10 Farmers Insurance SQL Interview Questions (Updated 2024)
At Farmers Insurance Exchange, SQL is used for analyzing claims data to identify fraud patterns, including suspicious claims activity and anomaly detection, as well as generating customer insights for personalized insurance offers, such as tailored policy recommendations. Because of this, Farmers Insurance asks SQL questionsduring interviews for Data Science, Data Engineering and Data Analytics jobs.
To help prep you for the Farmers Insurance SQL interview, we've collected 10 Farmers Insurance Exchange SQL interview questions in this blog.
10 Farmers Insurance Exchange SQL Interview Questions
SQL Question 1: Identify VIP Customers in Farmers Insurance Database
Given the and tables in the Farmers Insurance database, write a SQL query to identify the "VIP" or "Whale" customers. These customers can be defined as those who have purchased a high number of policies and have submitted a low number of claims.
Assume that each entry in the table represents a policy purchased by a customer, and each entry in the table represents a claim submitted by a customer. You should return a list of user IDs for these VIP customers.
Example Input:
| policy_id | user_id | purchase_date | policy_type |
|---|---|---|---|
| 1034 | 111 | 01/08/2022 | Car Insurance |
| 2030 | 222 | 02/15/2022 | Home Insurance |
| 2931 | 111 | 03/10/2022 | Health Insurance |
| 1309 | 333 | 04/16/2022 | Life Insurance |
| 1291 | 222 | 05/18/2022 | Car Insurance |
Example Input:
| claim_id | user_id | submit_date | claim_type | policy_id |
|---|---|---|---|---|
| 9071 | 111 | 06/08/2022 | Damage Claim | 1034 |
| 8908 | 444 | 06/10/2022 | Health Claim | 2930 |
| 8929 | 555 | 06/18/2022 | Accident Claim | 2345 |
| 9113 | 666 | 07/26/2022 | Theft Claim | 4566 |
| 9234 | 777 | 07/05/2022 | Fire Claim | 4566 |
Answer:
This query first joins the and tables on the user id. It then groups the joined table by user id, and applies a clause to filter for users who have purchased more than 5 policies and submitted less than 2 claims - these users are considered the VIP customers.
To work on another SQL customer analytics question where you can solve it right in the browser and have your SQL code automatically checked, try this Walmart Labs SQL Interview Question:
SQL Question 2: Top 3 Department Salaries
Assume you had a table of Farmers Insurance employee salary data. Write a SQL query to find the top three highest paid employees within each department.
Farmers Insurance Example Input:
| employee_id | name | salary | department_id |
|---|---|---|---|
| 1 | Emma Thompson | 3800 | 1 |
| 2 | Daniel Rodriguez | 2230 | 1 |
| 3 | Olivia Smith | 2000 | 1 |
| 4 | Noah Johnson | 6800 | 2 |
| 5 | Sophia Martinez | 1750 | 1 |
| 8 | William Davis | 6800 | 2 |
| 10 | James Anderson | 4000 | 1 |
Example Input:
| department_id | department_name |
|---|---|
| 1 | Data Analytics |
| 2 | Data Science |
Example Output:
| department_name | name | salary |
|---|---|---|
| Data Analytics | James Anderson | 4000 |
| Data Analytics | Emma Thompson | 3800 |
| Data Analytics | Daniel Rodriguez | 2230 |
| Data Science | Noah Johnson | 6800 |
| Data Science | William Davis | 6800 |
Check your SQL query for this question and run your code right in DataLemur's online SQL environment:
Answer:
We use the DENSE_RANK() window function to generate unique ranks for each employee's salary within their department, with higher salaries receiving lower ranks. Then, we wrap this up in a CTE and filter the employees with a ranking of 3 or lower.
If the solution above is tough, you can find a step-by-step solution here: Top 3 Department Salaries.
SQL Question 3: What is a primary key?
A primary key is a column or set of columns in a table that uniquely identifies each row in the table. The primary key is used to enforce the uniqueness and non-nullability of the rows in the table.
In a SQL database, a primary key is defined using the constraint. For example, say you had a table of :
In this example, the column is the primary key of the Farmers Insurance employees table. It is defined as an integer and is marked as the primary key using the constraint.
A table can have only one primary key, but the primary key can consist of multiple columns. For example, say you had a table of Farmers Insurance customer transactions:
In the above example, the primary key of the Orders table consists of two columns: TransactionID and ProductID. This means that the combination of OrderID and ProductID must be unique for every row in the table.
SQL Question 4: Month over Month policy sales analysis
Please write a SQL query to show the number of policies sold per agent each month, along with a running total of policies sold by that agent from the start of the year. This will allow us to evaluate the monthly performance of each agent over time.
Example Input:
| policy_id | agent_id | sell_date |
|---|---|---|
| 56487 | 717 | 01/18/2022 00:00:00 |
| 70092 | 864 | 02/10/2022 00:00:00 |
| 58926 | 717 | 02/19/2022 00:00:00 |
| 63558 | 192 | 03/26/2022 00:00:00 |
| 45129 | 981 | 03/05/2022 00:00:00 |
| 46128 | 717 | 03/15/2022 00:00:00 |
Example Output:
| month | agent_id | policies_sold | running_total |
|---|---|---|---|
| 1 | 717 | 1 | 1 |
| 2 | 717 | 1 | 2 |
| 2 | 864 | 1 | 1 |
| 3 | 192 | 1 | 1 |
| 3 | 717 | 1 | 3 |
| 3 | 981 | 1 | 1 |
Answer:
This query breaks down the sales by each agent for each month and provides a running total count of policies sold by the agent from the start of the year. This is accomplished through using the function twice with two different window specifications. The first one partitions by and month to get the count for each month, while the second one orders by month to get a running total.
p.s. Window functions show up pretty frequently during SQL interviews, so practice the 27+ window function questions on DataLemur
SQL Question 5: What does the clause do vs. the clause?
Before we address vs. question, let's address the clause which is used in a statement to group rows into a set of summary rows. It's typically used in conjunction with aggregate functions such as:
- `MIN
The clause is used to filter the groups created by the clause. It's similar to the clause, but it is used to specify conditions on the groups created by the clause, rather than on the individual rows of the table.
For example:
This query retrieves the total salary for each department and groups the rows by department. The clause then filters the groups to include only Farmers Insurance departments where the total salary is greater than $1 million
Note that the clause must be used after the clause and can only reference columns listed in the clause or aggregated columns selected by the statement.
SQL Question 6: Calculate Average Insurance Premium By Policy Type
Your task is to evaluate and calculate the average premium cost for each type of policy offered by Farmers Insurance. To accomplish this, you have been provided with two tables, namely and , which contain information about the policies and their types respectively.
Example Input:
| policy_id | customer_id | policy_type_id | premium |
|---|---|---|---|
| 1 | 100 | 1 | 500 |
| 2 | 200 | 2 | 1000 |
| 3 | 300 | 1 | 600 |
| 4 | 400 | 3 | 1500 |
| 5 | 500 | 2 | 1200 |
Example Input:
| policy_type_id | type_name |
|---|---|
| 1 | Home |
| 2 | Auto |
| 3 | Life |
Answer:
Here is the PostgreSQL query to solve it:
This query uses a JOIN operation to combine relevant data from the and tables. Then, it calculates the average premium for each policy type using the function and groups the result by policy type using . The result of this query would be a new table with each type of policy and the average premium cost for that policy type.
SQL Question 7: What's denormalization, and when does it make sense to do it?
Imagine you've got giant AF jigsaw puzzle with thousands of pieces, and each piece represents a piece of data in your database. You've spent hours organizing the puzzle into neat little piles, with each pile representing a table in your database. This process is called normalization, and it's a great way to make your database efficient, flexible, and easy to maintain.
But what if you want to solve the puzzle faster (aka make your queries run faster?)?
That's where denormalization comes in – Denormalization is like the puzzle-solving equivalent of taking a shortcut!
Instead of putting all the pieces in separate piles, you might decide to clone some of the pieces, and then have that one puzzle piece be put into multiple piles. Clearly, we are breaking the rules of physics, but that's just like de-normalization because it breaks the normal rules of normalization (1st, 2nd, 3rd normal forms).
By adding redundant puzzle pieces, it can be easier to find the pieces you need, but it also means that you have to be extra careful when you're moving pieces around or adding new ones (aka INSERT/UPDATE commands become more complex).
On the plus side, denormalization can improve the performance of your database and make it easier to use. On the downside, it can make your database more prone to errors and inconsistencies, and it can be harder to update and maintain. In short, denormalization can be a helpful tool, but it's important to use it wisely!
SQL Question 8: Calculate the Average Claim Amount per Policy by Insurance Type
Farmers Insurance uses a database to manage their policies and their associated claims. They would like to understand the average claim amount by policy type (e.g., Auto, Home, Life).
Specifically, they would like a query that returns the policy type along with the average of the claim amounts made under those policies.
Example Input:
| policy_id | customer_id | sign_up_date | policy_type |
|---|---|---|---|
| 1001 | 4451 | 01/01/2021 | Auto |
| 2002 | 6587 | 03/05/2021 | Home |
| 3003 | 6587 | 04/10/2021 | Life |
| 4004 | 7891 | 07/26/2021 | Auto |
| 5005 | 4451 | 08/05/2021 | Life |
Example Input:
| claim_id | policy_id | claim_date | claim_amount |
|---|---|---|---|
| 7007 | 1001 | 02/15/2021 | 5000 |
| 8008 | 2002 | 07/15/2021 | 10000 |
| 9009 | 3003 | 07/18/2021 | 50000 |
| 1010 | 4004 | 08/15/2021 | 4000 |
| 1111 | 4004 | 09/10/2021 | 3000 |
Answer:
This SQL query performs a join operation on the Policies and Claims tables based on the policy_id. The GROUP BY clause is used to group the data according to each policy_type. For each policy_type, it then calculates the average claim amount using the AVG function.
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for requiring calculation of metrics grouped by categories or this Amazon Average Review Ratings Question which is similar for invloving the calculation and grouping of averages.
SQL Question 9: Average Claim Amount per Policy Type
A business analyst at Farmers Insurance wants to understand the average claim amount per policy type for the last year. Write a SQL query to find the average claim amount per policy type for all claims filed in the year 2022.
Given two tables:
table:
| policy_id | policy_type | user_id |
|---|---|---|
| 1 | Home | 123 |
| 2 | Auto | 265 |
| 3 | Life | 362 |
| 4 | Auto | 192 |
| 5 | Home | 981 |
table:
| claim_id | policy_id | claim_amount | claim_date |
|---|---|---|---|
| 1001 | 1 | 1200.00 | 2022-05-10 |
| 1002 | 2 | 800.00 | 2022-05-15 |
| 1003 | 1 | 1500.00 | 2022-07-01 |
| 1004 | 2 | 600.00 | 2022-07-15 |
| 1005 | 3 | 2000.00 | 2022-09-15 |
| 1006 | 4 | 750.00 | 2022-11-05 |
| 1007 | 5 | 1300.00 | 2022-12-15 |
Answer:
The above SQL query joins the and tables on , then filters out the claims that were not made in the year 2022. It then groups by to find the average claim amount for each policy type.
SQL Question 10: Can you describe the concept of database denormalization in layman's terms?
Denormalization is a technique used to improve the read performance of a database, typically at the expense of some write performance.
By adding redundant copies of data or grouping data together in a way that does not follow normalization rules, denormalization improves the performance and scalability of a database by eliminating costly join operations, which is important for OLAP use cases that are read-heavy and have minimal updates/inserts.
Preparing For The Farmers Insurance SQL Interview
The best way to prepare for a Farmers Insurance SQL interview is to practice, practice, practice. In addition to solving the above Farmers Insurance SQL interview questions, you should also solve the 200+ SQL coding questions which come from companies like Amazon, JP Morgan, and insurance companies like Farmers Insurance.
Each problem on DataLemur has hints to guide you, detailed solutions and best of all, there is an interactive SQL code editor so you can instantly run your SQL query answer and have it checked.
To prep for the Farmers Insurance SQL interview it is also a great idea to solve SQL questions from other insurance companies like:
- Principal Financial SQL questions
- AmFam SQL questions
- Guardian Life SQL questions
- Loews SQL questions
Stay up-to-date on the latest developments and innovations from Farmers Insurance!
In case your SQL coding skills are weak, don't worry about going right into solving questions – go learn SQL with this SQL interview tutorial.
This tutorial covers SQL concepts such as handling date/timestamp data and UNION – both of which come up routinely during Farmers Insurance SQL interviews.
Farmers Insurance Exchange Data Science Interview Tips
What Do Farmers Insurance Data Science Interviews Cover?
Beyond writing SQL queries, the other types of problems to practice for the Farmers Insurance Data Science Interview include:
- Statistics & Probability Questions
- Coding Questions in Python or R
- Product Metrics Interview Questions
- ML Modelling Questions
- Behavioral & Resume-Based Questions
How To Prepare for Farmers Insurance Data Science Interviews?
To prepare for the Farmers Insurance Data Science interview make sure you have a firm understanding of the company's cultural values – this will be clutch for acing the behavioral interview. For the technical Data Science interviews, get ready by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from Microsoft, Amazon & startups
- A Crash Course on Python, SQL & ML
- Amazing Reviews (1000+ 5-star reviews on Amazon)