10 Guardian Life SQL Interview Questions (Updated 2024)
Guardian Life employees use SQL for extracting valuable customer data, including policyholder information and claims experience, for refining insurance packages. It is also used for managing database systems for complex actuarial calculations, such as calculating policy premiums and reserves, which is why Guardian Life asks SQL questions in interviews for Data Analyst, Data Science, and BI jobs.
As such, to help you prep for the Guardian Life SQL interview, here’s 10 Guardian Life Ins. Co. of America SQL interview questions – can you solve them?
10 Guardian Life Ins. Co. of America SQL Interview Questions
SQL Question 1: Finding VIP Customers at Guardian Life
In this SQL question, we want to analyze Guardian Life's customer database to identify the customers who are the most important to the business. These "VIP" customers are those who have the highest premium commitments to Guardian Life on a monthly basis.
The premium commitment is calculated by the sum of the customer's insurance policies at Guardian Life. For the purpose of this question, we will refer to these customers as "Whale Users".
Let's assume that you have two tables: and . table stores customer-specific data, while table stores data on each policy sold by Guardian Life.
Example Input:
| customer_id | first_name | last_name |
|---|---|---|
| 001 | John | Doe |
| 002 | Jane | Smith |
| 003 | Bob | Gordon |
Example Input:
| policy_id | customer_id | monthly_premium |
|---|---|---|
| HS001 | 001 | 1500 |
| LTC002 | 001 | 2500 |
| HS003 | 002 | 1200 |
| DV004 | 002 | 1800 |
| AC005 | 003 | 2500 |
In this scenario, a "Whale User" is a customer whose total monthly premiums is one of the top 10% among all customers or is among the top 5 customers who have the highest premiums, whichever is higher.
Answer:
Here is a PostgreSQL query to identify the Whale Users (VIP customers):
This query first calculates the total monthly premium for each customer in the CTE. We then join with the table to get customer details. The customers are sorted by the descending order of their total premiums, limiting the result set to the greater of 10% of total customers or the top 5 customers.
To solve a related customer analytics question on DataLemur's free online SQL coding environment, try this recently asked Microsoft SQL interview question:
SQL Question 2: Top Three Salaries
Given a table of Guardian Life employee salary data, write a SQL query to find the top 3 highest paid employees in each department.
Guardian Life 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 |
Try this problem interactively on DataLemur:
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 confusing, you can find a step-by-step solution with hints here: Top 3 Department Salaries.
SQL Question 3: What's the purpose of a primary key?
A primary key is a column or group of columns that uniquely identifies a row in a table. For example, say you had a database of Guardian Life marketing campaigns data:
In this Guardian Life example, the CampaignID column is the primary key of the MarketingCampaigns table. The constraint ensures that no two rows have the same CampaignID. This helps to maintain the integrity of the data in the table by preventing duplicate rows.
SQL Question 4: Analyzing Policy Sales
At Guardian Life, one of the most important dataset is the database of sold policies. Suppose the sales team wants to analyze the total number of policies sold by each agent each month as well as the running total. Write an SQL query using Window Functions to perform this analysis.
Consider the following table :
Example Input:
| agent_id | policy_id | sale_date | premium |
|---|---|---|---|
| 927 | 00752 | 07/03/2022 | 1000 |
| 927 | 79852 | 07/05/2022 | 1500 |
| 315 | 74521 | 07/15/2022 | 1200 |
| 315 | 07483 | 07/25/2022 | 650 |
| 927 | 37428 | 08/01/2022 | 900 |
| 315 | 97482 | 08/05/2022 | 800 |
| 315 | 70839 | 08/15/2022 | 1300 |
| 927 | 20938 | 08/25/2022 | 1100 |
Answer:
This query will provide a table output for each month (referred as in the SELECT statement), showing the total number of policies sold by each agent in that month (). The query also calculates the running total of policies sold by using the Window Function SUM over the count of policies id partitioned by and ordered by the sale date.
For more window function practice, solve this Uber SQL problem on DataLemur's online SQL coding environment:
SQL Question 5: How does the LEAD() function differ from the LAG() function?
Both the and window functions are used to access a row at a specific offset from the current row.
However, the function retrieves a value from a row that follows the current row, whereas the function retrieves a value from a row that precedes the current row.
Say you had a table of salary data for Guardian Life employees:
| name | salary |
|---|---|
| Amanda | 130000 |
| Brandon | 90000 |
| Carlita | 80000 |
You could use the function to output the salary of each employee, along with the next highest-paid employee:
This would yield the following output:
| name | salary | next_salary |
|---|---|---|
| Amanda | 130000 | 90000 |
| Brandon | 90000 | 80000 |
| Carlita | 80000 | NULL |
Swapping for would get you the salary of the person who made just more than you:
| name | salary | next_salary |
|---|---|---|
| Amanda | 130000 | NULL |
| Brandon | 90000 | 130000 |
| Carlita | 80000 | 90000 |
SQL Question 6: Filter Client Records
As an assistant data officer at Guardian Life, you have been tasked with creating a SQL query that helps to filter down the customer records database. The goal is to select customers who are older than 65 years, have a life policy, are living in the state of Texas and have paid their premium payment in the last six months.
For this task, we'll work with two tables: the table and the table.
Example Input:
| customer_id | date_of_birth | state |
|---|---|---|
| 1001 | 01/01/1950 | 'Texas' |
| 1015 | 02/03/1980 | 'California' |
| 1024 | 07/10/1955 | 'Texas' |
| 1078 | 12/12/1952 | 'New York' |
| 1156 | 06/02/1945 | 'Texas' |
Example Input:
| policy_id | customer_id | policy_type | last_premium_date |
|---|---|---|---|
| 2030 | 1001 | 'Life' | 07/01/2022 |
| 2055 | 1015 | 'Life' | 08/01/2022 |
| 2098 | 1024 | 'Life' | 07/15/2022 |
| 2103 | 1078 | 'Home' | 07/10/2022 |
| 2160 | 1156 | 'Life' | 06/15/2022 |
We want to filter the list based on the conditions specified above. We can use PostgreSQL for this task.
Answer:
This query joins the two tables on the field and filters rows based on the conditions specified in the clause. We use to get the age and check if it's greater than 65. The condition ensures that we only select records where the last premium was paid within the last six months.
SQL Question 7: DBMS transactions are expected to follow the ACID properties. What are they, and what does each property mean?
ACID refers to the four key properties that are essential to the reliable and correct execution of database transactions. These properties are:
Atomicity: ensures that a transaction is treated as a single operation, and either all of the changes are made or none of them are! Basically, the database version of a "-FULL SEND-"
Consistency: ensures that the data is in a consistent state before and after a transaction is completed. For example, if wiring money to a friendly Nigerian prince whose fallen on hard times, consistency ensures that the total value of funds lost in my account is the same amount that's gained in the prince's account!
Isolation: ensures that the intermediate state of a transaction is invisible to other transactions. Back to the wiring-the-prince-some-money example, isolation ensures that another transaction sees the transferred funds in my account OR the princes, but not in both accounts at the same time
Durability: ensures that once a transaction has been completed successfully, the changes made by the transaction are permanent and cannot be undone, even in the event of a system failure. Basically, no taksies backsies (even if your system has a meltdown!).
SQL Question 8: Analyzing Click-Through Conversion Rates
As Guardian Life sells insurance products, they are particularly interested in the conversion rates of their digital ads. They want to know how effective their ads have been at attracting users to view a product, and then convert these views to actual purchases (policies sold).
Given the following tables , , and , write a query that gives the click-through rate (CTR) and conversion rate (CR) for each product for the month of September. The CTR is defined as (Number of Clicks / Number of Views) and the CR is defined as (Number of Purchases / Number of Clicks).
Example Input:
| view_id | user_id | view_date | product_id |
|---|---|---|---|
| 101 | 123 | 09/01/2022 00:00:00 | 20001 |
| 102 | 265 | 09/02/2022 00:00:00 | 20002 |
| 103 | 362 | 09/03/2022 00:00:00 | 20001 |
| 104 | 192 | 09/04/2022 00:00:00 | 20002 |
| 105 | 981 | 09/05/2022 00:00:00 | 20001 |
Example Input:
| click_id | user_id | click_date | product_id |
|---|---|---|---|
| 901 | 123 | 09/01/2022 00:02:00 | 20001 |
| 902 | 265 | 09/02/2022 00:03:00 | 20002 |
| 903 | 362 | 09/03/2022 00:04:00 | 20001 |
| 904 | 192 | 09/04/2022 00:05:00 | 20002 |
Example Input:
| purchase_id | user_id | purchase_date | product_id |
|---|---|---|---|
| 801 | 123 | 09/01/2022 00:10:00 | 20001 |
| 802 | 265 | 09/02/2022 00:20:00 | 20002 |
Answer:
This query calculates the CTR and CR per product for the month of September. It joins the , , and tables on and then it groups by to calculate the rates. The use of ensures that we are only counting unique clicks or purchases.
To solve a related SQL interview question on DataLemur's free interactive coding environment, solve this SQL interview question asked by Facebook:
SQL Question 9: Calculating Average Policy Premium for Different Policy Types
Suppose Guardian Life sells various types of insurance policies like Life Insurance, Health Insurance, Auto Insurance etc. Each policy has its own unique ID along with the customer ID it belongs to, the date it was purchased and the premium amount.
The task is to create a SQL query that calculates the average premium for each policy type per year.
Here are some sample data tables:
Example Input:
| policy_id | customer_id | policy_type | purchase_date | premium |
|---|---|---|---|---|
| 101 | 609 | Life Insurance | 2022-01-15 | 450 |
| 202 | 314 | Health Insurance | 2022-02-20 | 350 |
| 303 | 720 | Auto Insurance | 2022-03-25 | 600 |
| 404 | 314 | Life Insurance | 2022-02-10 | 500 |
| 505 | 609 | Health Insurance | 2022-01-05 | 300 |
We want to get output as follows:
Example Output:
| year | policy_type | average_premium |
|---|---|---|
| 2022 | Life Insurance | 475.00 |
| 2022 | Health Insurance | 325.00 |
| 2022 | Auto Insurance | 600.00 |
Answer:
Assuming the table name is "policies", here is SQL query to calculate average premium for each policy type:
The query begins by extracting the year from the purchase_date column in the policies table. Then it groups the results by year and policy_type. AVG() is an aggregate function used to calculate the average of the premium values within each grouping. The ORDER BY clause then sorts the output by year and policy_type for easier readability.
SQL Question 10: What is denormalization?
Denormalization is the practice of altering a database schema in a way that breaks the normalization rules (1st, 2nd, 3rd normal forms).
Denormalization is done to improve the performance of the database when it is being used for online analytics processing (OLAP), rather than online transaction processing (OLTP) use cases.
For these OLAP use cases, you're bottleneck frequently is joining multiple tables, but de-normalizing your database cuts these outs.
Denormalization can improve query performance by reducing the number of expensive joins required to retrieve data, but it comes with its own drawbacks too. By adding redundant columns, you incur more data storage costs, and there's need for more complex update and delete operations in order to maintain data integrity across all the duplicated data. Thus, it's important to carefully consider the trade-offs involved before implementing denormalization.
Guardian Life SQL Interview Tips
The best way to prepare for a SQL interview, besides making sure you have strong SQL fundamentals, is to practice a ton of real SQL questions that were asked in recent job interviews. Besides solving the earlier Guardian Life SQL interview questions, you should also solve the 200+ SQL exercises on DataLemur which come from companies like FAANG tech companies and tech startups.
Each problem on DataLemur has hints to guide you, full answers and most importantly, there is an online SQL code editor so you can instantly run your query and have it executed.
To prep for the Guardian Life SQL interview it is also useful to practice interview questions from other insurance companies like:
- Loews SQL questions
- Reinsurance Group of America SQL questions
- Liberty Mutual SQL questions
- Nationwide SQL questions
See how Guardian Life is driving business success with data-driven results that speak for themselves!
But if your SQL foundations are weak, forget about jumping right into solving questions – refresh your SQL knowledge with this SQL tutorial for Data Scientists & Analysts.
This tutorial covers SQL topics like CTE vs. Subquery and filtering on multiple conditions using AND/OR/NOT – both of which pop up frequently during Guardian Life SQL interviews.
Guardian Life Ins. Co. of America Data Science Interview Tips
What Do Guardian Life Data Science Interviews Cover?
Beyond writing SQL queries, the other question categories to prepare for the Guardian Life Data Science Interview are:
- Probability & Statistics Questions
- Python or R Programming Questions
- Product Metrics Interview Questions
- ML Interview Questions
- Behavioral Interview Questions focussed on Guardian Life values & principles
How To Prepare for Guardian Life Data Science Interviews?
To prepare for Guardian Life Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions taken from companies like Google, Tesla, & Goldman Sachs
- a crash course covering Python, SQL & ML
- over 1000+ 5-star reviews on Amazon
Also focus on the behavioral interview – prep for it using this Behavioral Interview Guide for Data Scientists.