11 W.R. Berkley SQL Interview Questions (Updated 2024)
W.R. Berkley employees write SQL queries daily for analyzing insurance data, including policyholder information and claims data, to identify trends and patterns, as well as managing database systems for risk-prevention strategies, such as developing fraud detection algorithms. Because of this, W.R. Berkley often uses SQL questions during interviews for Data Science, Data Engineering and Data Analytics jobs.
As such, to help you practice for the W.R. Berkley SQL interview, we'll cover 11 W.R. Berkley SQL interview questions – can you solve them?
11 W.R. Berkley SQL Interview Questions
SQL Question 1: Identify Power Users in W.R. Berkley
W.R. Berkley is an insurance holding company that operates worldwide. For W.R. Berkley, power users could be customers with a high number of insurance policies, frequent claims, and high transactions in terms of premium amounts.
Suppose you have access to two tables, and . The table contains user id, name, and registration date. The table contains policy id, user id, issue date, claim amount, and premium amount.
Write a SQL query to identify the power users i.e., the top 10 users with the highest total number of insurance policies and total claim amounts in the last calendar year.
Example Input:
| user_id | name | registration_date |
|---|---|---|
| 1 | Alice | 2015-01-01 |
| 2 | Bob | 2017-06-15 |
| 3 | Charlie | 2018-05-20 |
| 4 | David | 2019-10-06 |
| 5 | Eve | 2020-12-31 |
Example Input:
| policy_id | user_id | issue_date | claim_amount | premium_amount |
|---|---|---|---|---|
| 1 | 1 | 2021-01-01 | 200 | 1000 |
| 2 | 1 | 2021-02-01 | 0 | 1200 |
| 3 | 2 | 2021-03-01 | 500 | 2000 |
| 4 | 3 | 2021-07-01 | 300 | 1500 |
| 5 | 3 | 2021-07-02 | 200 | 1000 |
Answer:
This is a SQL query using PostgreSQL.
The above query identifies the top 10 power users considering the number of insurance policies and claim amounts in the previous year. The operation combines rows from and tables based on user_id. The clause filters the policies issued in the last year. The clause groups records by user_id and name. The clause sorts the results first by the total number of policies and then total claim amounts in descending order. Finally, returns the top 10 users.
To solve a super-customer analysis question on DataLemur's free interactive SQL code editor, try this Microsoft SQL Interview problem:
SQL Question 2: Employee Salaries Higher Than Their Manager
Given a table of W.R. Berkley employee salaries, write a SQL query to find all employees who make more than their direct manager.
W.R. Berkley 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.
Solve this interview question and run your code right in DataLemur's online SQL environment:
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: Highly-Paid Employees.
SQL Question 3: 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 example, suppose you had a table of W.R. Berkley orders and W.R. Berkley customers.
INNER JOIN: When there is a match in the shared key or keys, rows from both tables are retrieved. In this example, an between the Orders and Customers tables would retrieve rows where the in the Orders table matches the in the Customers table.
FULL OUTER JOIN: A retrieves all rows from both tables, regardless of whether there is a match in the shared key or keys. If there is no match, NULL values will be returned for the columns of the non-matching table.
SQL Question 4: Calculate Running Total Claims
In W.R. Berkley, you have a dataset that contains details about the claims made by customers. These details include claim_id, policy_id, claim_date, claim_amount. Your task is to write a SQL query to calculate the running total of the claim_amount on a monthly basis for each policy, sorted by policy_id and date. The output should contain policy_id, year_month (in YYYYMM format), and monthly_running_total.
Example Input:
| claim_id | policy_id | claim_date | claim_amount |
|---|---|---|---|
| 135 | 321 | 2022-01-10 | 5000 |
| 226 | 321 | 2022-01-15 | 3000 |
| 424 | 321 | 2022-02-10 | 2000 |
| 535 | 789 | 2022-01-12 | 7000 |
| 623 | 789 | 2022-02-18 | 2500 |
| 712 | 789 | 2022-02-22 | 3500 |
Example Output:
| policy_id | year_month | monthly_running_total |
|---|---|---|
| 321 | 202201 | 8000 |
| 321 | 202202 | 2000 |
| 789 | 202201 | 7000 |
| 789 | 202202 | 6000 |
Answer:
This SQL query calculates the running total of the on a monthly basis for each policy. The window function SUM with the OVER clause is used to calculate the running total. We partition the data by and and order by . The keyword 'ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW' gives us the running total as it includes all rows from the start of the partition to the current row. Finally, we order the result by and .
p.s. Window functions show up super frequently during SQL interviews, so practice the 27+ window function questions on DataLemur
SQL Question 5: In the context of database schema design, can you give an example of a one-to-one relationship vs. a one-to-many relationship?
In database schema design, a one-to-one relationship between two entities means that each entity is linked to a single instance of the other. For example, the relationship between a car and a license plate is one-to-one because each car has only one license plate, and each license plate belongs to one car.
In contrast, a one-to-many relationship is when one entity can be associated with multiple instances of the other entity. An example of this is the relationship between a teacher and their classes - a teacher can teach multiple classes, but each class is only associated with one teacher.
SQL Question 6: Berkley Insurance Claim Data Analysis
W.R. Berkley is a renowned insurance company. They want to study the different types of claims their customers made over the past year. They want an average monthly claim amount for each type of claim.
To achieve this, you need to work with the following tables - 'claims' and 'claimtypes'.
Example Input:
| claim_id | customer_id | type_id | claim_date | claim_amount |
|---|---|---|---|---|
| 851 | 12001 | 1 | 01/04/2021 | 5000 |
| 852 | 12002 | 2 | 02/15/2021 | 6000 |
| 853 | 12003 | 3 | 03/10/2021 | 7000 |
| 854 | 12004 | 1 | 04/20/2021 | 8000 |
| 855 | 12005 | 2 | 05/30/2021 | 9000 |
Example Input:
| type_id | type_name |
|---|---|
| 1 | Home |
| 2 | Auto |
| 3 | Health |
Answer:
This query extracts the month from the claim_date from the "claims" table and joins it with the "claimtypes" table on the type_id. The query then uses GROUP BY clause to create groups of each month and claim type and calculates the average claim amount for each group. The result will be ordered by month in ascending order, indicating the average monthly claim amount for each type of claim.
SQL Question 7: What does do in a SQL query?
is used to combine the output of multiple statements into one big result!
For a concrete example, say you were a Data Analyst supporting the Sales Analytics team at W.R. Berkley, and data on potential sales leads lived in both Salesforce CRM and Hubspot. To write a query to analyze leads created after 2023 started, across both CRMs, you could use in the following way:
SQL Question 8: Filter Active and High-Value Customers
W.R. Berkley, an insurance company, maintains a database with records of all customer interactions. Each row represents an individual customer and contains the fields , , , , and . The can be 'Active', 'Inactive', or 'Expired'.
The company wants to identify active customers from the 'North' region, who had their last interaction in June 2022 and have a premium amount greater than or equal to $5000 for targeted marketing purposes. Write an SQL query that fits these conditions.
Example Input:
| customer_id | region | status | last_interaction_date | premium_amount |
|---|---|---|---|---|
| 1001 | North | Active | 06/15/2022 | $7000 |
| 2453 | South | Active | 06/20/2022 | $4800 |
| 3295 | North | Expired | 05/30/2022 | $5250 |
| 4312 | North | Inactive | 06/25/2022 | $6500 |
| 5201 | North | Active | 06/28/2022 | $7400 |
Example Output:
| customer_id | region | status | last_interaction_date | premium_amount |
|---|---|---|---|---|
| 1001 | North | Active | 06/15/2022 | $7000 |
| 5201 | North | Active | 06/28/2022 | $7400 |
Answer:
The function in PostgreSQL is used to fetch the year and month from a date. This query returns all the fields for customers who are active, from the 'North' region, interacted last in June 2022, and have a premium amount of $5000 or more.
SQL Question 9: Analyzing Click-through Conversion Rates for W.R. Berkley
W.R. Berkley, a company focused on the insurance sector, is analyzing the click-through rates of their website visitors. Specifically, they want to understand the click-through conversion rates, meaning the amount of people who click a product, and how many of those add it to the cart.
They have two main tables:
Example Input:
| click_id | user_id | click_date | product_id | convert_to_cart |
|---|---|---|---|---|
| 101 | 134 | 06/05/2022 00:00:00 | 001 | FALSE |
| 102 | 265 | 06/01/2022 00:00:00 | 002 | TRUE |
| 103 | 362 | 06/09/2022 00:00:00 | 001 | FALSE |
| 104 | 657 | 07/20/2022 00:00:00 | 003 | TRUE |
| 105 | 892 | 07/22/2022 00:00:00 | 002 | FALSE |
where indicates the user added the product to the cart after clicking it.
Example Input:
| product_id | product_name |
|---|---|
| 001 | Product A |
| 002 | Product B |
| 003 | Product C |
They want to know, per month, the click-through conversion rate for each product.
Answer:
We can use a query that first integrated the date part into our main table, grouping by the month, the product ID, counts the total clicks, and the clicks where is TRUE. Then, we calculate the conversion rate.
The result will give the monthly click-through conversion rate for each product.
Please note that in real-world scenarios, we probably would require a larger time frame for meaningful statistical results.
To practice a similar problem about calculating rates, try this TikTok SQL Interview Question within DataLemur's interactive SQL code editor:
SQL Question 10: In SQL, are blank spaces the same as a NULL?
A NULL value represents a missing or unknown value. It is different from a zero or a blank space, which are actual values. It is important to handle NULL values properly in SQL because they can cause unexpected results if not treated correctly.
SQL Question 11: Analyzing Policies By State
As W.R. Berkley is a global provider of two broad categories of insurance: Property Casualty and Reinsurance & Monoline Excess, let's say the company would like an analysis on their policies, specifically the average policy amount by state. They'd like to see the states with the highest average policy amount and which type of these insurance contribute most to this amount.
Example Input:
| policy_id | customer_id | state | issue_date | insurance_type | policy_amount |
|---|---|---|---|---|---|
| 101 | 0132 | New York | 2022/01/11 | Property_Casualty | 200000 |
| 102 | 0256 | California | 2022/01/15 | Reinsurance_Monoline_Excess | 150000 |
| 103 | 0363 | California | 2022/01/18 | Property_Casualty | 300000 |
| 104 | 0193 | Texas | 2022/02/01 | Reinsurance_Monoline_Excess | 400000 |
| 105 | 0982 | New York | 2022/02/05 | Property_Casualty | 500000 |
Example Output:
| state | average_policy_amount | popular_insurance_type |
|---|---|---|
| New York | 350000 | Property_Casualty |
| California | 225000 | Property_Casualty |
| Texas | 400000 | Reinsurance_Monoline_Excess |
Answer:
This SQL query groups the policy data by state and insurance_type, calculates the average policy_amount for each group and then orders the results by the average policy_amount in descending order, limiting the output to 3 highest states. The insurance type also appears in the result which is the most common in the particular state.
W.R. Berkley SQL Interview Tips
The key to acing a W.R. Berkley SQL interview is to practice, practice, and then practice some more! Besides solving the earlier W.R. Berkley SQL interview questions, you should also solve the 200+ SQL exercises on DataLemur which come from companies like Google, Facebook, Microsoft and Amazon.
Each problem on DataLemur has hints to guide you, full answers and most importantly, there is an interactive SQL code editor so you can right online code up your SQL query and have it checked.
To prep for the W.R. Berkley SQL interview it is also a great idea to practice SQL questions from other insurance companies like:
- Erie Insurance SQL questions
- Cincinnati Fin SQL questions
- Thrivent SQL questions
- Auto-Owners SQL questions
Learn how W.R. Berkley is harnessing the power of technology to revolutionize business insurance and protect your company's future!
But if your SQL query skills are weak, forget about going right into solving questions – refresh your SQL knowledge with this DataLemur SQL tutorial.
This tutorial covers topics including filtering data with WHERE and CASE/WHEN statements – both of which come up often during SQL interviews at W.R. Berkley.
W.R. Berkley Data Science Interview Tips
What Do W.R. Berkley Data Science Interviews Cover?
Beyond writing SQL queries, the other question categories tested in the W.R. Berkley Data Science Interview are:
- Experimental Design & AB Testing Questions
- Python or R Programming Questions
- Business Sense and Product-Sense Questions
- Machine Learning Questions
- Behavioral Questions focussed on W.R. Berkley values & principles
How To Prepare for W.R. Berkley Data Science Interviews?
To prepare for W.R. Berkley Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions taken from Google, Microsoft & tech startups
- a crash course covering Python, SQL & ML
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Don't ignore the behavioral interview – prep for it with this list of behavioral interview questions for Data Scientists.