California | 200000 |
| Florida | 120000 |
Answer:
This query calculates the average loan amount for each different state in the loans table. It uses the function to calculate the average and the clause to group the results by state. The keyword is used to rename the average column for clarity.
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for calculating top items per category or this Amazon Average Review Ratings Question which is similar for calculating averages for a specific group.
SQL Question 9: Calculate the Click-Through Rates for Advertisements
Mr. Cooper Group is a Mortgage company and like aforementioned companies, they utilize digital ads to attract potential buyers. Mr. Cooper runs several advertisements on various online platforms to attain leads. They want to get insights into their click-through rates, from how often each ad was displayed ('impressions') to how many times an ad was clicked ('clicks'). To cater to this, as a data analyst, you're given a table named structured as follows:
Example Input:
| ad_id | display_date | impressions | clicks |
|---|---|---|---|
| 101 | 07/01/2022 | 2300 | 120 |
| 102 | 07/01/2022 | 3100 | 290 |
| 103 | 07/02/2022 | 1500 | 85 |
| 104 | 07/02/2022 | 2700 | 143 |
| 105 | 07/03/2022 | 1900 | 100 |
Your task is to write a SQL query that calculates the click-through rate (CTR) for each ad, which can be calculated as (clicks/impressions) * 100. The output should present ad_id, display_date, and CTR (rounded to 2 decimal places).
Answer:
You can solve the problem by calculating the ratio of clicks to impressions for each ad:
Example Output:
| ad_id | display_date | ctr |
|---|---|---|
| 101 | 07/01/2022 | 5.22 |
| 102 | 07/01/2022 | 9.35 |
| 103 | 07/02/2022 | 5.67 |
| 104 | 07/02/2022 | 5.30 |
| 105 | 07/03/2022 | 5.26 |
In the output, ctr is calculated as (clicks/impressions) * 100 and rounded to 2 decimal places. The columns are ad_id, display_date, and ctr which represent Ad id, date ad was displayed, and calculated click-through rate of the ad respectively.
To practice a similar problem about calculating rates, solve this TikTok SQL question within DataLemur's interactive coding environment:
SQL Question 10: What are the similarities and differences between correleated and non-correlated sub-queries?
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. On the other hand, 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.
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.
Here is an example of a correlated sub-query:
This query selects the and total sales of all Mr. Cooper Group 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 ().
Non-correlated sub-queries are typically faster, as they only have to be executed once, wheareas correlated sub-queries are slower since they have to be re-evaluated for each row of the outer query.
SQL Question 11: Get the Average Loan Amount per Loan Type for Each Year
As a data analyst at Mr. Cooper Group, a mortgage company, your manager would like to understand the trends in loan types that customers are choosing. He asks you to write an SQL query to determine the average loan amount per loan type for each year the loan was issued.
Example Input:
| loan_id | customer_id | loan_type | loan_amount | loan_issue_date |
|---|---|---|---|---|
| 789 | 123 | Home Equity | 250000 | 01/02/2020 |
| 456 | 456 | Refinance | 300000 | 02/03/2021 |
| 123 | 789 | New Purchase | 450000 | 29/04/2020 |
| 321 | 321 | Refinance | 500000 | 05/06/2021 |
| 654 | 654 | Home Equity | 200000 | 06/07/2020 |
Example Output:
| Year | Loan Type | Average Loan Amount |
|---|---|---|
| 2020 | Home Equity | 225000 |
| 2020 | New Purchase | 450000 |
| 2021 | Refinance | 400000 |
Answer:
This SQL query uses the clause to group the data by the year of the loan issue date and the loan type. The function is used to obtain the year from the loan issue date. The function is then applied to find the average loan amount for each group.
Mr. Cooper Group SQL Interview Tips
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Mr. Cooper Group SQL interview is to solve as many practice SQL interview questions as you can!
Beyond just solving the above Mr. Cooper Group SQL interview questions, you should also solve the 200+ DataLemur interview questions which come from companies like Netflix, Airbnb, and Amazon.
Each SQL question has hints to guide you, fully explained answers along with a discussion board to see how others solved it and crucially, there is an online SQL code editor so you can easily right in the browser your query and have it checked.
To prep for the Mr. Cooper Group SQL interview it is also useful to practice SQL problems from other mortgage & loan companies like:
However, if your SQL foundations are weak, don't worry about diving straight into solving questions – improve your SQL foundations with this interactive SQL tutorial.
This tutorial covers SQL topics like inner vs. outer JOIN and handling date/timestamp data – both of which show up often during Mr. Cooper Group interviews.
Mr. Cooper Group Data Science Interview Tips
What Do Mr. Cooper Group Data Science Interviews Cover?
Besides SQL interview questions, the other question categories covered in the Mr. Cooper Group Data Science Interview are:
- Probability & Stats Questions
- Python or R Programming Questions
- Product Data Science Interview Questions
- ML Modelling Questions
- Resume-Based Behavioral Questions
How To Prepare for Mr. Cooper Group Data Science Interviews?
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