# 8 loanDepot SQL Interview Questions (Updated 2024)

Updated on

June 30, 2024

loanDepot employees write SQL queries for analyzing loan application data for risk assessment and generating customer trend reports to improve sales strategies. That's the reason behind why loanDepot covers SQL query questions during interviews for Data Analyst, Data Science, and BI jobs.

So, to help you prep, we've collected 8 loanDepot SQL interview questions – can you answer each one?

## 8 loanDepot SQL Interview Questions

### SQL Question 1: Identifying VIP Loan Customers

In this scenario, loanDepot would like to identify their 'VIP' or 'whale' customers. For the context of this business, let's say that a VIP customer is defined as a customer that has either taken out a large number (more than 10) of loans or has taken out loans that total to more than \$1,000,000.

Given a table and a table, write a SQL query that identifies any VIP customers.

##### Example Input:
customer_idnamejoin_date
001John Doe01/01/2020
002Jane Smith05/05/2019
003Mary Johnson12/12/2018
##### Example Input:
loan_idcustomer_idloan_dateloan_amount
000100101/01/2020100000
000200102/02/2020200000
000300203/03/2020300000
000400204/04/2020500000
000500305/05/2020600000

This PostgreSQL query joins the table with a subquery that aggregates the table by into a count of loans and a total loan amount per customer. The WHERE clause filters out any customers that do not fit the criteria of a VIP customer -- having total loan amount more than \$1,000,000 or more than 10 loans.

To work on a similar customer analytics SQL question where you can solve it interactively and have your SQL solution automatically checked, try this Walmart SQL Interview Question:

### SQL Question 2: Department Salaries

You're given a table of loanDepot 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.

You can solve this interview question interactively on DataLemur:

The answer is LONG – 30+ lines of SQL. You can find a detailed solution here: Department Salaries.

Make sure you stay up to date with recent press releases from LoanDepot!

### SQL Question 3: What does / 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 loanDepot should be lenient!).

For a tangible example in PostgreSQL, suppose you were doing an HR Analytics project for loanDepot, and had access to loanDepot's contractors and employees data. Assume that some employees were previously contractors, and vice versa, and thus would show up in both tables. You could use operator to find all contractors who never were a employee using this query:

### SQL Question 4: Calculate Average Interest Rate and Loan Amount Per Month Per Product

Given a dataset of loans with loanIds, productIds, loanAmounts, interestRates, and originatedDates. Write a SQL query to calculate the average loanAmount and interest rate per product per month.

Assume that the table has the following structure:

##### Example Input:
loanIdproductIdloanAmountinterestRateoriginatedDate
11001500007.52022-06-01
21002300006.22022-06-09
31001400007.82022-07-10
41002600006.02022-07-21
51001350007.62022-07-25

Your output should return the average loan amount and interest rate for each product () for each month. The output should be month (as 'yyyy-mm'), productId, average loanAmount, and average interestRate:

##### Example Output:
monthproductIdavg_loanAmountavg_interestRate
2022-061001500007.5
2022-061002300006.2
2022-071001375007.7
2022-071002600006.0

In this query, we're grouping our data by both the month-year date and productId, using the TO_CHAR function to convert the date to a 'YYYY-MM' format. This allows us to gather individual sets of data for each product for each month.

Next, we calculate the average loan amount and interest rate for each of these sets of data using the AVG function, which adds all values in a particular column and divides them by the number of values.

The ORDER BY clause is used to sort the output in ascending order, first by month and then by productId.

Pro Tip: Window functions are a frequent SQL interview topic, so practice every window function problem on DataLemur

### SQL Question 5: What does the SQL function do?

The function allows you to select the first non-NULL value from a list of values. It is often used to replace a NULL value with a default value in order to run aggregate functions like or on a column without being affected by values.

For example, if you conducted a customer satisfaction survey for a company and asked customers to rate their level of agreement with a statement on a scale of 1 to 10, some customers may not have answered all the questions, resulting in NULL values in the survey data:

customer_idquestion_idagree_amount
10119
101210
20216
2022NULL
30314
3032NULL

To replace the NULLs in the column with the default value of 5 which corresponds to neutral on the survey, you could run the following SQL query:

You'd get the following output:

customer_idquestion_idagree_amount
10119
101210
20216
20225
30314
30325

### SQL Question 6: Filter on Credit Score and Loan Status for LoanDepot Customers

Write a SQL query to extract records of customers from the loanDepot database who have a credit score above 680 and have either applied for a loan and been accepted, or have not applied for a loan.

We are only interested in those customers who joined the platform between 2020 and 2022.

##### Example Input:
customer_idregister_datecredit_scorehas_appliedloan_status
100101/28/2020690trueaccepted
100205/12/2019710truerejected
100307/16/2021650falsenull
100412/30/2022700falsenull
100503/15/2020680trueaccepted
##### Example Output:
customer_idregister_datecredit_scorehas_appliedloan_status
100101/28/2020690trueaccepted
100307/16/2021650falsenull
100412/30/2022700falsenull
100503/15/2020680trueaccepted

This query filters on three conditions:

1. The customer's credit score is above 680,
2. The customer either haven't applied for a loan or they have applied and been accepted,
3. The customer registered between the years 2020 and 2022.

This will exclude customers with a lower credit score, or who applied for a loan but were rejected, or who registered outside the specified date 2020-2022. These conditions are determined by the WHERE clause, which uses AND to combine multiple conditions, OR to allow for two possibilities (not applied or accepted), and BETWEEN to specify a range for the date.

### SQL Question 7: How is the constraint used in a database?

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 8: Calculating Click-through Rate for Loan Products

loanDepot wants to analyse its marketing campaign's efficiency by examining the click-through rates (CTR) of their loan products. The CTR is calculated as the number of users who clicked on an ad (ad_clicks) divided by the number of times the ad was shown (impressions), expressed as a percentage. You have two tables: , which includes ad_id, impressions, and product_id for each advertisement, and , which contains user_id, ad_id, and click_date for each interaction.

Using loanDepot's and , write a PostgreSQL query to calculate the CTR for each loan product.

11000A
22000A
31500B
42500C
##### Example Input:
1001106/08/2022
1002206/10/2022
1003106/16/2022
1004307/01/2022
1005207/03/2022

This query first creates an intermediate view where is joined with the count of clicks for each advertisement in using a left join. For each ad, it calculates the click-through rate and shows the final output in terms of product_id and click_through_rate.

To practice a similar problem about calculating rates, solve this TikTok SQL Interview Question on DataLemur's interactive SQL code editor:

### How To Prepare for the loanDepot SQL Interview

The best way to prepare for a loanDepot SQL interview is to practice, practice, practice. Besides solving the earlier loanDepot SQL interview questions, you should also solve the 200+ SQL exercises on DataLemur which come from companies like Microsoft, Google, and Facebook.

Each SQL question has multiple hints, step-by-step solutions and most importantly, there is an interactive SQL code editor so you can easily right in the browser your query and have it checked.

To prep for the loanDepot SQL interview it is also wise to practice SQL problems from other mortgage & loan companies like:

However, if your SQL skills are weak, forget about jumping right into solving questions – strengthen your SQL foundations with this free SQL for Data Analytics course.

This tutorial covers SQL concepts such as sorting data with ORDER BY and filtering strings using LIKE – both of these pop up often during SQL interviews at loanDepot.

### loanDepot Data Science Interview Tips

#### What Do loanDepot Data Science Interviews Cover?

In addition to SQL interview questions, the other question categories to prepare for the loanDepot Data Science Interview include:

#### How To Prepare for loanDepot Data Science Interviews?

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• 201 interview questions taken from tech companies like Netflix, Google, & Airbnb
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