8 Sentry Insurance SQL Interview Questions (Updated 2025)

Sentry Insurance use SQL for analyzing and processing vast insurance claim data, including claims frequency and severity analysis, as well as generating critical reports, such as dashboards for business leaders and data visualizations for stakeholders, supporting business intelligence and decision-making purposes. For this reason, Sentry Insurance always asks SQL questions during interviews for Data Science and Data Engineering positions.

Thus, to help you prep, here's 8 Sentry Insurance Group SQL interview questions – able to solve them?

8 Sentry Insurance Group SQL Interview Questions

SQL Question 1: Customer Policy Analytics

As a part of Sentry Insurance, you are part of a team that tracks and analyzes insurance policies taken by the customers. To assist in strategizing future business plans, you are requested to extract information detailing the 'number of policies bought by each customer each year' and 'the cumulative number of policies bought by each customer over the years'. To add a level of complexity, include only those customers who have bought at least 2 policies in a given year.

customer_policies Example Input:

Example Output:

Answer:

WITH yearly_policies AS (
    SELECT customer_id, 
           purchase_year, 
           COUNT(policy_id) AS annual_policies
    FROM customer_policies
    GROUP BY customer_id, purchase_year
    HAVING COUNT(policy_id) >= 2
)

SELECT yp.customer_id, 
       yp.purchase_year, 
       yp.annual_policies, 
       SUM(yp2.annual_policies) OVER (PARTITION BY yp.customer_id ORDER BY yp.purchase_year) AS cumulative_policies
FROM yearly_policies yp
JOIN yearly_policies yp2
ON yp.customer_id = yp2.customer_id AND yp2.purchase_year <= yp.purchase_year
ORDER BY yp.customer_id, yp.purchase_year;

In the first step, a common table expression (CTE) named yearly_policies is created which groups the data by customer_id, purchase_year and counts the number of policies bought by each customer in a year filtering out those who bought less than two policies.

In the next step, we use a window function to keep a running total of the policies bought by each customer over the years (cumulative_policies). The PARTITION BY clause in the window function ensures that the running total is reset for each customer, and the ORDER BY clause ensures that years are processed in ascending order for each customer. Thus, we get the desired output.

For more window function practice, try this Uber SQL problem within DataLemur's online SQL code editor:

SQL Question 2: 2nd Highest Salary

Given a table of Sentry Insurance employee salaries, write a SQL query to find the 2nd highest salary among all employees.

Sentry Insurance employees Example Input:

Example Output:

Write a SQL query for this interview question and run your code right in DataLemur's online SQL environment:

Answer:

SELECT MAX(salary) AS second_highest_salary
FROM employee
WHERE salary < (
    SELECT MAX(salary)
    FROM employee
);

You can find a detailed solution here: 2nd Highest Salary.

SQL Question 3: What's the difference between HAVING and WHERE?

The WHERE clause is used to filter rows from the result set of a SELECT, UPDATE, or DELETE statement. It allows you to specify a condition that must be met for a row to be included in the result set.

The HAVING clause is used to filter groups created by the GROUP BY clause. It is similar to the WHERE clause, but it is used to specify conditions on the groups created by the GROUP BY clause, rather than on the individual rows of the table.

Say you were working on a social media analytics project for Sentry Insurance.

Here is an example of a SQL query that you might write which uses both the WHERE and HAVING clauses:

SELECT platform, SUM(impressions) AS total_impressions, AVG(conversions) AS avg_conversions
FROM sentry_insurance_social_media_data
WHERE date >= '2023-01-01' AND date < '2023-02-01'
GROUP BY platform
HAVING SUM(impressions) > 5000 AND AVG(conversions) > 0.2;

This query retrieves the total impressions and average conversions for each platform in the sentry_insurance_social_media_data table, WHERE the date of the campaign is in January 2023. The rows are grouped by platform and the HAVING clause filters the groups to include only those with more than 5000 impressions and an average conversion rate above 0.2.

SQL Question 4: Calculate the average claim amount by customer

Sentry Insurance is looking to better understand their claim statistics. Specifically, your task is to identify the average claim amount by customer, which would help the company gain insights on client behavior and improve their service.

For this problem, we will use the Customers and Claims tables:

Customers Example Input:

Claims Example Input:

Answer:

You can fetch the required data using the following SQL query.

SELECT c.customer_id, cu.name, AVG(c.amount) AS avg_claim_amount
FROM Claims c
INNER JOIN Customers cu ON c.customer_id = cu.customer_id
GROUP BY c.customer_id, cu.name;

This query joins the Claims and Customers tables on the customer_id field, then groups by the customer_id and name. For each customer_id, it calculates the average claim amount. In the final output table, each row corresponds to a different customer and shows the average claim amount for that customer.

Example output:

SQL Question 5: Can you explain the purpose of the FOREIGN KEY constraint?

A FOREIGN KEY is a field in a table that references the PRIMARY KEY of another table. It creates a link between the two tables and ensures that the data in the FOREIGN KEY field is valid.

Say for example you had sales analytics data from Sentry Insurance's CRM (customer-relationship management) tool.

CREATE TABLE sentry_insurance_accounts (
    account_id INTEGER PRIMARY KEY,
    account_name VARCHAR(255) NOT NULL,
    industry VARCHAR(255) NOT NULL
);

CREATE TABLE opportunities (
    opportunity_id INTEGER PRIMARY KEY,
    opportunity_name VARCHAR(255) NOT NULL,
    account_id INTEGER NOT NULL,
    FOREIGN KEY (account_id) REFERENCES sentry_insurance_accounts(account_id)
);

The FOREIGN KEY constraint ensures that the data in the account_id field of the "opportunities" table is valid, and prevents the insertion of rows in the opportunities table that do not have corresponding entries in the sentry_insurance_accounts table. It also helps to enforce the relationship between the two tables and can be used to ensure that data is not deleted from the accounts table if there are still references to it in the opportunities table.

SQL Question 6: Average Claim Amount by Insurance Type at Sentry Insurance

You are provided with a database containing information about the numerous insurance claims Sentry Insurance has processed. The structure of the claims table is outlined in the example input below.

claims Example Input:

As an analyst for Sentry Insurance, management has asked you to find out the average claim amount for each type of insurance policy.

The output should contain the type of insurance policy and the average claim amount for that policy. Order the output by average claim amount in descending order.

Example Output:

Answer:

Here's the PostgreSQL query that would solve this problem:

SELECT policy_type, 
       AVG(claim_amount) as avg_claim_amount
FROM claims
GROUP BY policy_type
ORDER BY avg_claim_amount DESC;

This query first groups the data by type of insurance policy. It then computes the average claim amount for each group using the AVG function. Finally, the results are ordered by average claim amount in descending order.

To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for calculating top items per category or this Amazon Average Review Ratings Question which is similar for calculating average values and grouping data.

SQL Question 7: Have you ever had to optimize a slow SQL query? How did you do it?

Here's some strategies that can generally speed up a slow SQL query:

• Only query the columns you actually need
• Index the columns used in the query
• Use the right kind of JOIN (be explicit about inner vs. outer joins)
• Use the right data types (for example INT vs BIGINT can save you disk space and memory)

While there's an entire industry dedicated to database performance tuning , for Data Analyst and Data Science interviews at Sentry Insurance, knowing the above basics should be more than enough to ace the SQL interview! Data Engineers on the other hand should know more about the EXPLAIN command and understand a bit more about a query-execution plan.

SQL Question 8: Calculate Average Insurance Premiums by Policy Type

In Sentry Insurance company, each customer has an insurance policy, each policy belongs to a certain type (for example, car insurance, home insurance, life insurance, etc.), and each policy has a premium associated with it. Your job is to write a PostgreSQL query that calculates the average insurance premium for each policy type, for the year 2021.

policies Example Input:

Example Output:

Answer:

SELECT policy_type, 
       AVG(premium) as avg_premium
FROM policies
WHERE EXTRACT(YEAR FROM start_date) = 2021
GROUP BY policy_type;

In this PostgreSQL query, we select the policy type and calculate the average premium for each policy type. We use the WHERE clause to filter entries to only include policies that started in the year 2021. We then use the GROUP BY clause to group the results by policy type and apply the AVG function to calculate the average insurance premium for each group of policies with the same type.

Sentry Insurance SQL Interview Tips

The best way to prepare for a Sentry Insurance SQL interview is to practice, practice, practice. Besides solving the above Sentry Insurance SQL interview questions, you should also solve the 200+ DataLemur SQL Interview Questions which come from companies like Google, Uber, and Microsoft.

Each problem on DataLemur has hints to guide you, full answers 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 Sentry Insurance SQL interview you can also be useful to solve SQL questions from other insurance companies like:

• Penn Mutual SQL questions
• Brighthouse Financial SQL questions
• Medical Mutual SQL questions
• American Equity SQL questions

Discover how Sentry Insurance is using AI to enhance fraud detection and protect customers, while also improving overall efficiency and effectiveness!

But if your SQL query skills are weak, forget about going right into solving questions – strengthen your SQL foundations with this DataLemur SQL Tutorial.

This tutorial covers SQL concepts such as joining multiple tables and sorting results with ORDER BY – both of which show up frequently in Sentry Insurance SQL interviews.

Sentry Insurance Group Data Science Interview Tips

What Do Sentry Insurance Data Science Interviews Cover?

Beyond writing SQL queries, the other types of problems to practice for the Sentry Insurance Data Science Interview are:

• AB Testing & Experimentation Questions
• Python Pandas or R Coding Questions
• Product Analytics Questions
• Machine Learning Questions
• Behavioral & Resume-Based Questions

How To Prepare for Sentry Insurance Data Science Interviews?

I think the best way to study for Sentry Insurance Data Science interviews is to read the book Ace the Data Science Interview.

It solves 201 data interview questions taken from FAANG (FB, Apple, Amazon, Netflix, Google). The book's also got a refresher on Stats, SQL & ML. And finally it's helped thousands of people land their dream job in data, which is why it's earned 1000+ reviews on Amazon and a 4.5-star rating.

While the book is more technical in nature, it's also important to prepare for the Sentry Insurance behavioral interview. Start by understanding the company's unique cultural values.