9 Prudential Financial SQL Interview Questions (Updated 2025)

Prudential Financial employees use SQL often for analyzing financial data and optimizing insurance risk modeling, as well as querying customer databases. Because of this, Prudential Financial typically asks SQL problems during interviews for Data Science and Data Engineering positions.

So, to help you practice for the Prudential Financial SQL interview, here's 9 Prudential Financial SQL interview questions can you solve them?

9 Prudential Financial SQL Interview Questions

SQL Question 1: Sales Performance Analysis for Different Sales Agents

Prudential Financial has a team of sales agents who sell insurance policies. They want to assess the performance of their sales agents. The metric they use for this is the total premium amounts of the policies sold by each agent in each month. They also want to see how each agent's monthly performance is relative to the overall average monthly premium amounts.

Here is the schema of the sales table:

sales Example Input:

Please write a query to generate a report which includes the following columns:

• yr_mon: the year and month of the sale
• agent_id: the agent ID
• agent_total_premium: total premium amount of policies sold by this agent in the month
• overall_avg_monthly_premium: the overall average monthly premium amount (across all agents)
• performance: the difference between the agent's total premium and the overall average monthly premium. This is calculated as (agent_total_premium - overall_avg_monthly_premium)

The result should be sorted by yr_mon and agent_id in ascending order.

Answer:

SELECT 
    CONCAT(EXTRACT(YEAR FROM sale_date), '-', EXTRACT(MONTH FROM sale_date)) as yr_mon,
    agent_id, 
    SUM(premium_amount) over (partition by EXTRACT(YEAR FROM sale_date), EXTRACT(MONTH FROM sale_date), agent_id) as agent_total_premium,
    AVG(premium_amount) OVER (partition by EXTRACT(YEAR FROM sale_date), EXTRACT(MONTH FROM sale_date)) as overall_avg_monthly_premium,
    (SUM(premium_amount) over (partition by EXTRACT(YEAR FROM sale_date), EXTRACT(MONTH FROM sale_date), agent_id) - AVG(premium_amount) OVER (partition by EXTRACT(YEAR FROM sale_date), EXTRACT(MONTH FROM sale_date))) as performance
FROM sales
ORDER BY yr_mon, agent_id;

In this query, window functions are used twice to calculate the total premium of each agent (SUM(premium_amount) OVER (PARTITION BY sale_date, agent_id)), and the average monthly premium(AVG(premium_amount) OVER (PARTITION BY sale_date)). Then, we calculate the performance by subtracting the overall average from the agent's total premium. The key idea here is calculating different metrics over different 'windows' of the data.

Please note that actual performance can depend on factors not considered in this analysis, like policy type or region, so always make sure to choose metrics that accurately reflect performance in your business context.

To practice a similar window function interview problem which uses RANK() on DataLemur's free interactive SQL code editor, solve this Amazon BI Engineer interview question:

SQL Question 2: Employees Earning More Than Their Boss

Suppose you had a table of Prudential Financial employee salary data. Write a SQL query to find all employees who earn more than their direct manager.

Prudential Financial employees Example Input:

Example Output:

This is the output because Olivia Smith earns $8,000, surpassing her manager, William Davis who earns 7,800.

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

Answer:

First, we perform a SELF-JOIN where we treat the first employee table (mgr) as the managers' table and the second employee table (emp) as the employees' table. Then we use a WHERE clause to filter the results, ensuring we only get employees whose salaries are higher than their manager's salary.

SELECT
  emp.employee_id AS employee_id,
  emp.name AS employee_name
FROM employee AS mgr
INNER JOIN employee AS emp
  ON mgr.employee_id = emp.manager_id
WHERE emp.salary > mgr.salary;

If the solution above is hard to understand, you can find a step-by-step solution here: Employee Salaries Higher Than Their Manager.

Check out Prudential Financial's 2023 sustainibility report!

SQL Question 3: Can you define what a database index is, and give some examples of different types of indexes?

{#Question-3}

A database index is a data structure that provides a quick lookup of data in a column or columns of a table.

There are several types of indexes that can be used in a database:

1. Primary index: a unique identifier is used to access the row directly.
2. Unique index: used to enforce the uniqueness of the indexed columns in a table.
3. Composite index: created on multiple columns of a table, is used to speed up the search process for multiple columns
4. Clustered index: determines the physical order of the data in a table

SQL Question 4: Prudential Insurance Policy Filter

As a data analyst at Prudential Financial, you are assigned a task to analyze the customer database. Your task is to extract all customer records where the customer's address_state is either 'FL' or 'NY'. Given that the customer has a 'life_insurance' policy and the policy has been purchased after '2022-01-01'. Here, 'address_state' and 'purchase_date' are columns in customers table and policies table respectively.

customers Example Input:

policies Example Input:

Answer:

SELECT c.customer_id,
       c.first_name,
       c.last_name,
       c.address_state,
       p.policy_type,
       p.purchase_date
FROM customers c
JOIN policies p ON c.customer_id = p.customer_id
WHERE c.address_state IN ('FL','NY')
AND p.policy_type = 'life_insurance'
AND p.purchase_date > '2022-01-01';

The above SQL query joins the 'customers' and 'policies' tables using a 'JOIN' clause on the 'customer_id' column which is common between both tables. It then filters out the records based on the criteria specified in the 'WHERE' clause. The 'IN' keyword is used to match any record in 'address_state' column that is either 'FL' or 'NY'. The query then checks if the 'policy_type' column is equal to 'life_insurance', and if 'purchase_date' is after the date '2022-01-01'. Finally, it projects selected columns from both tables.

SQL Question 5: What does INTERSECT do?

When using INTERSECT, only rows that are identical in both sets will be returned.

For a concrete example, say you were a Data Analyst supporting the Sales Analytics team at Prudential Financial, and data on potential sales leads lived in both Salesforce and Hubspot CRMs. To write a query to analyze leads created before 2023 started, that show up in BOTH CRMs, you would use the INTERSECT command:

SELECT email, job_title, company_id
FROM prudential_financial_sfdc_leads
WHERE created_at < '2023-01-01';

INTERSECT

SELECT email, job_title, company_id
FROM prudential_financial_hubspot_leads
WHERE created_at < '2023-01-01'

SQL Question 6: Calculating Prudential's Digital Ad Click-Through-Rates

Prudential Financial runs a variety of digital ad campaigns to attract prospective customers to their website. Given a table of generated ad impressions and another table of ad clicks by users, calculate the click-through-rate (CTR) for each ad campaign.

CTR is defined as the number of ad clicks divided by the number of ad impressions and it is expressed as a percentage.

Hint: Some ad impressions may not result in ad clicks, so you should use a LEFT JOIN clause when joining the two tables.

Sample tables are provided below:

ad_impressions Example Input:

ad_clicks Example Input:

Answer:

Here is a PostgreSQL query to solve the problem:

SELECT
    ad_impressions.ad_campaign_id,
    COUNT(DISTINCT ad_clicks.click_id)::FLOAT / COUNT(DISTINCT ad_impressions.impression_id) AS click_through_rate
FROM
    ad_impressions
LEFT JOIN
    ad_clicks ON ad_impressions.user_id = ad_clicks.user_id AND ad_impressions.ad_campaign_id = ad_clicks.ad_campaign_id
GROUP BY
    ad_impressions.ad_campaign_id;

This query joins the ad_impressions and ad_clicks tables on the user_id and ad_campaign_id fields. For each distinct ad_campaign_id, it calculates the click-through-rate as the number of unique clicks divided by the number of unique impressions.

To solve a similar problem about calculating rates, solve this TikTok SQL question on DataLemur's interactive coding environment:

SQL Question 7: What's a self-join, and when would you use one?

A self-join is a type of JOIN where a table is joined to itself. To execute a self-join, you must include the table name twice in the FROM clause and assign a different alias to each instance. You can then join the two copies of the table using a JOIN clause, and use a WHERE clause to specify the relationship between the rows.

One common use case for self-joins is to compare data within a single table. For example, you might use a self-join to compare the salaries of employees within the same department, or to identify pairs of products that are frequently purchased together (like in this real Walmart SQL interview question)[https://datalemur.com/questions/frequently-purchased-pairs].

For a more concrete example, imagine you had website visitor data for Prudential Financial, exported from the company's Google Analytics account. To help the web-dev team, you needed to analyze pairs of pages for UX or navigational issues. As part of that analysis, you wanted to produce all pairs of URLs, but needed to exclude pairs where both URLs were the same since that is not a valid pair.

You could use the following self-join:

SELECT page1.url AS page_url, page2.url AS referred_from
FROM google_analytics AS page1
JOIN google_analytics AS page2 ON page1.referrer_id = page2.id
WHERE page1.id <> page2.id;

This query retrieves the url of each page (page1.url) along with the url of the page that referred to it (page2.url). The self-join is performed using the referrer_id field, which identifies the id of the page that referred the visitor to the current page, and excludes any pages that referred to themselves (i.e., data anomalies since pages shouldn't refer to themself).

SQL Question 8: Calculate the Average Policy Premium by State

As a data analyst at Prudential Financial, you've been asked to determine the average policy premium by state for each type of insurance product Prudential offers.

Here's an example of the data you may work with:

policy Example Input:

We want to find out the average annual premium for each product in each state.

The output table should look like this:

Example Output:

Answer:

SELECT 
    state, 
    product_type, 
    AVG(annual_premium) AS average_annual_premium
FROM 
    policy
GROUP BY 
    state, 
    product_type;

This SQL query groups the policy table by the state and product_type, and for each group, it calculates the average (AVG) annual_premium. The result is a new table that shows the average annual premium for each insurance product by state.

SQL Question 9: Analyzing Policy and Claim Data

You are given access to the policy and claim tables in Prudential Financial's database. The policy table contains information on all issued policies, while the claim table holds data on all claims made by policyholders.

Write a SQL query that joins these tables and returns the total claim amount for each policyholder in 2022. Also include the number of claims they have made. Sort the results by total claim amount in descending order.

The policy table:

The claim table:

Answer:

SELECT p.policyholder_id, 
       COUNT(*) AS number_of_claims, 
       SUM(c.claim_amount) AS total_claim_amount
FROM policy p
JOIN claim c
ON p.policy_id = c.policy_id
WHERE EXTRACT(YEAR FROM c.claim_date) = 2022
GROUP BY p.policyholder_id
ORDER BY total_claim_amount DESC;

The query first joins the policy and claim tables on the policy_id field. Next, it limits the records to only those having claims made in 2022. The function EXTRACT(YEAR FROM c.claim_date) is used to grab the year part from the claim_date column.

The grouping operation (GROUP BY) is applied based on the policyholder_id. After that, the COUNT(*) function returns the number of claims for each policyholder, and the SUM operator calculates the total amount of claims. Finally, the ORDER BY statement sorts the result set in descending order by the total claim value.

Since joins come up frequently during SQL interviews, try this Spotify JOIN SQL question:

Prudential Financial SQL Interview Tips

Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Prudential Financial SQL interview is to solve as many practice SQL interview questions as you can! In addition to solving the earlier Prudential Financial SQL interview questions, you should also solve the 200+ SQL coding questions which come from companies like Microsoft, Google, Amazon, and tech startups.

Each SQL question has multiple hints, step-by-step solutions and best of all, there's an online SQL code editor so you can right in the browser run your SQL query answer and have it checked.

To prep for the Prudential Financial SQL interview it is also useful to practice SQL problems from other insurance companies like:

• MetLife SQL questions

• Citigroup SQL questions

• State Farm SQL questions

• Wells Fargo SQL questions

However, if your SQL foundations are weak, forget about jumping right into solving questions – refresh your SQL knowledge with this SQL tutorial for Data Analytics.

This tutorial covers SQL concepts such as using wildcards with LIKE and window functions – both of these come up often in SQL job interviews at Prudential Financial.

Prudential Financial Data Science Interview Tips

What Do Prudential Financial Data Science Interviews Cover?

In addition to SQL query questions, the other types of problems covered in the Prudential Financial Data Science Interview are:

• Statistics and Probability Questions
• Python or R Coding Questions
• Business Sense and Product-Sense Questions
• ML Modelling Questions
• Behavioral Based Interview Questions

How To Prepare for Prudential Financial Data Science Interviews?

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• 201 interview questions sourced from FAANG, tech startups, and Wall Street
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