9 Pacific Life SQL Interview Questions (Updated 2024)
Pacific Life employees write SQL queries for analyzing customer data, including policyholder demographics and claims history, to predict risk patterns, as well as generating reports on policyholder trends for strategic decision-making, like optimizing policy pricing and underwriting. That is why Pacific Life includes SQL questions in interviews for Data Analytics, Data Science, and Data Engineering jobs.
As such, to help you prep for the Pacific Life SQL interview, here's 9 Pacific Life SQL interview questions in this article.
9 Pacific Life SQL Interview Questions
SQL Question 1: Analyzing Policyholder Behavior Using Window Functions
Pacific Life Insurance Company is interested in understanding the behavior of its policyholders over time. They are particularly interested in knowing the average age of policyholders who purchase a life insurance policy per quarter, including the most recent quarter in their database.
You are given a table with the following schema:
Example Input:
| policyholder_id | purchase_date | policy_id | age |
|---|---|---|---|
| 121 | 08/01/2022 | 70001 | 35 |
| 259 | 05/02/2022 | 85502 | 42 |
| 321 | 15/03/2022 | 70001 | 29 |
| 159 | 30/04/2022 | 85502 | 46 |
| 193 | 14/06/2022 | 85502 | 33 |
Your task is to construct a SQL query that calculates the average age of policyholders who bought a policy in each quarter (grouped by quarter) with the help of SQL window functions. The output should be sorted by quarter.
Example Output:
| quarter | avg_age |
|---|---|
| Q1 2022 | 35.50 |
| Q2 2022 | 39.50 |
Answer:
This query first truncates the purchase date to its respective quarter so that all purchases made within the same quarter are grouped together. Then, for each of these quarterly groups, it computes the average age of the policyholders. The window function over the partition defined by allows us to calculate the average separately for each quarter. Finally, the query sorts the results by quarter.
Pro Tip: Window functions are a popular SQL interview topic, so practice every window function problem on DataLemur
SQL Question 2: Department Salaries
You're given a table of Pacific Life employee and department salaries. Write a SQL query to find departments where the departments average salary is higher than the company's average salary.
You can solve this interview question and run your code right in DataLemur's online SQL environment:
The answer is LONG – 30+ lines of SQL. You can find a detailed solution here: Department Salaries.
SQL Question 3: In the context of a database transaction, what does ACID 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 4: Policy Duration and Premium Information
Pacific Life needs insight into their policyholders' data to optimize their business strategy. They want to know how long policyholders tend to hold onto insurance policies and how much premium they pay on average. They require this information segregated by policy type.
Assume we have two tables, and .
Example Input:
| policyholder_id | name | date_of_birth |
|---|---|---|
| 234 | Alice | 01/31/1975 |
| 789 | Bob | 06/11/1983 |
| 312 | Charlie | 11/22/1991 |
Example Input:
| policy_id | policyholder_id | policy_type | start_date | end_date | annual_premium |
|---|---|---|---|---|---|
| 10234 | 234 | Life | 01/01/2020 00:00:00 | 12/31/2020 00:00:00 | 1000 |
| 20345 | 234 | Health | 01/01/2021 00:00:00 | 12/31/2022 00:00:00 | 1800 |
| 30456 | 789 | Auto | 06/15/2018 00:00:00 | 06/14/2020 00:00:00 | 800 |
| 40567 | 789 | Health | 01/01/2021 00:00:00 | 1200 | |
| 50678 | 312 | Life | 01/01/2021 00:00:00 | 2000 |
Answer:
Here's a PostgreSQL query to calculate the average policy duration and annual premium for each policy type.
Explanation:
The query selects from the table and groups by the . The Duration of each policy is calculated as the difference between and . The function in PostgreSQL is then used to get the average policy duration and average annual premium for each policy type.
SQL Question 5: Can you explain the distinction between a left and right join?
A join in SQL combines rows from two or more tables based on a shared column or set of columns. To demonstrate the difference between a and , say you had a table of Pacific Life orders and Pacific Life customers.
LEFT JOIN: A retrieves all rows from the left table (in this case, the Orders table) and any matching rows from the right table (the Customers table). If there is no match in the right table, NULL values will be returned for the right table's columns.
RIGHT JOIN: A retrieves all rows from the right table (in this case, the Customers table) and any matching rows from the left table (the Orders table). If there is no match in the left table, NULL values will be returned for the left table's columns.
SQL Question 6: Filter Customer Records
Pacific Life wants to get a list of customers from their database who are younger than 25 years old or older than 60 years old, live in California, and have a life insurance policy with Pacific Life.
Additionally, they are especially interested in customers who have opened policies in the year 2021.
Can you write a SQL query to generate this list?
Example Input:
| customer_id | age | state | policy_type | policy_start_date |
|---|---|---|---|---|
| 101 | 22 | California | Life Insurance | 2020-10-15 |
| 102 | 61 | California | Life Insurance | 2021-02-15 |
| 103 | 33 | New York | Life Insurance | 2022-01-27 |
| 104 | 45 | California | Car Insurance | 2021-05-20 |
| 105 | 23 | California | Life Insurance | 2021-07-15 |
Expected Output:
| customer_id | age | state | policy_type | policy_start_date |
|---|---|---|---|---|
| 102 | 61 | California | Life Insurance | 2021-02-15 |
| 105 | 23 | California | Life Insurance | 2021-07-15 |
Answer:
This query begins by selecting the relevant columns from the 'customers' table. Filters are then applied in order to return only the rows that meet the specified conditions. Specifically, we filter for customers who are either younger than 25 years old or older than 60 years old, who live in California, and who have a life insurance policy with Pacific Life. We also extract the year part from the 'policy_start_date' and compare it to the year 2021, to specifically filter for policies that were opened in that year.
SQL Question 7: What's a cross-join, and why are they used?
A cross-join, also known as a cartesian join, is a JOIN that produces the cross-product of two tables. In a cross-join, each row from the first table is matched with every row from the second table, resulting in a new table with a row for each possible combination of rows from the two input tables.
Suppose you were building a Neural Network ML model, that tried to score the probability of a customer buying a Pacific Life product. Before you started working in Python and Tensorflow, you might want to do some Exploratory Data Analysis (EDA) in SQL, and generate all pairs of customers and Pacific Life products.
Here's a cross-join query you could use to find all the combos:
Cross-joins are great for generating all possible combinations, but they can also create really big tables if you're not careful. For example, if you had 10,000 potential customers, and Pacific Life had 500 different product SKUs, you'd get 5 million rows as a result!!
SQL Question 8: Determining Click-Through Conversion Rates
Given the campaign and clicks data from Pacific Life's latest online marketing initiatives, write a SQL query to determine the click-through conversion rate (i.e., the percentage of impressions that lead to a click) for each campaign. For this exercise, use the tables and .
table:
| impression_id | user_id | timestamp | campaign_id |
|---|---|---|---|
| 101 | 1 | 2022-06-08 06:30:00 | 4001 |
| 102 | 2 | 2022-06-08 07:00:00 | 5001 |
| 103 | 1 | 2022-06-08 07:30:00 | 4001 |
| 104 | 3 | 2022-06-08 08:00:00 | 5001 |
| 105 | 2 | 2022-06-08 08:30:00 | 4001 |
table:
| click_id | user_id | timestamp | campaign_id |
|---|---|---|---|
| 201 | 1 | 2022-06-08 06:31:00 | 4001 |
| 202 | 2 | 2022-06-08 07:01:00 | 5001 |
| 203 | 1 | 2022-06-08 07:31:00 | 4001 |
Answer:
Here is your PostgreSQL query:
This query calculates the click-through conversion rate for each campaign by dividing the number of unique users who click (from the table) by the number of unique users who have an impression (from the table). We use a to connect both tables on and . In case there are no impressions for a particular campaign (to avoid dividing by zero), we use the function.
To solve a related problem on DataLemur's free interactive coding environment, solve this Facebook SQL Interview question:
SQL Question 9: Analyze Customer Purchase Data
Imagine you are a data analyst at Pacific Life, an insurance company. You are tasked to write a SQL query to find the total number of policies sold and the total premium for those policies by each agent each year from the and tables.
Here's the structure for the and tables.
Example Input:
| agent_id | agent_name | region |
|---|---|---|
| 101 | John Smith | West |
| 102 | Jane Doe | East |
| 103 | Dave Brown | North |
| 104 | Linda Green | South |
Example Input:
| policy_id | agent_id | customer_id | sale_date | premium |
|---|---|---|---|---|
| 201 | 101 | 801 | 2018-02-15 | 1500 |
| 202 | 102 | 802 | 2019-03-10 | 2000 |
| 203 | 101 | 803 | 2018-06-20 | 1800 |
| 204 | 103 | 804 | 2020-07-25 | 1000 |
| 205 | 101 | 805 | 2020-09-30 | 1500 |
Answer:
This query joins the table with the table on . It then groups the results by agent name and the sale year (extracted from the sale date). For each grouping, it counts the number of policies () and sums the premium value (). The results are ordered by agent name and sale year.
Since joins come up routinely during SQL interviews, practice this Spotify JOIN SQL question:
Pacific 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. Beyond just solving the above Pacific Life SQL interview questions, you should also solve the 200+ tricky sql questions on DataLemur which come from companies like Netflix, Google, and Amazon.
Each SQL question has multiple hints, step-by-step solutions and crucially, there's an interactive SQL code editor so you can easily right in the browser your SQL query answer and have it executed.
To prep for the Pacific Life SQL interview you can also be wise to solve SQL questions from other insurance companies like:
- Farmers Insurance SQL questions
- Principal Financial SQL questions
- AmFam SQL questions
- Guardian Life SQL questions
Stay informed about the latest news and developments from Pacific Life and see how they're making a difference in the insurance industry!
In case your SQL coding skills are weak, forget about jumping right into solving questions – strengthen your SQL foundations with this SQL interview tutorial.
This tutorial covers topics including window functions and filtering strings based on patterns – both of which come up frequently in Pacific Life SQL interviews.
Pacific Life Data Science Interview Tips
What Do Pacific Life Data Science Interviews Cover?
Besides SQL interview questions, the other types of problems tested in the Pacific Life Data Science Interview include:
- Statistics & Probability Questions
- Python or R Programming Questions
- Analytics and Product-Metrics Questions
- ML Modelling Questions
- Behavioral Questions based on Pacific Life cultural values
How To Prepare for Pacific Life Data Science Interviews?
To prepare for Pacific 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 refresher on SQL, AB Testing & ML
- over 1000+ reviews on Amazon & 4.5-star rating
Don't ignore the behavioral interview – prep for it using this Behavioral Interview Guide for Data Scientists.