10 Cincinnati Fin SQL Interview Questions (Updated 2024)
Data Analysts and Data Engineers at Cincinnati Fin uses SQL to analyze insurance claim patterns, including identifying fraudulent claims and optimizing claims processing. It is also used for predicting risk factors in policy underwriting, such as assessing policyholder risk profiles, the reason why Cincinnati Fin asks SQL problems in job interviews.
Thus, to help you practice, we've curated 10 Cincinnati Financial SQL interview questions – able to solve them?
10 Cincinnati Financial SQL Interview Questions
SQL Question 1: Identifying VIP Users
Cincinnati Fin is a company that provides financial services and would like to identify their VIP users. VIP users are defined as customers who have made more than 500 transactions per month continuously for a period of 3 months.
Example Input:
| transaction_id | user_id | transaction_date | amount |
|---|---|---|---|
| 1181 | 123 | 06/08/2022 00:00:00 | 100.00 |
| 4752 | 265 | 06/10/2022 00:00:00 | 350.00 |
| 2439 | 362 | 06/18/2022 00:00:00 | 500.00 |
| 3521 | 192 | 07/26/2022 00:00:00 | 1000.00 |
| 7817 | 981 | 07/05/2022 00:00:00 | 200.00 |
Example Output:
| month | user_id | num_transactions |
|---|---|---|
| 06 | 123 | 520 |
| 06 | 265 | 510 |
| 07 | 192 | 530 |
Answer:
In PostgreSQL, you can identify VIP users with the following SQL query:
The query begins by creating a list of users and the number of transactions they have made each month, rounding the dates to the nearest month using . This list is limited to users who have made more than 500 transactions in any given month.
The list is then filtered again to include only those users who have had more than 500 transactions for at least 3 months in total, and a final output of month, user_id, and num_transactions for these VIP users is provided, sorted by month and user_id.
The outcome will only include users who satisfy the VIP criteria and thus, are most important for the business.
To practice a super-customer analysis question on DataLemur's free interactive SQL code editor, try this Microsoft Azure Cloud SQL Interview Question:
SQL Question 2: Top 3 Salaries
Given a table of Cincinnati Fin employee salary data, write a SQL query to find the top 3 highest paid employees in each department.
Cincinnati Fin 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 |
You can solve this interview 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 code above is hard to understand, you can find a step-by-step solution with hints here: Top 3 Department Salaries.
SQL Question 3: What does do in a SQL query?
The operator merges the output of two or more statements into a single result set. The two SELECT statements within the UNION must have the same number of columns and the data types of the columns are all compatible.
For example, if you were a Data Analyst on the marketing analytics team at Cincinnati Fin, this statement would return a combined result set of both Cincinnati Fin's Google and Facebook ads that have more than 300 impressions:
SQL Question 4: Calculate Monthly Average Ratings
As a Data Analyst at Cincinatti Financial, you have been provided with a dataset of reviews made by the customers towards various products they have used. Your task is to write a SQL query that calculates the monthly average ratings for each product.
The table has the following columns: (integer), (integer), (timestamp), (integer), and (integer, rating value between 1 and 5).
Example Input:
| review_id | user_id | submit_date | product_id | stars |
|---|---|---|---|---|
| 6171 | 123 | 02/02/2022 00:00:00 | 50001 | 4 |
| 7802 | 265 | 03/01/2022 00:00:00 | 69852 | 4 |
| 5293 | 362 | 03/18/2022 00:00:00 | 50001 | 3 |
| 6352 | 192 | 04/26/2022 00:00:00 | 69852 | 3 |
| 4517 | 981 | 04/05/2022 00:00:00 | 69852 | 2 |
Example Output:
| month | product_id | avg_stars |
|---|---|---|
| 2 | 50001 | 4.00 |
| 3 | 50001 | 3.00 |
| 3 | 69852 | 4.00 |
| 4 | 69852 | 2.50 |
Answer:
Here is a PostgreSQL solution using the and functions to retrieve the desired dataset:
The function is used to extract the month from the timestamp, allowing us to aggregate ratings over each month. The function is then used to calculate the average rating for each product per month.
Note that you will need to convert the month back to an appropriate format, depending upon your application. The above query will return the timestamp at the start of each respective month.
Pro Tip: Window functions are a frequent SQL interview topic, so practice every window function problem on DataLemur
SQL Question 5: Could you provide a list of the join types in SQL and explain what each one does?
Joins in SQL allow you to combine data from different tables based on a shared key or set of keys.
Four JOIN types are available in SQL. For an example of each one, say you had sales data exported from Cincinnati Fin's Salesforce CRM stored in a PostgreSQL database, and had access to two tables: and .
-
: retrieves rows from both tables where there is a match in the shared key or keys. For example, an INNER JOIN between the Sales table and the Customers table would retrieve only the rows where the customer_id in the Sales table matches the customer_id in the table.
-
: retrieves all rows from the left table (in this case, the sales table) and any matching rows from the right table (the table). If there is no match in the right table, NULL values will be returned for the right table's columns.
-
: retrieves all rows from the right table (in this case, the customers table) and any matching rows from the left table (the sales table). If there is no match in the left table, NULL values will be returned for the left table's columns.
-
: 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 6: Calculate Click-Through Conversion Rate
Cincinnati Fin is a company that utilizes advertisement marketing and sells digital products. As a Business Analyst in Cincinnati Fin, you are required to calculate the click-through conversion rate from users clicking on an advertisement to adding a product to the cart.
The company uses two main tables for keeping track of this process: and .
Example Input:
| click_id | user_id | click_date | ad_id |
|---|---|---|---|
| 371 | 543 | 06/07/2022 15:00:00 | 1234 |
| 472 | 465 | 06/09/2022 16:30:00 | 5678 |
| 873 | 282 | 06/13/2022 12:45:00 | 9101 |
| 654 | 768 | 06/15/2022 14:20:00 | 9101 |
| 955 | 789 | 06/17/2022 15:35:00 | 1234 |
Example Input:
| add_id | user_id | add_date | product_id |
|---|---|---|---|
| 8102 | 789 | 06/17/2022 16:05:00 | 55001 |
| 8103 | 465 | 06/09/2022 16:55:00 | 76082 |
| 8104 | 543 | 06/07/2022 15:30:00 | 55001 |
| 8105 | 882 | 06/22/2022 13:20:00 | 76082 |
| 8106 | 282 | 06/15/2022 13:00:00 | 55001 |
You have to write a SQL query to calculate the click-through conversion rate, i.e., percentage of users who added a product to the cart after clicking an ad.
Answer:
In this query, we're starting by creating a CTE (Common Table Expression) which combines the and tables filtering only users that added a product to the cart after clicking an ad. Then, we're calculating the total clicks, total adds to cart after clicks and the click-through conversion rate, which is the ratio of total adds to cart after clicks to total clicks, multiplied by 100 to get it as a percentage.
To solve a similar SQL interview question on DataLemur's free online SQL coding environment, try this Meta SQL interview question:
SQL Question 7: What is the purpose of the constraint, and when might it be helpful to use it?
The CHECK constraint is used to enforce rules on the data in a specific column. If a row is inserted or updated with data that does not follow the CHECK constraint's rule, the operation will fail. The CHECK constraint is often used with other constraints, such as NOT NULL or UNIQUE, to ensure that data meets certain conditions. You may want to use a CHECK constraint in your database if you want to maintain the quality and reliability of your data by ensuring that it meets specific criteria.
For example, you might use a CHECK constraint to ensure that a column contains only positive numbers, or that a date is within a certain range.
SQL Question 8: Find the Maximum Loan Amount per Branch
Assume that Cincinnati Fin, a financial services company, has several branches that process a large volume of loans for its clients. For internal data analysis, the company wants to know the maximum loan amount processed by each branch in the year 2022.
For this problem, we will work with one table, .
Example Input:
| loan_id | branch_id | client_id | loan_date | loan_amount |
|---|---|---|---|---|
| 101 | 321 | 1001 | 01/05/2022 00:00:00 | 5000.00 |
| 102 | 222 | 1002 | 02/10/2022 00:00:00 | 1500.00 |
| 103 | 321 | 1004 | 03/12/2022 00:00:00 | 7000.00 |
| 104 | 333 | 1002 | 04/15/2022 00:00:00 | 2500.00 |
| 105 | 222 | 1008 | 06/20/2022 00:00:00 | 8000.00 |
Answer:
This query first filters the records for the year 2022 using the WHERE clause. Then, with the GROUP BY clause, it groups records by , and for each group, it finds the maximum using the aggregate function MAX.
Example Output:
| branch_id | max_loan |
|---|---|
| 321 | 7000.00 |
| 222 | 8000.00 |
| 333 | 2500.00 |
SQL Question 9: Analyzing Customer Transaction Data
As a data analyst for Cincinnati Fin, you have access to the customer and transactions databases. Your task is to write an SQL query to identify the total transaction amount made by each customer and also join the result with customer's name from the customer table.
Example Input:
| customer_id | first_name | last_name |
|---|---|---|
| 3857 | John | Doe |
| 2846 | Jane | Smith |
| 4756 | Michael | Johnson |
| 8573 | Sarah | Taylor |
| 1836 | Emily | Davis |
Example Input:
| transaction_id | customer_id | amount |
|---|---|---|
| 6192 | 3857 | 100 |
| 7513 | 2846 | 200 |
| 4534 | 4756 | 300 |
| 5673 | 8573 | 400 |
| 8762 | 1836 | 500 |
| 4521 | 3857 | 200 |
| 6741 | 8573 | 600 |
| 6385 | 4756 | 700 |
| 4862 | 1836 | 800 |
| 5631 | 2846 | 900 |
Answer:
This query joins the and tables based on the column which is common between both tables. After joining, the query sums the transaction for each customer categorized by . The result provides the customer's first and last names from the table and the total transaction amount for each customer from the table.
Because join questions come up so often during SQL interviews, practice this SQL join question from Spotify:
SQL Question 10: What distinguishes an inner join from 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 Cincinnati Fin orders and Cincinnati Fin 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.
Cincinnati Fin SQL Interview Tips
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Cincinnati Fin SQL interview is to solve as many practice SQL interview questions as you can! In addition to solving the above Cincinnati Fin SQL interview questions, you should also solve the 200+ SQL questions on DataLemur which come from companies like FAANG (FB, Apple, Amazon, Netflix, Google).
Each DataLemur SQL question has hints to guide you, full answers and crucially, there is an online SQL coding environment so you can right in the browser run your SQL query answer and have it graded.
To prep for the Cincinnati Fin SQL interview it is also useful to practice interview questions from other insurance companies like:
- Thrivent SQL questions
- Auto-Owners SQL questions
- Mutual of Omaha SQL questions
- Assurant SQL questions
Stay up-to-date on the latest news and announcements from Cincinnati Fin and see how they're shaping the insurance industry!
In case your SQL query skills are weak, don't worry about diving straight into solving questions – improve your SQL foundations with this free SQL tutorial.
This tutorial covers SQL concepts such as RANK() window functions and working with string/text data – both of which show up often in Cincinnati Fin SQL assessments.
Cincinnati Financial Data Science Interview Tips
What Do Cincinnati Fin Data Science Interviews Cover?
In addition to SQL query questions, the other types of questions to prepare for the Cincinnati Fin Data Science Interview are:
- Probability & Stats Questions
- Python or R Coding Questions
- Product Analytics Questions
- ML Interview Questions
- Behavioral Interview Questions
How To Prepare for Cincinnati Fin Data Science Interviews?
To prepare for the Cincinnati Fin Data Science interview have a strong understanding of the company's culture and values – this will be key to acing the behavioral interview. For technical interviews get ready by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from Facebook, Google & startups
- A Refresher on SQL, Product-Sense & ML
- Amazing Reviews (1000+ 5-star reviews on Amazon)