# 8 MassMutual SQL Interview Questions (Updated 2024)

Updated on

August 11, 2024

MassMutual employees use SQL for querying and manipulating policyholder data, including policy coverage and claims history, for risk assessment. It is also used for analyzing financial transactions data, such as premium payments and claims payouts, to generate insurance-related insights, which is why MassMutual uses SQL questions in interviews for Data Science and Data Engineering positions.

To help you ace the MassMutual SQL interview, we'll cover 8 Massachusetts Mutual Life Insurance SQL interview questions – can you solve them?

## 8 Massachusetts Mutual Life Insurance SQL Interview Questions

### SQL Question 1: New Policy Holder Analysis

As an analyst at MassMutual, you've been asked to analyze the company's policyholder's data. The data includes information about when a policy was taken (policy_start_date), the policy holder's age, and the annual premium (premium_amount).

Your task is to write a SQL query to calculate the average premium amount for each age category (under 30, 30 to 40, over 40) , and the ratio of the average premium of a given age category to the average premium of the next age category in each year. You need to use window functions in your solution.

Here is an example of how the data looks like:

##### Example Input
101/05/202028\$650
201/12/202035\$700
301/03/202045\$800
401/01/202132\$600
501/11/202027\$550

We are not interested in policies started in the current year. The output should be sorted by the year and age category.

##### Example Output:
2020under 30\$6001
202030 to 40\$7001.17
2020over 40\$8001.14
202130 to 40\$600null

The solution divides policy holders into age categories and calculates the average premium for each category using aggregate function AVG(). We then use the LAG() window function to find the average premium for the previous age category in each year. In the final SELECT statement, we calculate and output the ratio.

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

### SQL Question 2: Highly-Paid Employees

Imagine there was a table of MassMutual employee salary data. Write a SQL query to find the employees who earn more than their own manager.

#### MassMutual Example Input:

employee_idnamesalarydepartment_idmanager_id
1Emma Thompson38001
2Daniel Rodriguez2230110
3Olivia Smith800018
4Noah Johnson680028
5Sophia Martinez1750110
8William Davis70002NULL
10James Anderson40001NULL

#### Example Output:

employee_idemployee_name
3Olivia Smith

This is the output because Olivia Smith earns \$8,000, surpassing her manager, William Davis who earns 7,800.

Solve this question interactively on DataLemur:

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 hard to understand, you can find a detailed solution here: Employees Earning More Than Managers.

### SQL Question 3: What's the SQL command do, and when would you use it?

If you have two tables and want to retrieve only the rows that are present in both tables, just use the operator!

For example, let's use to find all of MassMutual's Facebook video ads that are also being run on YouTube:

### SQL Question 4: Retrieve Customers with Active Insurance Policies

As an insurance data analyst at MassMutual, you are tasked to retrieve customer details who have active policies, are aged between 18 to 25, and belong to Massachusetts.

Use the following tables to write your SQL query:

#### Table:

customer_idagestate
10124Massachusetts
10227California
10322Massachusetts
10430New York
10520Massachusetts

#### Table:

policy_idcustomer_idpolicy_typestatus
201101HealthActive
202102LifeExpired
203103AutomobileActive
204104HealthActive
205105AutomobileExpired

In this query, we join the customers table with insurance_policies table using an inner join, where the condition is both tables share the same customer_id. We then filter the records based on the conditions provided using the WHERE clause. This clause checks whether the insurance policy is active, the age of the customer is between 18 and 25, and the state of the customer is Massachusetts. This will bring forth all the customers who satisfy all conditions.

### SQL Question 5: How does the RANK() window function differ from DENSE_RANK()?

As the name implies, the window function ranks each row within your window/partition. If two rows have the same rank, the next number in the ranking will be the previous rank plus the number of duplicates. For example, if you've got three records tied for 5th place, they'll get the values 5, 6, and 7, and the next row after this 3-way tie will have a rank of 8.

The function assigns a distinct rank to each row within a partition based on the provided column value with no gaps. This function will assign the same rank to two rows if they have the same rank, and give the next row the next rank number. To make this more concrete, imagine you had three records at rank 5 – then, the next rank would be 6.

### SQL Question 6: Average Policy Premium for MassMutual

You are a database analyst at MassMutual, one of the leading insurance companies. MassMutual sells multiple types of insurance policies to its customers. Your task is to write a SQL query to find the average premium amount for each type of policy sold by the company.

#### Example Input:

101235401/01/2020Life Insurance300
102623105/01/2020Health Insurance200
103984209/01/2020Life Insurance400
104623101/01/2021Vehicle Insurance100
105984205/01/2021Health Insurance250

#### Example Output:

Life Insurance350.00
Health Insurance225.00
Vehicle Insurance100.00

This SQL query groups the policies by their type and calculates the average premium_amount for each type of policy. AVG is the SQL function to calculate the average of a column in a group. The result is a table with policy_types and their corresponding average premium amount.

To practice a very similar question try this interactive Amazon Average Review Ratings Question which is similar for calculating averages on grouped data or this Alibaba Compressed Mean Question which is similar for deriving insights from transactional data.

### SQL Question 7: Do and a typically produce equivalent results?

For all practical purposes, and do NOT produce the same results.

While both are similar in that they combine two tables, you can think of joins as increasing the width of the resulting table (you'll have more columns in the result set for a left/inner/right join), whereas a union is used to combine rows which increases the height of the result set but keeps the column count the same.

If your familiar with set operations, you can think of a as set addition, whereas a is set multiplication.

### SQL Question 8: Extracting Customer Data Based on Policy Number

Assume you are a data analyst at MassMutual, and you've been asked to retrieve customer records where the policy number starts with 'MA2020'. MassMutual uses the policy number to identify when and where a policy was initiated. Policies starting with 'MA2020' signify policies that were initiated in Massachusetts in the year 2020.

#### Example Input:

customer_idpolicy_numberpolicy_start_datenamestate
234MA202031642020-02-01 00:00:00JohnMA
542NY202047822020-07-01 00:00:00AmyNY
953MA202097822020-11-01 00:00:00TomMA
184CA201970132019-05-01 00:00:00MiaCA
287MA202084262020-08-01 00:00:00MaxMA

#### Example Output:

customer_idpolicy_numberpolicy_start_datenamestate
234MA202031642020-02-01 00:00:00JohnMA
953MA202097822020-11-01 00:00:00TomMA
287MA202084262020-08-01 00:00:00MaxMA

In this SQL query, the keyword is used to filter records in the table where the starts with 'MA2020'. The '%' sign is used to define wildcards (missing letters) both before and after the pattern. In this SQL query, it refers to any set of characters following 'MA2020'. The result is a table that includes all customers who bought their insurance policies in Massachusetts in the year 2020.

### MassMutual SQL Interview Tips

Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the MassMutual SQL interview is to solve as many practice SQL interview questions as you can! In addition to solving the earlier MassMutual SQL interview questions, you should also solve the 200+ SQL Interview Questions on DataLemur which come from companies like FAANG (FB, Apple, Amazon, Netflix, Google).

Each DataLemur SQL question has multiple hints, step-by-step solutions and crucially, there's an online SQL coding environment so you can right online code up your query and have it executed.

To prep for the MassMutual SQL interview it is also useful to practice SQL questions from other insurance companies like:

Stay up-to-date on the latest happenings in the financial industry with MassMutual's news and press releases!

However, if your SQL foundations are weak, forget about going right into solving questions – refresh your SQL knowledge with this free SQL tutorial.

This tutorial covers SQL topics like sorting results with ORDER BY and LEAD/LAG – both of these pop up frequently in SQL job interviews at MassMutual.

### Massachusetts Mutual Life Insurance Data Science Interview Tips

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

In addition to SQL query questions, the other topics covered in the MassMutual Data Science Interview are:

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

To prepare for MassMutual Data Science interviews read the book Ace the Data Science Interview because it's got:

• 201 interview questions taken from FAANG & startups
• a refresher covering Python, SQL & ML
• over 1000+ reviews on Amazon & 4.5-star rating

Don't forget about the behavioral interview – prep for it with this behavioral interview question bank.