10 AmFam SQL Interview Questions (Updated 2024)
AmFam employees use SQL for analyzing databases to assess risk and claims patterns, including identifying high-risk customer segments and claims hotspots, as well as generating customer profiling reports to tailor insurance products, such as customized policy offerings. That is why AmFam frequently asks SQL questions in interviews for Data Science, Data Engineering and Data Analytics jobs.
So, to help you study, here's 10 American Family Insurance Group SQL interview questions – can you answer each one?
10 American Family Insurance Group SQL Interview Questions
SQL Question 1: Identify Power Users
AmFam, a renowned insurance company, values clients who frequently update their insurance policies and purchase multiple products. Power users for AmFam are defined as users who have updated/purchased a new policy more than 10 times in the last month.
Your task is to write a SQL query to identify these power users.
Example Input:
| update_id | user_id | update_date | policy_id | type_of_update |
|---|---|---|---|---|
| 1021 | 452 | 08/01/2021 | 30001 | new_purchase |
| 2470 | 365 | 08/02/2021 | 60012 | policy_update |
| 5123 | 789 | 08/23/2021 | 84520 | policy_update |
| 3580 | 452 | 08/24/2021 | 30001 | policy_update |
| 4001 | 365 | 08/25/2021 | 60012 | new_purchase |
| 2300 | 123 | 08/26/2021 | 9021 | new_purchase |
| 4848 | 452 | 08/28/2021 | 30002 | policy_update |
Answer:
This code begins by filtering the table for rows where the is within the current month. It then groups these rows by and counts the number of policy updates each user has made. The clause is used to filter this grouped and counted result so that only users who have made more than 10 policy updates in the last month are included in the final data set.
To work on a similar customer analytics SQL question where you can solve it right in the browser and have your SQL query instantly graded, try this Walmart SQL Interview Question:
SQL Question 2: Employees Earning More Than Their Boss
Suppose there was a table of AmFam employee salaries. Write a SQL query to find the employees who earn more than their own manager.
AmFam Example Input:
| employee_id | name | salary | department_id | manager_id |
|---|---|---|---|---|
| 1 | Emma Thompson | 3800 | 1 | |
| 2 | Daniel Rodriguez | 2230 | 1 | 10 |
| 3 | Olivia Smith | 8000 | 1 | 8 |
| 4 | Noah Johnson | 6800 | 2 | 8 |
| 5 | Sophia Martinez | 1750 | 1 | 10 |
| 8 | William Davis | 7000 | 2 | NULL |
| 10 | James Anderson | 4000 | 1 | NULL |
Example Output:
| employee_id | employee_name |
|---|---|
| 3 | Olivia Smith |
This is the output because Olivia Smith earns $8,000, surpassing her manager, William Davis who earns 7,800.
Code your solution to this problem directly within the browser on DataLemur:
Answer:
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 with hints here: Employees Earning More Than Managers.
SQL Question 3: What does the constraint do?
A constraint ensures that all values in a column are different. It is often used in conjunction with other constraints, such as NOT NULL, to ensure that the data meets certain conditions.
For example, if you had AmFam sales leads data stored in a database, here's some constraints you'd use:
In this example, the constraint is applied to the "email" and "phone" fields to ensure that each AmFam lead has a unique email address and phone number. This helps to ensure the integrity of the data in the database and prevents errors that could occur if two leads had the same email address or phone number.
SQL Question 4: Calculate the cumulative number of policyholders acquired by AmFam per month in each state.
Assume AmFam is interested in understanding its growth in terms of the policyholders it has acquired every month in each state. Marketting team wants you to generate a report that calculates the cumulative number of policyholders AmFam has acquired month-over-month in each state.
Assume we have a table named "PolicyHolders" structured as below:
Example Input:
| policyholder_id | acquisition_date | state |
|---|---|---|
| 101 | 01/03/2022 | WI |
| 102 | 01/15/2022 | WI |
| 103 | 01/28/2022 | MN |
| 104 | 02/02/2022 | WI |
| 105 | 02/06/2022 | MN |
| 106 | 02/15/2022 | MN |
| 107 | 03/05/2022 | WI |
| 108 | 03/20/2022 | MN |
| 109 | 03/30/2022 | WI |
We would like the output to be the following format, where the "cumulative_policyholders" column represents the total number of policyholders acquired by AmFam up to that month in that state.
Example Output:
| month | state | cumulative_policyholders |
|---|---|---|
| 1 | WI | 2 |
| 1 | MN | 1 |
| 2 | WI | 3 |
| 2 | MN | 3 |
| 3 | WI | 5 |
| 3 | MN | 4 |
Answer:
The SQL query to solve this problem could look as follows:
This code extracts the month from the acquisition date and partitions data by the state. The COUNT function in the window clause counts the number of policyholders up to the current row in each partition (i.e., for each state), effectively creating a cumulative count of policyholders.
To practice another window function question on DataLemur's free interactive SQL code editor, try this Google SQL Interview Question:
SQL Question 5: What are the differences between an inner and a full outer join?
An inner join only includes rows from both tables that satisfy the join condition, whereas a full outer join includes all rows from both tables, regardless of whether they match the join condition or not.
For a concrete example, imagine you had two database tables, an advertising campaigns table which had information on Google Ads keywords and how much was bid for each keyword, and a AmFam sales table, which has data on how many products were sold and which Google Ads keyword drove that sale.
: retrieves rows from both tables where there is a match in the shared key or keys. For example, an INNER JOIN between the table and the table could be performed using the keyword column as the shared key. This would retrieve only the rows where the keyword in the table matches the keyword in the table.
: retrieves all rows from both tables, regardless of whether there is a match in the shared key or keys. If there is no match, values will be returned for the columns of the non-matching table.
SQL Question 6: Average Premium for Different Policy Types
AmFam (American Family Insurance) is a large insurance company offering various types of insurance policies like Auto, Home, Life, etc. Each policy has a premium amount that the customer has to pay. The company is interested to know the average premium received for different types of insurance policies.
Consider the following tables:
Example Input:
| policy_id | customer_id | policy_type | policy_start_date |
|---|---|---|---|
| 101 | 5001 | Auto | 01/01/2022 00:00:00 |
| 102 | 5002 | Home | 01/02/2022 00:00:00 |
| 103 | 5003 | Life | 01/03/2022 00:00:00 |
| 104 | 5004 | Auto | 01/04/2022 00:00:00 |
| 105 | 5005 | Life | 01/05/2022 00:00:00 |
Example Input:
| premium_id | policy_id | premium_amount |
|---|---|---|
| 201 | 101 | 1000.00 |
| 202 | 102 | 1500.00 |
| 203 | 103 | 2000.00 |
| 204 | 104 | 1200.00 |
| 205 | 105 | 3000.00 |
The objective is to write a PostgreSQL query to calculate the average premium amount for different policy types.
Answer:
This query joins the policies and premiums tables over the common column "policy_id". Then it groups the resulted dataset on the policy type and calculates the average premium amount for each type. This will help AmFam to understand which type of insurance policy generates more average revenue.
SQL Question 7: What are the different kinds of joins in SQL?
A join in SQL allows you to retrieve data from multiple tables and combine it into a single set of results.
In SQL, there are four different types of JOINs. To demonstrate each kind, imagine you had two database tables, an advertising campaigns table which had information on Google Ads keywords and how much was bid for each keyword, and a sales table, which has data on how many products were sold and which Google Ads keyword drove that sale.
: An INNER JOIN retrieves rows from both tables where there is a match in the shared key or keys. For example, an INNER JOIN between the table and the table could be performed using the keyword column as the shared key. This would retrieve only the rows where the keyword in the table matches the keyword in the table.
: A LEFT JOIN retrieves all rows from the left table (in this case, the table) and any matching rows from the right table (the Sales table). If there is no match in the right table, values will be returned for the right table's columns.
: A RIGHT JOIN retrieves all rows from the right table (in this case, the Sales table) and any matching rows from the left table (the table). If there is no match in the left table, values will be returned for the left table's columns.
: A FULL OUTER JOIN retrieves all rows from both tables, regardless of whether there is a match in the shared key or keys. If there is no match, values will be returned for the columns of the non-matching table.
SQL Question 8: Retrieve Customers with Active Insurance Policies
Imagine you work at AmFam, an auto insurance company, and you want to determine how many of your customers have an active insurance policy. Consider the database and as below:
Example Input:
| customer_id | first_name | last_name | state |
|---|---|---|---|
| 234 | John | Doe | Texas |
| 567 | Jane | Smith | California |
| 890 | Sam | Adams | New York |
| 321 | Mary | Johnson | Florida |
| 654 | Robert | Fox | Texas |
Example Input:
| policy_id | customer_id | start_date | end_date | status |
|---|---|---|---|---|
| 1 | 234 | 01/01/2022 | 12/31/2022 | active |
| 2 | 567 | 01/01/2021 | 12/31/2021 | expired |
| 3 | 890 | 01/01/2021 | 12/31/2021 | expired |
| 4 | 890 | 02/01/2022 | 12/31/2022 | active |
| 5 | 321 | 06/01/2022 | 06/01/2022 | cancelled |
Write a SQL query that shows all customers who currently have an active auto insurance policy. The query should return the customer's first name, last name, and state.
Answer:
This SQL JOIN query merges the and tables together based on where the in each table matches, and then filters down to only rows where the policy is labeled as 'active'.
SQL Question 9: Find the average policy premium at AmFam
You are given a table of insurance policies by American Family Insurance (AmFam), with various attributes like policy_id, start_date, policy_type, and annual_premium (in USD). Can you write a SQL query to find the average annual premium for each type of policy from the year 2020?
Consider the following table :
Example Input:
| policy_id | start_date | policy_type | annual_premium |
|---|---|---|---|
| 1001 | 01/15/2020 | Auto | 900 |
| 2002 | 05/22/2020 | Home | 1200 |
| 3003 | 03/30/2020 | Life | 600 |
| 4004 | 10/27/2019 | Auto | 950 |
| 5005 | 08/14/2020 | Life | 700 |
Answer:
In PostgreSQL, we will first use the EXTRACT function to find the year part of the start_date and filter those from the year 2020. Then we group by policy_type and find the average of the annual_premium.
This query will group all the policies from 2020 by their types and calculate the average annual premium for each group. The AVG function calculates the mean of the values in a group and ignores NULL values.
To practice a very similar question try this interactive Amazon Average Review Ratings Question which is similar for calculating averages for categories or this McKinsey 3-Topping Pizzas Question which is similar for < evaluating costs of categories.
SQL Question 10: In the context of databases, what does atomicity, consistency, isolation, and durability mean?
A DBMS (database management system), in order to ensure transactions are reliable and don't ruin the integrity of the data, tries to maintain the following ACID properties: Atomicity, Consistency, Isolation, and Durability.
To make this concept more concrete, here is what each of the ACID properties would mean in the context of banking transactions:
- Atomicity: a transaction is either completed fully, or not complete at all. For example, if a customer is transferring money from one account to another, the transaction should either transfer the full amount or none at all.
- Consistency: a transaction will only be completed if it follows all database constraints and checks. For example, if a customer is withdrawing money from an account, the transaction should only be completed if the account has sufficient funds available, otherwise the transaction is rejected
- Isolation: ensures that concurrent transactions are isolated from each other, so that the changes made by one transaction cannot be seen by another transaction. This isolation prevents race conditions, like two customers trying to withdraw money from the same account at the same time.
- Durability: ensures that once a transaction has been committed and completed, the changes are permanent. A reset / shutdown of the database shouldn't erase someone's savings accounts!
Preparing For The AmFam SQL Interview
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the AmFam SQL interview is to solve as many practice SQL interview questions as you can! Beyond just solving the above AmFam SQL interview questions, you should also solve the 200+ SQL questions from real Data Science & Analytics interviews which come from companies like FAANG tech companies and tech startups.
Each exercise has hints to guide you, detailed solutions and most importantly, there's an interactive SQL code editor so you can right in the browser run your SQL query and have it checked.
To prep for the AmFam SQL interview it is also a great idea to solve SQL problems from other insurance companies like:
- Guardian Life SQL questions
- Loews SQL questions
- Reinsurance Group of America SQL questions
- Lincoln National SQL questions
Explore the latest news and stories from AmFam and discover what's driving their success!
However, if your SQL foundations are weak, don't worry about going right into solving questions – go learn SQL with this SQL interview tutorial.
This tutorial covers things like Subquery vs. CTE and UNION – both of which pop up frequently during AmFam interviews.
American Family Insurance Group Data Science Interview Tips
What Do AmFam Data Science Interviews Cover?
In addition to SQL interview questions, the other types of problems tested in the AmFam Data Science Interview are:
- Probability & Stats Questions
- Python or R Coding Questions
- Data Case Study Questions
- ML Modelling Questions
- Behavioral Interview Questions
How To Prepare for AmFam Data Science Interviews?
I believe the best way to prepare for AmFam Data Science interviews is to read the book I wrote: Ace the Data Science Interview.
It solves 201 data interview questions taken from Microsoft, Amazon & startups. The book's also got a crash course on SQL, Product-Sense & 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 key to prepare for the AmFam behavioral interview. Start by reading the company's unique cultural values.