11 MeridianLink SQL Interview Questions (Updated 2024)
MeridianLink employees write SQL queries often for analyzing customer behavior to optimize loan processing times and manipulating financial databases for developing effective risk assessment models. So, it shouldn't surprise you that MeridianLink often tests SQL query questions in interviews for Data Analyst, Data Science, and BI jobs.
Thus, to help you ace the MeridianLink SQL interview, here’s 11 MeridianLink SQL interview questions in this blog.
11 MeridianLink SQL Interview Questions
SQL Question 1: Identify the Top Customers at MeridianLink
MeridianLink, a software company that specializes in loan origination, credit reporting, and business consulting services, wants to identify its top customers. These "VIP" or "whale" users are considered as such due to their high frequency of loans and consulting services. The assessment is based on the number of loan applications a user has created over a given period.
Assuming all transactions are logged within a table with the following structure:
Example Input:
| transaction_id | user_id | transaction_date | product_type | product_id |
|---|---|---|---|---|
| 1001 | 500 | 2022-09-01 00:00:00 | Loan Application | 1 |
| 1002 | 800 | 2022-09-01 00:00:00 | Credit Report | 25 |
| 1003 | 500 | 2022-09-02 00:00:00 | Business Consulting | 10 |
| 1004 | 300 | 2022-09-05 00:00:00 | Loan Application | 26 |
| 1005 | 500 | 2022-09-10 00:00:00 | Loan Application | 5 |
The identification of VIP users should be calculated per month for the last 6 months. We would like to consider a user a VIP if he/she has made at least 5 transactions in a single month.
Write a SQL query that returns a list of all VIP users for the previous six months, include the month and year, user_id, and number of transactions the user conducted during that month.
Answer:
The PostgreSQL query that solves this is as follows:
In the first part (WITH clause), we count the number of transactions per user per month for the last 6 months. Then, in the main part of the query, we filter out the cases where a user performed less than 5 transactions in a month, and order the results by year, month (both descending), and transactions (also descending). The result is the VIP users for each of the last six months, with the user having more transactions appearing first.
To practice a similar VIP customer analysis question on DataLemur's free online SQL code editor, try this Microsoft Azure Cloud SQL Interview Question:
SQL Question 2: Second Highest Salary
Suppose there was a table of MeridianLink employee salary data. Write a SQL query to find the 2nd highest salary amongst all the .
MeridianLink Example Input:
| employee_id | salary |
|---|---|
| 1 | 2500 |
| 2 | 800 |
| 3 | 1000 |
| 4 | 1200 |
Example Output:
| second_highest_salary |
|---|
| 1200 |
You can solve this problem directly within the browser on DataLemur:
Answer:
You can find a step-by-step solution here: 2nd Highest Salary.
SQL Question 3: How does and differ?
The clause works similarly to the clause, but it is used to filter the groups of rows created by the clause rather than the rows of the table themselves.
For example, say you were analyzing MeridianLink sales data:
This query retrieves the total sales for all products in each region, and uses the clause to only sales made after January 1, 2023. The rows are then grouped by region and the clause filters the groups to include only those with total sales greater than $500k.
SQL Question 4: Calculate The Average Processing Time Of Loan Applications Per Month
As a Data Analyst at MeridianLink, one of your first tasks is to analyze the processing time of loan applications. Specifically, you're asked to write a SQL query to calculate the average processing time (in days) for loan applications per month for the year 2022. The table has the following schema:
Example Input:
| application_id | submit_date | processing_date |
|---|---|---|
| 1 | 2022-01-01 | 2022-03-04 |
| 2 | 2022-02-15 | 2022-03-16 |
| 3 | 2022-03-10 | 2022-03-13 |
| 4 | 2022-04-11 | 2022-04-14 |
| 5 | 2022-05-05 | 2022-06-05 |
Dates are provided in the format 'YYYY-MM-DD'. You can assume that all applications are processed by the end of the year of 2022.
Answer:
This query works by taking advantage of PostgreSQL's date arithmetic capabilities. It subtracts the from to calculate the processing time for each loan application.
The function is used to isolate the month from the so that we can calculate the average processing time for each month.
The clause filters the data for the year 2022, groups data based on the submission month, and orders the output based on month. The function is then used to calculate the average processing time for each month.
To practice a related window function SQL problem on DataLemur's free online SQL code editor, try this Google SQL Interview Question:
SQL Question 5: In what circumstances might you choose to denormalize a database?
Normalization can reduce redundancy and improve performance and flexibility in a database.
By dividing larger tables into smaller, more specific ones linked by foreign keys, it is often possible to improve query speed. Note: it is not always the case that query speed will be improved due to costly nature of joins. Still, we think normalization is a good idea because it can also increase flexibility by making it easier to alter individual tables without affecting others, which can be useful for adapting MeridianLink's database to ever-changing business needs.
SQL Question 6: Analyzing Loan Application Status
At MeridianLink, one common task might be to analyze trends in loan application data. This could involve understanding the loan statuses for different loan types over time. Let's assume we have a loan application table that tracks the status of each application.
Please create a report that shows the average number of days it takes for each loan type to move from 'Application Received' to 'Loan Approved' or 'Loan Rejected' status in the year 2022.
Example Input:
| application_id | loan_type | status | status_date |
|---|---|---|---|
| 9120 | Home Loan | Application Received | 01/07/2022 |
| 9510 | Auto Loan | Application Received | 02/16/2022 |
| 8502 | Home Loan | Loan Approved | 01/15/2022 |
| 9120 | Home Loan | Loan Approved | 01/22/2022 |
| 9510 | Auto Loan | Loan Rejected | 02/20/2022 |
Answer:
This SQL query uses the clause inside the aggregate function to separately find the first date of 'Application Received' status and the first date of either 'Loan Approved' or 'Loan Rejected' status for each application. It then calculates the average number of days between these two dates for each loan type. The clause in the main statement ensures that we only include applications that have both an application received date and a resolution date.
SQL Question 7: When would you use a clustered index vs. a non-clustered index?
Both clustered and non-clustered indexes are used to improve query performance, but they have different characteristics.
A clustered index determines the physical order of the data rows in a table, while a non-clustered index does not.This means that a table can have only one clustered index, but it can have multiple non-clustered indexes.
In terms of query performance, a clustered index is usually faster for searches that return a large number of records, while a non-clustered index is faster for searches that return a small number of records. However, updates to a clustered index are slower, as they require the data rows to be physically rearranged, while updates to a non-clustered index are faster, as they only require the index data structure to be updated.
SQL Question 8: Determine the Click-Through Conversion Rates for MeridianLink Products
MeridianLink, a software company offering web-based credit reporting solutions, wants to understand the click-through conversion rate for their products from product views to adding a product to the cart.
Given the two tables and below:
Example Input:
| view_id | user_id | view_date | product_id |
|---|---|---|---|
| 5842 | 1500 | 07/05/2022 00:00:00 | 60001 |
| 5720 | 2100 | 07/15/2022 00:00:00 | 65010 |
| 5230 | 3100 | 08/15/2022 00:00:00 | 60001 |
| 5732 | 2000 | 07/18/2022 00:00:00 | 65010 |
| 4547 | 3200 | 08/01/2022 00:00:00 | 65010 |
Example Input:
| cart_id | user_id | cart_date | product_id |
|---|---|---|---|
| 6541 | 1500 | 07/05/2022 00:00:00 | 60001 |
| 6292 | 2000 | 07/18/2022 00:00:00 | 65010 |
| 5123 | 3100 | 08/15/2022 00:00:00 | 60001 |
Write a PostgreSQL query that calculates the click-through conversion rate by product on a monthly basis. The click-through conversion rate is defined as the number of users who added a product to the cart after viewing it divided by the total number of views for that product, shown as a percentage. Assume that if a user views a product and adds it to the cart on the same day, it counts as a successful conversion.
Answer:
This query will first group the data by month and product, then it calculates the total views () and successful conversions () for that product in each month. The click-through conversion rate () is then calculated as the ratio of successful conversions to total views, presented as a percentage. The result will give MeridianLink a comprehensive view of their product's click-through conversion rates on a monthly basis.
To solve a similar problem about calculating rates, try this TikTok SQL question within DataLemur's online SQL coding environment:
SQL Question 9: Average Loan Application Processing Time by Processor
MeridianLink is a company that provides loan and credit processing software solutions. Assume the company has a table that records all the loan applications processed by each processor. For each application, it records the , (the timestamp for when the processor starts working on a loan application), and (the timestamp for when the processor completes a loan application).
The question is: What is the average processing time, in minutes, for each processor across all the loan applications?
Example Input:
| application_id | processor_id | application_start_time | application_end_time |
|---|---|---|---|
| 1001 | 300 | 2022-06-01 10:00:00 | 2022-06-01 10:20:00 |
| 1012 | 400 | 2022-06-02 09:00:00 | 2022-06-02 09:30:00 |
| 1023 | 300 | 2022-06-02 14:00:00 | 2022-06-02 14:15:00 |
| 1034 | 500 | 2022-06-03 16:00:00 | 2022-06-03 16:30:00 |
| 1045 | 400 | 2022-06-04 08:00:00 | 2022-06-04 08:20:00 |
Example Output:
| processor_id | avg_processing_time_minutes |
|---|---|
| 300 | 17.5 |
| 400 | 25 |
| 500 | 30 |
Answer:
This query calculates the average processing time for each processor in the loan_applications table. The processing time of each loan application is calculated as the duration from to . The duration is then converted to minutes by dividing it by 60. The AVG function is then applied to compute the average processing time for each processor.
SQL Question 10: How does the constraint function, and in what scenarios might it be useful?
The CHECK constraint is used to enforce rules on the data in a specific column. If a row is inserted or updated with data that does not follow the CHECK constraint's rule, the operation will fail. The CHECK constraint is often used with other constraints, such as NOT NULL or UNIQUE, to ensure that data meets certain conditions. You may want to use a CHECK constraint in your database if you want to maintain the quality and reliability of your data by ensuring that it meets specific criteria.
For example, you might use a CHECK constraint to ensure that a column contains only positive numbers, or that a date is within a certain range.
SQL Question 11: Find Customers From Specific Location
As part of the customer relations team at MeridianLink, you are asked to focus on customers from California for a specific marketing campaign. Write an SQL query to find all customers whose address contains the word "California".
Example Input:
| customer_id | first_name | last_name | address |
|---|---|---|---|
| 101 | John | Doe | 123 Palm Dr, Los Angeles, California |
| 102 | Jane | Smith | 345 Oak Rd, New York, New York |
| 103 | Mary | Johnson | 678 Pine Path, San Francisco, California |
| 104 | James | Brown | 901 Elm St, Miami, Florida |
| 105 | Patricia | Jones | 234 Birch Blvd, Sacramento, California |
Example Output:
| customer_id | first_name | last_name | address |
|---|---|---|---|
| 101 | John | Doe | 123 Palm Dr, Los Angeles, California |
| 103 | Mary | Johnson | 678 Pine Path, San Francisco, California |
| 105 | Patricia | Jones | 234 Birch Blvd, Sacramento, California |
Answer:
Using the keyword in SQL allows for pattern matching, which can be helpful when searching for specific strings within database columns. Here, we're looking for customers whose addresses contain the word "California". The '%' sign is used to define wildcards (missing letters) both before and after the pattern. Therefore, any record which has "California" somewhere in the 'address' field will be returned by the query.
MeridianLink SQL Interview Tips
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the MeridianLink SQL interview is to solve as many practice SQL interview questions as you can!
In addition to solving the earlier MeridianLink SQL interview questions, you should also solve the 200+ SQL coding questions which come from companies like Facebook, Google, and VC-backed startups.
Each exercise has multiple hints, step-by-step solutions and crucially, there's an interactive coding environment so you can easily right in the browser your query and have it executed.
To prep for the MeridianLink SQL interview you can also be a great idea to practice interview questions from other tech companies like:
In case your SQL coding skills are weak, don't worry about jumping right into solving questions – refresh your SQL knowledge with this SQL tutorial for Data Analytics.
This tutorial covers things like CTEs and aggregate window functions – both of which come up frequently in SQL job interviews at MeridianLink.
MeridianLink Data Science Interview Tips
What Do MeridianLink Data Science Interviews Cover?
In addition to SQL query questions, the other topics to prepare for the MeridianLink Data Science Interview are:
- Experimental Design & AB Testing Questions
- Python or R Programming Questions
- Product Data Science Interview Questions
- ML Modelling Questions
- Behavioral Interview Questions
How To Prepare for MeridianLink Data Science Interviews?
I'm a tad biased, but I believe the optimal way to study for MeridianLink Data Science interviews is to read my book Ace the Data Science Interview.
The book solves 201 interview questions sourced from Microsoft, Amazon & startups. It also has a crash course on SQL, Product-Sense & ML. And finally it's vouched for by the data community, which is why it's got over 1000+ 5-star reviews on Amazon.