At CUNA Mutual Group, SQL is used for analyzing complex financial datasets, such as credit union performance metrics and loan portfolios, to identify trends, as well as managing data integrity for precise risk assessment calculations, like calculating credit scores. This is why TruStage asks SQL problems in interviews for Data Science and Data Engineering positions.
Thus, to help you practice for the TruStage SQL interview, we've curated 10 CUNA Mutual Group SQL interview questions in this article.
TruStage is a insurance company and one of the key metrics that they monitor is the total insured amount per policyholder per month. As part of your interview, they have asked you to create a SQL query that calculates this amount.
policy_id | policyholder_id | policy_start_date | insured_amount |
---|---|---|---|
101 | 254 | 2022-01-01 | 10000 |
102 | 365 | 2022-01-15 | 50000 |
103 | 254 | 2022-02-10 | 30000 |
104 | 654 | 2022-03-01 | 20000 |
105 | 365 | 2022-03-15 | 40000 |
payment_id | policy_id | payment_date | amount |
---|---|---|---|
1001 | 101 | 2022-02-01 | 200 |
1002 | 102 | 2022-02-15 | 500 |
1003 | 103 | 2022-03-10 | 600 |
1004 | 104 | 2022-04-01 | 400 |
1005 | 105 | 2022-04-15 | 800 |
This SQL calculation uses a window function to calculate the sum of insured amounts for each policyholder per month, ordered by the policy start date. The function is used to round down the policy_start_date to month, and the function is partitioned by policyholder_id to calculate the total insured amount per policyholder per month. Each policyholder's total insured amount per month is returned, sorted by policyholder_id and month.
Pro Tip: Window functions are a popular SQL interview topic, so practice all the window function problems on DataLemur
You're given a table of TruStage employee and department salary data. Write a SQL query to find departments where the departments average salary is higher than the company's average salary.
Solve this question directly within the browser on DataLemur:
The answer is LONG – 30+ lines of SQL. You can find a detailed solution with hints here: Department Salaries.
Both types of joins in SQL help you retrieve data from multiple tables and merge the results into a single table.
To demonstrate the difference between a left join versus a right join, imagine you had two database tables: an table that contains data on Google Ads keywords and their bid amounts, and a table with information on product sales and the Google Ads keywords that drove those sales.
A 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.
TruStage, a major insurance provider, wants to carry out an analysis of their customer dataset. They are specifically interested in identifying customers whose coverage has lapsed due to non-payment and have not renewed their policies for over a year.
The relevant data can be found in their 'customers' table, as structured below:
customer_id | name | register_date | coverage_end_date | payment_status |
---|---|---|---|---|
2341 | John Doe | 2019-06-10 | 2021-05-20 | Paid |
2394 | Jane Zachary | 2019-05-15 | 2021-07-15 | Unpaid |
2023 | Alice Wong | 2019-09-12 | 2020-09-11 | Paid |
1762 | Bob Wilson | 2016-03-20 | 2021-06-20 | Unpaid |
1185 | Emma Watson | 2018-10-09 | 2020-10-08 | Unpaid |
Write an SQL query that will obtain a list of customers whose payment status is 'Unpaid' and their coverage end date is more than one year from the current date. Assume the current date for the problem is '2022-07-01'.
The query filters out the customers from the 'customers' table whose payment status is 'Unpaid' and whose coverage has ended before '2021-07-01', which means their coverage has lapsed for over a year according to the provided current date. The result will have the filtered customer's details with their customer_id, name, register_date, coverage_end_date, and payment_status.
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 TruStage's marketing analytics database which stores data from Google Ads campaigns:
ad_id | campaign_id | keyword | click_count |
---|---|---|---|
1 | 100 | TruStage pricing | 10 |
2 | 100 | TruStage reviews | 15 |
3 | 101 | TruStage alternatives | 7 |
4 | 101 | buy TruStage | 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.
TruStage is an insurance company that provides various types of insurance policies such as life insurance, health insurance, and auto insurance. Suppose you have a database table named 'policies' that tracks each policy sold by TruStage with details like policy_id, user_id, policy_type and policy_premium. Calculate the average policy premium for each type of insurance policy sold.
policy_id | user_id | policy_type | policy_premium |
---|---|---|---|
1234 | 7832 | Life Insurance | 150 |
4869 | 1548 | Auto Insurance | 100 |
5723 | 6491 | Health Insurance | 200 |
6731 | 1723 | Life Insurance | 180 |
9253 | 0648 | Auto Insurance | 120 |
policy_type | avg_premium |
---|---|
Life Insurance | 165 |
Auto Insurance | 110 |
Health Insurance | 200 |
This query calculates the average policy premium for each type of insurance policy. It uses the AVG function which computes the average of the values in a column. It combines this with the GROUP BY statement, which arranges the input into groups, which are defined by the values in the policy_type column. The AVG function then calculates the average premium for each of these groups.
To practice a very similar question try this interactive Amazon Average Review Ratings Question which is similar for calculating averages grouped by a category or this Alibaba Compressed Mean Question which is similar for finding mean values.
To better understand the difference between a primary key and a foreign key, let's use an example from TruStage's marketing analytics database, which holds data on Google Ads campaigns:
ad_id | campaign_id | keyword | click_count |
---|---|---|---|
1 | 100 | TruStage pricing | 10 |
2 | 100 | TruStage reviews | 15 |
3 | 101 | TruStage alternatives | 7 |
4 | 101 | buy TruStage | 12 |
In this table, serves as the primary key. It uniquely identifies each ad and cannot be null.
is a foreign key that connects to the of the corresponding Google Ads campaign. This establishes a relationship between the ads and their campaigns, enabling easy querying to find which ads belong to a specific campaign or which campaigns a specific ad belongs to.
The table may also have multiple foreign keys that reference primary keys in other tables. For example, and foreign keys could be used to link each ad to its ad group and the Google Ads account that the campaigns belong to, respectively.
TruStage is a company that offers various insurance products. They have implemented a digital marketing campaign where they have ads displayed on various websites. The premise is simple, when someone clicks on their ad, they land on the TruStage product webpage.
TruStage marketing team wants to measure the click-through rate (CTR) of these ads. They need to know the percentage of unique users who viewed an ad and then clicked on it. This information is critical for evaluating the effectiveness of their online advertising campaigns. They also want to compare the CTRs based on the type of product promoted in the ad.
Use the following data from the and tables:
ad_id | product_type |
---|---|
1 | life |
2 | auto |
3 | home |
click_id | user_id | ad_id |
---|---|---|
101 | 1001 | 1 |
102 | 1002 | 2 |
103 | 1002 | 2 |
104 | 1003 | 1 |
105 | 1003 | 3 |
We can calculate the CTR as the number of unique users who clicked on an ad to the number of total unique users who saw the ad. Here, we are considering the number of unique users who clicked, because a person might click multiple times.
SQL query:
This query joins the and tables on the field. It then groups records by and calculates the click-through rate for each group.
Remember, the CTR calculated here is a percentage. So, a CTR of 0.05 would be 5%, which means 5 out of every 100 users who saw the ad ended up clicking on it.
To solve another question about calculating rates, solve this SQL interview question from TikTok on DataLemur's interactive SQL code editor:
You are given two tables, 'customers' and 'purchases'. The 'customers' table holds information about each customer, including their id, name, email, and the state they live in. The 'purchases' table contains information about each purchase made by the customers, including the purchase id, the customer id of the customer who made the purchase, the product id of the purchased product and the purchase price.
Write a SQL query to analyze the customer database and join it with the purchases table to find out the total amount spent by customers in each state.
customer_id | name | state | |
---|---|---|---|
1 | John Doe | johndoe@example.com | California |
2 | Jane Smith | janesmith@example.com | New York |
3 | Bob Johnson | bobjohnson@example.com | Texas |
4 | Mary Williams | marywilliams@example.com | California |
purchase_id | customer_id | product_id | price |
---|---|---|---|
1 | 1 | 200 | 25 |
2 | 1 | 201 | 30 |
3 | 2 | 200 | 25 |
4 | 3 | 201 | 30 |
5 | 4 | 200 | 25 |
6 | 2 | 201 | 30 |
state | total_spent |
---|---|
California | 80 |
New York | 55 |
Texas | 30 |
This SQL query uses an INNER JOIN to merge the table with the table based on the common . It then groups the resulting table by the column and calculates the sum of the column for each group to get the total amount spent by customers in each state.
Because joins come up routinely during SQL interviews, practice an interactive Spotify JOIN SQL question:
Imagine you are organizing a party and have two database tables: one table of people you want to invite and another list of food items you want to serve.
A cross join would be like inviting every person on your list to the party and serving them every food item on the menu, regardless of whether they like the food or not. So, if you had 10 people on your invite list and 5 food items on the menu, you would generate all 50 different combinations of people and food (10 x 5 = 50).
On the other hand, a natural join would be like inviting only the people who like the food items on the menu (based on doing a inner/left/right/outer JOIN on a common key like ).
The key to acing a TruStage SQL interview is to practice, practice, and then practice some more! Beyond just solving the earlier TruStage SQL interview questions, you should also solve the 200+ SQL Interview Questions on DataLemur which come from companies like Netflix, Google, and Amazon.
Each exercise has hints to guide you, fully explained answers along with a discussion board to see how others solved it and crucially, there is an interactive SQL code editor so you can right in the browser run your query and have it graded.
To prep for the TruStage SQL interview it is also helpful to solve interview questions from other insurance companies like:
Get the latest news and insights from TruStage, a trusted name in insurance and financial services!
In case your SQL skills are weak, forget about going right into solving questions – refresh your SQL knowledge with this SQL tutorial for Data Scientists & Analysts.
This tutorial covers SQL topics like UNION vs. joins and joining a table to itself – both of which pop up frequently in TruStage interviews.
Besides SQL interview questions, the other topics tested in the TruStage Data Science Interview are:
I believe the best way to prepare for TruStage Data Science interviews is to read the book Ace the Data Science Interview.
It has 201 data interview questions sourced from Facebook, Google, & Amazon. The book's also got a crash course covering SQL, Product-Sense & ML. And finally it's helped a TON of people, 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 important to prepare for the TruStage behavioral interview. A good place to start is by understanding the company's culture and values.