At Auto-Owners Insurance, SQL is used for analyzing large datasets, including policyholder claims history and demographic data, to predict risk factors. It is also used for managing customer data to enhance service delivery, such as personalizing policyholder communications, which is why Auto-Owners asks SQL problems during interviews for Data Analyst, Data Science, and BI jobs.
Thus, to help you prep, we've collected 10 Auto-Owners Insurance SQL interview questions – able to answer them all?
Suppose you are working for Auto-Owners. In the company's database, there is a table named which contains information about each policy holder. It includes the following columns:
Of interest to the business is the average premium they charge in each state. They want to understand this trend month by month.
Write a SQL query to calculate the monthly average insurance premium per state, with earliest month first, using window functions. Order the result by state and month.
id | state | start_date | premium |
---|---|---|---|
1 | AZ | 2022-05-06 | 2000.00 |
2 | TX | 2022-05-07 | 1800.00 |
3 | AZ | 2022-06-10 | 1500.00 |
4 | TX | 2022-06-15 | 2500.00 |
5 | AZ | 2022-06-20 | 2100.00 |
month_year | state | avg_premium |
---|---|---|
2022-05 | AZ | 2000.00 |
2022-05 | TX | 1800.00 |
2022-06 | AZ | 1800.00 |
2022-06 | TX | 2500.00 |
This query uses a window function to find the average premium per state for each month. The ‘OVER’ clause is used to define the window, or set of rows, the function operates on. The causes the function to reset every time the state or month_year changes. We finally order the results by state and month_year for easier reading.
For more window function practice, try this Uber SQL problem within DataLemur's interactive SQL code editor:
Given a table of Auto-Owners employee salary data, write a SQL query to find the top 3 highest paid employees within each department.
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 |
department_id | department_name |
---|---|
1 | Data Analytics |
2 | Data Science |
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 |
You can solve this interview question directly within the browser on DataLemur:
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 code above is confusing, you can find a detailed solution here: Top 3 Department Salaries.
A foreign key is a field in a database table that links to the primary key of another table, establishing a connection between the two tables.
To demonstrate this concept, let's analyze Auto-Owners's marketing analytics database which stores data from Google Ads campaigns:
ad_id | campaign_id | keyword | click_count |
---|---|---|---|
1 | 100 | Auto-Owners pricing | 10 |
2 | 100 | Auto-Owners reviews | 15 |
3 | 101 | Auto-Owners alternatives | 7 |
4 | 101 | buy Auto-Owners | 12 |
is a foreign key. It references the of the Google Ads campaign that each ad belongs to, establishing a relationship between the ads and their campaigns. This foreign key allows you to easily query the table to find out which ads belong to a specific campaign, or to find out which campaigns a specific ad belongs to.
It is also possible for a table to have multiple foreign keys that reference different primary keys in other tables. For example, the table could have additional foreign keys for the of the ad group that each ad belongs to, and the of the Google Ads account that the campaigns belong to.
As a data analyst for Auto-Owners, an auto insurance company, you have been asked to create a relational data model that represents the company's business of insuring automobiles and handling insurance claims. The model should capture policyholders, their cars, insurance policies, as well as claims made against those policies when accidents occur. You should also consider performance considerations in your design and explain your choices.
Different aspects you should consider are represented in the following sample tables:
holder_id | full_name | address |
---|---|---|
1001 | John Smith | 123 Main St, Springfield |
1002 | Jane Doe | 456 Elm St, Springfield |
1003 | Bob Davis | 789 Oak St, Springfield |
policy_id | holder_id | car_id | policy_start_date | policy_end_date |
---|---|---|---|---|
P001 | 1001 | C001 | 01/01/2022 | 31/12/2022 |
P002 | 1002 | C002 | 01/01/2022 | 31/12/2022 |
P003 | 1003 | C003 | 01/01/2022 | 31/12/2022 |
car_id | make | model | year |
---|---|---|---|
C001 | Toyota | Camry | 2018 |
C002 | Honda | Accord | 2019 |
C003 | Ford | Fusion | 2020 |
claim_id | policy_id | date_of_loss | claim_description | claim_status |
---|---|---|---|---|
CL001 | P001 | 01/15/2022 | Front end damage from a collision | Closed |
CL002 | P002 | 02/20/2022 | Rear end damage from a collision | Open |
CL003 | P003 | 03/25/2022 | Damage from hail storm | Closed |
Given the database design, write a PostgreSQL query that:
This query first joins the and tables on the field, then filters for active policies using . It groups by and selects those with more than one policy.
This query joins the , and tables to provide the claim details along with the policyholder's information for open claims.
is used to combine the output of multiple statements into one big result!
Suppose you were a Data Analyst at Auto-Owners working on a Sales Analytics project, and had data on sales leads from both the company's Salesforce CRM and it's legacy Hubspot CRM. To write a query to analyze leads created before 2023 started, across both CRMs, you could use in the following way:
filters out duplicates, so each email/job title/company only shows up once (even if it's in both Salesforce and HubSpot CRMs).
On the other hand, the operator does NOT filter out duplicates, so if a lead shows up in both CRMs, it'll be listed twice in the output of .
As an analyst at Auto-Owners, you are asked to get insights about insurance claims made by their customers. Your task is to find the average claim amount per vehicle type in the last year.
Here are the sample data tables you will be working with:
claim_id | policy_id | claim_date | claim_amount |
---|---|---|---|
1124 | 1181 | 2022-09-05 | $5000 |
1189 | 1493 | 2022-01-01 | $6500 |
1292 | 1181 | 2022-02-05 | $7000 |
1316 | 1634 | 2022-03-15 | $4500 |
1421 | 1493 | 2022-07-09 | $4000 |
policy_id | vehicle_id |
---|---|
1181 | V123 |
1493 | V124 |
1634 | V125 |
vehicle_id | vehicle_type |
---|---|
V123 | Car |
V124 | Car |
V125 | Truck |
This SQL query joins three tables together to get the necessary data: the table to get the claim amount and claim date, the table to link the claims with the vehicles, and the table to get the vehicle type. The query limits the date range to the last year using a WHERE clause. It then groups the data by vehicle type and calculates the average claim amount for each type.
To practice a very similar question try this interactive Amazon Average Review Ratings Question which is similar for calculating averages of numerical data or this JPMorgan Chase Card Launch Success Question which is similar for < analysing data within a specific time frame.
While both the and operators are used to filter data based on some criteria, selects for values within a given range, whereas for it checks if the value is in a given list of values.
For example, say you had a table called , which had the salary of the employee, along with the country in which they reside.
To find all employees who made between 120k, you could use the operator:
To find all employees that reside in the US or Canada, you could use the operator:
As an analyst at Auto-Owners, you have been tasked to measure the effectiveness of digital marketing campaigns. Specifically, management is interested in understanding the click-through rates from ad views to product inspections within the Auto-Owners website. Essentially, for each product category, they want to know how often someone who sees an ad will actually click through and inspect one of the advertised auto insurance products.
ad_id | campaign_id | view_date | user_id | category_id |
---|---|---|---|---|
101 | 1 | 01/05/2022 | 2001 | 1 |
102 | 1 | 01/05/2022 | 2002 | 1 |
103 | 2 | 01/06/2022 | 2003 | 2 |
104 | 3 | 01/06/2022 | 2004 | 3 |
inspection_id | inspection_date | user_id | product_id | category_id |
---|---|---|---|---|
201 | 01/05/2022 | 2001 | 101 | 1 |
202 | 01/05/2022 | 2002 | 102 | 1 |
203 | 01/06/2022 | 2003 | 103 | 3 |
In this query, we join and tables based on and , ensuring that the inspection date is the same as ad view date. We calculate the total counts of unique ad views and product inspections. The click-through-rate (CTR) is calculated as the ratio of unique product inspections to unique ad views. To handle potential division by zero, function is used to nullify the denominator if it is zero. Note that we are using left join to ensure all ad views are considered in the calculation even if they do not have corresponding inspections.
To practice another question about calculating rates, solve this TikTok SQL question on DataLemur's interactive coding environment:
In Auto-Owners insurance company, we have two main tables, and . The table stores information about each customer, such as his/her id, name, and other details. On the other hand, the table records all the auto insurance claims made by customers, with claim_id, customer_id, claim_date, claim_amount, and claim_type.
Our goal here is to find out the total claim amount by each customer along with their names.
Could you write a SQL query to accomplish this task?
The sample tables are as follows:
customer_id | name | address |
---|---|---|
101 | John Doe | 123 Main St |
102 | Jane Smith | 456 Oak Dr |
103 | Bob Johnson | 789 Pine St |
claim_id | customer_id | claim_date | claim_amount | claim_type |
---|---|---|---|---|
1001 | 101 | 06/08/2022 | 500 | "Collision" |
1002 | 101 | 06/18/2022 | 300 | "Theft" |
2001 | 102 | 07/10/2022 | 200 | "Hail" |
3001 | 103 | 07/26/2022 | 1000 | "Fire" |
This PostgreSQL query is joining the table with the table on the . Then it sums up all the by . If two customers have the same name they would be grouped together, for a more accurate result could be considered.
Since joins come up so often during SQL interviews, try this Snapchat JOIN SQL interview question:
The / operator is used to remove to return all rows from the first SELECT statement that are not returned by the second SELECT statement.
Note that is available in PostgreSQL and SQL Server, while MINUS is available in MySQL and Oracle (but don't stress about knowing which DBMS supports what exact commands since the interviewers at Auto-Owners should be lenient!).
For a tangible example in PostgreSQL, suppose you were doing an HR Analytics project for Auto-Owners, and had access to Auto-Owners's contractors and employees data. Assume that some employees were previously contractors, and vice versa, and thus would show up in both tables. You could use operator to find all contractors who never were a employee using this query:
The key to acing a Auto-Owners SQL interview is to practice, practice, and then practice some more! Beyond just solving the earlier Auto-Owners SQL interview questions, you should also solve the 200+ tricky sql questions on DataLemur which come from companies like Microsoft, Google, and Meta.
Each SQL question has multiple hints, fully explained answers along with a discussion board to see how others solved it and most importantly, there is an interactive coding environment so you can right in the browser run your SQL query and have it executed.
To prep for the Auto-Owners SQL interview it is also helpful to solve SQL problems from other insurance companies like:
Explore exciting job opportunities with Auto-Owners and join a team of dedicated professionals!
In case your SQL coding skills are weak, forget about going right into solving questions – strengthen your SQL foundations with this SQL tutorial for Data Scientists & Analysts.
This tutorial covers SQL topics like WHERE vs. HAVING and using LIKE – both of which show up routinely during SQL interviews at Auto-Owners.
In addition to SQL query questions, the other topics to prepare for the Auto-Owners Data Science Interview include:
I think the best way to prep for Auto-Owners Data Science interviews is to read the book I wrote: Ace the Data Science Interview.
It solves 201 interview questions sourced from FAANG (FB, Apple, Amazon, Netflix, Google). The book's also got a refresher on Stats, 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, it's also crucial to prepare for the Auto-Owners behavioral interview. A good place to start is by understanding the company's culture and values.