At Unum Group, SQL is used for analyzing insurance claim data for insights, including claims frequency and severity analysis, as well as managing customer databases for policy customization, such as tailoring policy benefits and riders. That is the reason why Unum Group often asks SQL questions during interviews for Data Science and Data Engineering positions.
So, to help you prep, here’s 9 Unum Group SQL interview questions – can you answer each one?
As an analyst at Unum Group, a leading provider of financial protection benefits, you've been handed a bunch of policy data. The objective is to analyze the average policy premiums on a monthly basis per product category. Unum wants to understand how the monthly average policy premiums are trending for each product category.
The table has the following schema:
policy_id | customer_id | start_date | product_category | premium_amount |
---|---|---|---|---|
7405 | 574 | 01/08/2022 00:00:00 | LifeInsurance | 550.00 |
1204 | 390 | 02/18/2022 00:00:00 | DisabilityInsurance | 350.50 |
6789 | 143 | 02/05/2022 00:00:00 | LifeInsurance | 620.00 |
1240 | 892 | 03/04/2022 00:00:00 | WorkplaceBenefits | 275.75 |
6520 | 346 | 03/18/2022 00:00:00 | DisabilityInsurance | 370.00 |
Based on this table, write a PostgreSQL query to find the average monthly premium for each product category.
We are looking for output in the following format:
month | product_category | avg_premium |
---|---|---|
1 | Life Insurance | 550.00 |
2 | DisabilityInsurance | 485.25 |
3 | WorkplaceBenefits | 275.75 |
3 | DisabilityInsurance | 370.00 |
This query first extracts the month from the start_date in the policy table using the function. It then groups the data by the extracted month and product category to calculate the average premium for each product category for each month using the function. The result is sorted by month in ascending order and average premium in descending order to display the data in a logical, easy-to-understand format.
For more window function practice, solve this Uber SQL problem on DataLemur's online SQL code editor:
Given a table of Unum Group employee salary data, write a SQL query to find the 2nd highest salary at the company.
employee_id | salary |
---|---|
1 | 2500 |
2 | 800 |
3 | 1000 |
4 | 1200 |
second_highest_salary |
---|
1200 |
Try this question and run your code right in DataLemur's online SQL environment:
You can find a step-by-step solution with hints here: 2nd Highest Salary.
Constraints are used to specify the rules concerning data in the table. It can be applied for single or multiple fields in an SQL table during the creation of the table or after creating using the ALTER TABLE command. The constraints are:
Say you were storing sales analytyics data from Unum Group's CRM inside a database. Here's some example constraints you could use:
PRIMARY KEY constraint: You might use a PRIMARY KEY constraint to ensure that each record in the database has a unique identifier. For example, you could use the field as the primary key in the table.
FOREIGN KEY constraint: You might use a FOREIGN KEY constraint to link the data in one table to the data in another table. For example, you could use a foreign key field in the table to reference the field in the table.
NOT NULL constraint: You might use a NOT NULL constraint to ensure that a field cannot contain a NULL value. For example, you could use a NOT NULL constraint on the field in the table to ensure that each opportunity has a name.
UNIQUE constraint: You might use a UNIQUE constraint to ensure that the data in a field is unique across the entire table. For example, you could use a UNIQUE constraint on the field in the table to ensure that each contact has a unique email address.
CHECK constraint: You might use a CHECK constraint to ensure that the data in a field meets certain conditions. For example, you could use a CHECK constraint to ensure that the field in the table is a value between 0 and 100.
DEFAULT constraint: You might use a DEFAULT constraint to specify a default value for a field. For example, you could use a DEFAULT constraint on the field in the table to set the default value to .
Unum Group is a leading provider of financial protection benefits in the United States and the United Kingdom. The company provides disability, life, accident, critical illness, dental and vision benefits that protect millions of working people and their families.
You are provided with two tables. The first table lists all the insurance policies sold, each policy has a unique , a (foreign key to policy holder's table), the when the policy was issued, the when the policy will expire, and the column indicating whether or not the policy was renewed after it expired.
The second table lists all the policies that have been cancelled before their expiry date; when a policy gets cancelled, it enters this table with a .
Unum Group wants to analyze the policies issued in 2021. More specifically, they are interested to know the number of policies sold each month, how many of them were renewed, and how many of them got cancelled before the expiry date.
Here are the sample tables:
policy_id | holder_id | issue_date | expire_date | was_renewed |
---|---|---|---|---|
101 | 4501 | 2021-01-12 | 2022-01-12 | true |
102 | 3502 | 2021-02-18 | 2022-02-18 | true |
103 | 7775 | 2021-03-25 | 2022-03-25 | false |
104 | 8201 | 2021-04-30 | 2022-04-30 | false |
105 | 9234 | 2021-05-19 | 2022-05-19 | true |
policy_id | cancel_date |
---|---|
101 | 2021-06-15 |
104 | 2021-07-10 |
issue_month | policies_issued | policies_renewed | policies_cancelled |
---|---|---|---|
1 | 1 | 1 | 1 |
2 | 1 | 1 | 0 |
3 | 1 | 0 | 0 |
4 | 1 | 0 | 1 |
5 | 1 | 1 | 0 |
This query extracts the month from and counts the number of policies issued each month. It then checks the column to count how many of the issued policies were renewed. For the policies that were cancelled, a left join is used to the table to count those that have a . The result is an analysis of Unum Group's policies for 2021.
The clause serves as a filter for the groups created by the clause, similar to how the clause filters rows. However, is applied to groups rather than individual rows.
For example, say you were a data analyst at Unum Group trying to understand how sales differed by region:
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 $400,000.
You're given a table that contains information about customers of Unum Group. The columns are , , , , and .
Write a SQL query to filter out customers who enrolled after January 1, 2020, reside in either 'MA' (Massachusetts) or 'TN' (Tennessee), and hold the policy plan 'Premium' or 'Gold'.
customer_id | customer_name | date_of_enrollment | state_of_residence | policy_plan |
---|---|---|---|---|
6189 | John Doe | 02/13/2020 | MA | Silver |
7811 | Jane Smith | 05/22/2020 | TN | Premium |
5306 | Carlos Teach | 01/08/2020 | CT | Gold |
6365 | Nadia Buhari | 07/26/2021 | TN | Gold |
4522 | David Banner | 01/01/2020 | MA | Premium |
9078 | Chioma Abah | 03/02/2019 | MA | Gold |
7900 | Michael Towett | 12/31/2019 | TN | Silver |
This SQL query filters rows from the table in which the is later than '2020-01-01', is either 'MA' or 'TN', and is either 'Premium' or 'Gold'. The result will display customers who meet all these conditions.
To explain the difference between a primary key and foreign key, let's start with an example Unum Group sales database:
order_id | product_id | customer_id | quantity |
---|---|---|---|
1 | 303 | 1 | 2 |
2 | 404 | 1 | 1 |
3 | 505 | 2 | 3 |
4 | 303 | 3 | 1 |
In this table, could be a primary key. It is unique for each row in the table and cannot contain null values.
and could both be foreign keys. They reference the primary keys of other tables, such as a Products table and a Customers table, respectively. This establishes a relationship between the table and the other tables, such that each row in the sales database corresponds to a specific product and a specific customer.
Given a database table with two columns: , which stores the unique id of customers, and , which stores the email addresses of the customers, your task is to write a SQL query that returns all customers whose email is registered with a 'unum.com' domain.
cust_id | |
---|---|
1 | jdoe@unum.com |
2 | mmouse@disney.com |
3 | jhon.snow@unum.com |
4 | ironman@avengers.com |
5 | unum_agent007@unum.com |
cust_id | |
---|---|
1 | jdoe@unum.com |
3 | jhon.snow@unum.com |
5 | unum_agent007@unum.com |
The PostgreSQL query for the problem would look as follows:
This query operates by filtering the table with a WHERE clause that uses the LIKE keyword to match the email addresses ending with '@unum.com'. The '%' before '@unum.com' is a wildcard that matches any sequence of characters. Therefore, all email addresses that end with '@unum.com' are selected.
The SELECT statement specifies the columns and to be included in the result, thus the output includes the customer id and email of all customers whose email is registered with a 'unum.com' domain.
Unum Group is a company that sells insurance policies. The company wants information about the number of policies per policy type in each state. Write a SQL query that will join the table, which includes data about each customer's location, and the table, which includes data about the policies that each customer has.
For this question, assume the table has the following columns:
Assume the table has the following columns:
Here are your example input tables:
customer_id | state |
---|---|
123 | CA |
456 | NY |
789 | NY |
321 | TX |
654 | CA |
policy_id | customer_id | policy_type |
---|---|---|
10001 | 123 | Life |
10002 | 456 | Disability |
10003 | 789 | Life |
10004 | 321 | Life |
10005 | 654 | Disability |
Example output:
state | policy_type | policy_count |
---|---|---|
CA | Life | 1 |
CA | Disability | 1 |
NY | Life | 1 |
NY | Disability | 1 |
TX | Life | 1 |
The following SQL query can be used to solve this:
This SQL query works by joining the and tables on the column that they share. It then groups the resulting table by and . The function is used to calculate the number of rows (representing policies) in each group. The result is a breakdown of each policy type per state.
Since join questions come up so often during SQL interviews, practice this Spotify JOIN SQL question:
The best way to prepare for a SQL interview, besides making sure you have strong SQL fundamentals, is to practice a ton of real SQL questions that were asked in recent job interviews. Beyond just solving the earlier Unum Group SQL interview questions, you should also solve the 200+ SQL coding questions which come from companies like FAANG tech companies and tech startups.
Each DataLemur SQL question has hints to guide you, fully explained answers along with a discussion board to see how others solved it and best of all, there is an online SQL code editor so you can right in the browser run your SQL query answer and have it graded.
To prep for the Unum Group SQL interview it is also a great idea to practice interview questions from other insurance companies like:
Stay ahead of the curve with Unum Group's latest news and insights from the insurance industry!
But if your SQL skills are weak, don't worry about going right into solving questions – improve your SQL foundations with this DataLemur SQL Tutorial.
This tutorial covers topics including WHERE with AND/OR/NOT and SUM/AVG window functions – both of these come up routinely during Unum Group SQL interviews.
Beyond writing SQL queries, the other question categories covered in the Unum Group Data Science Interview include:
I think the best way to prep for Unum Group Data Science interviews is to read the book Ace the Data Science Interview.
It solves 201 data interview questions taken from FAANG, tech startups, and Wall Street. The book's also got a crash course on Product Analytics, SQL & ML. And finally it's vouched for by the data community, which is how it's gotten to be an Amazon best-seller with over 1000+ 5-star reviews.
While the book is more technical in nature, it's also crucial to prepare for the Unum Group behavioral interview. Start by reading the company's culture and values.