11 LPL Financial SQL Interview Questions (Updated 2024)
Data Analysts & Data Scientists at LPL Financial writes SQL queries to analyze financial data trends, including market performance and economic indicators, as well as managing client data for portfolio optimization, such as identifying optimal asset allocations. That is why LPL Financial asks prospective hires SQL interview problems.
To help prep you for the LPL Financial SQL interview, here’s 11 LPL Financial Holdings SQL interview questions – scroll down to start solving them!
11 LPL Financial Holdings SQL Interview Questions
SQL Question 1: Identifying VIP Customers at LPL Financial
At LPL Financial, our most crucial customers are the ones who execute the most transactions with the highest value within a given period(in this case, a calendar month). As a part of our analytics team, we would like you to write a SQL query that identifies these VIP customers. For this question, the 'VIP' designation is given to customers who execute more than 10 transactions or whose average transaction value exceeds $100,000 in a month.
Example Input:
| transaction_id | customer_id | transaction_date | transaction_value |
|---|---|---|---|
| 1001 | 4567 | 08/05/2022 | 50000 |
| 1002 | 4568 | 08/10/2022 | 60000 |
| 1003 | 4567 | 08/15/2022 | 100000 |
| 1004 | 4569 | 08/15/2022 | 50000 |
| 1005 | 4567 | 08/20/2022 | 50000 |
| 1006 | 4569 | 08/20/2022 | 150000 |
| 1007 | 4568 | 08/25/2022 | 50000 |
| 1008 | 4569 | 08/25/2022 | 20000 |
| 1009 | 4568 | 08/30/2022 | 50000 |
| 1010 | 4569 | 08/30/2022 | 20000 |
Answer:
This query groups transactions by customer and by month, and counts the total number of transactions for each customer for each month, along with the average transaction value for the month. The HAVING clause filters the results to only include customers who have either more than 10 transactions in a month or an average transaction value of over $100,000, effectively identifying the VIP customers.
To practice a similar customer analytics SQL question where you can code right in the browser and have your SQL code instantly executed, try this Walmart Labs SQL Interview Question:
SQL Question 2: 2nd Largest Salary
Given a table of LPL Financial employee salary information, write a SQL query to find the 2nd highest salary at the company.
LPL Financial Example Input:
| employee_id | salary |
|---|---|
| 1 | 2500 |
| 2 | 800 |
| 3 | 1000 |
| 4 | 1200 |
Example Output:
| second_highest_salary |
|---|
| 1200 |
Solve this problem directly within the browser on DataLemur:
Answer:
You can find a detailed solution here: 2nd Highest Salary.
SQL Question 3: Can you explain the distinction between a clustered and a non-clustered index?
Clustered indexes have a special characteristic in that the order of the rows in the database corresponds to the order of the rows in the index. This is why a table can only have one clustered index, but it can have multiple non-clustered indexes.
The main difference between clustered and non-clustered indexes is that the database tries to maintain the order of the data in the database to match the order of the corresponding keys in the clustered index. This can improve query performance as it provides a linear-access path to the data stored in the database.
SQL Question 4: Calculate the Monthly Dividend Yield
For an investment company like LPL Financial, it might be important to analyze how their financial products, particularly their stocks, are performing over time. One common metric used is the monthly dividend yield. Let's say the company wants to calculate the average monthly dividend yield for each stock over a particular period.
Given a table that tracks each dividend payment and a table that tracks the end of month (EOM) price for each stock, write a SQL query to calculate the average monthly dividend yield for each stock. Dividend Yield can be calculated as (Dividend/Price)*100.
Consider the and tables defined as follows:
Example Input:
| dividend_id | stock_id | date | dividend |
|---|---|---|---|
| 221 | 33 | 04/30/2022 | 0.52 |
| 332 | 21 | 05/30/2022 | 0.45 |
| 443 | 33 | 05/30/2022 | 0.55 |
| 554 | 21 | 06/30/2022 | 0.50 |
| 665 | 33 | 06/30/2022 | 0.60 |
Example Input:
| price_id | stock_id | date | price |
|---|---|---|---|
| 7766 | 33 | 04/30/2022 | 50.00 |
| 8877 | 21 | 05/31/2022 | 35.00 |
| 9988 | 33 | 05/31/2022 | 55.00 |
| 11099 | 21 | 06/30/2022 | 40.00 |
| 12100 | 33 | 06/30/2022 | 60.00 |
Answer:
This query first joins the and table on and month of the date fields. It then groups the results by the month and and calculates the average monthly yield for each group using the given formula. The result is sorted by date and for easy analysis.
Pro Tip: Window functions are a frequent SQL interview topic, so practice every window function problem on DataLemur
SQL Question 5: What is the purpose of a primary key in a database?
The primary key of a table is a column or set of columns that serves as a unique identifier for each row. It ensures that all rows are distinct and does not allow null values.
For example, say you had stored some Facebook ad campaign data that LPL Financial ran:
The column uniquely identifies each row in the table, and the PRIMARY KEY constraint ensures that no two rows have the same . This helps to maintain the integrity of the data in the table by preventing duplicate rows.
The primary key is also an important part of the table because it allows you to easily identify and reference specific campaigns in your Facebook Ad data. You can use it to join to other tables in the database, such as a table containing data on the results of the campaigns.
SQL Question 6: Filter Customers Based on Financial Status and Activity
LPL Financial is interested in analyzing their customer base. Given a database of customers with details such as account status (active or inactive), balance amount, account creation date, and the financial advisor assigned to each customer, write a query to filter down to the customers who:
- Have an status
- Have been with LPL Financial for over a year
- Have a balance of less than $5000
- Are not assigned to the financial advisor named 'John Smith'
Please format the table as such:
Example Input:
| customer_id | status | balance | advisor_name | account_created |
|---|---|---|---|---|
| 5682 | ACTIVE | 4500.00 | John Smith | 06/10/2020 |
| 7835 | ACTIVE | 10000.00 | Mary Johnson | 05/15/2020 |
| 4321 | INACTIVE | 3000.00 | Jane Doe | 04/21/2020 |
| 1298 | ACTIVE | 3500.00 | Mark Davis | 08/08/2019 |
Answer:
This query selects the columns from the table where the is , the is less than $5000, the is not 'John Smith', and the date is before the date one year ago from today. The function is used to truncate the current date to the year so that we can retrieve the date exactly 1 year ago.
SQL Question 7: Can you explain what / SQL commands do?
The / operator is used to remove to return all rows from the first SELECT statement that are not returned by the second SELECT statement.
Note that is available in PostgreSQL and SQL Server, while MINUS is available in MySQL and Oracle (but don't worry about knowing which DBMS supports which exact commands since LPL Financial interviewers aren't trying to trip you up on memorizing SQL syntax).
For a tangible example of in PostgreSQL, suppose you were doing an HR Analytics project for LPL Financial, and had access to LPL Financial's contractors and employees data. Assume that some employees were previously contractors, and vice versa, and thus would show up in both tables.
You could use operator to find all contractors who never were a employee using this query:
SQL Question 8: Find the Average Investment Amount per Customer
As an analyst at LPL Financial, your task is to find the average investment amount per customer. You are given a database with two tables, and .
table contain details about the customers and table contain details about customers' investments.
Sample Data:
| customer_id | first_name | last_name |
|---|---|---|
| 101 | John | Doe |
| 102 | Jane | Doe |
| 103 | Alice | Smith |
| 104 | Bob | Johnson |
Sample Data:
| investment_id | customer_id | amount |
|---|---|---|
| 201 | 101 | 5000 |
| 202 | 101 | 8000 |
| 203 | 102 | 7000 |
| 204 | 103 | 1000 |
| 205 | 103 | 2000 |
| 206 | 104 | 6000 |
Your solution should return the customer details along with their average investment amount.
Answer:
This query joins the and tables on and groups them on the same id and customer names. The function is applied to the field to provide the average investment amount per customer.
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for calculating highest revenue per category or this Stripe Repeated Payments Question which is similar for analyzing same user transaction records.
SQL Question 9: Finding the Maximum Total Assets Managed by Advisors Per Office
LPL Financial employs various advisors in different offices who each manage a certain amount of assets. We want to find the maximum total amount of assets managed by the advisors in each office for a particular year. Assume that each advisor could manage multiple portfolios but the total assets for each advisor is recorded annually.
Here's a sample data table we'll be using:
Example Input:
| advisor_id | office_id | year | total_assets_managed |
|---|---|---|---|
| 001 | 100 | 2018 | 1200 |
| 002 | 100 | 2018 | 1500 |
| 003 | 101 | 2018 | 900 |
| 004 | 101 | 2018 | 1100 |
| 005 | 100 | 2019 | 2000 |
| 006 | 101 | 2019 | 2100 |
Example Output:
| office_id | year | max_total_assets_managed |
|---|---|---|
| 100 | 2018 | 2700 |
| 101 | 2018 | 2000 |
| 100 | 2019 | 2000 |
| 101 | 2019 | 2100 |
Answer:
Here's a PostgreSQL query that could answer the question:
This query creates a subquery that sums up the total assets managed by the advisors in each office for each year, then finds the maximum total amount in each office per year.
The result is a table that shows you the office_id, the year, and the maximum total amount of assets managed in that office for that year, which provides a yearly comparison and office comparison of the company's asset management.
SQL Question 10: What are the similarities and difference between relational and NoSQL databases?
While both types of databases are used to store data (obviously), there's some key differences in how they store and organize data.
Relational databases try to represent the world into neat little tables, with rows and columns. Non-relational (NoSQL) databases use a variety of data models to represent data, including document, key-value, columnar, and graph storage formats.
While the exact types of NoSQL databases is beyond the scope of a Data Analyst and Data Scientist SQL interview at LPL Financial, it's good to know that companies generally choose to use NoSQL databases:
- when dealing with unstructured or semi-structured data
- when the database needs to be scaled horizontally easily
- when the data is non-relational (like storing social network data which makes more sense in a graph format)
SQL Question 11: Obtain the Average Account Balance per Financial Advisor
LPL Financial, a finance company, wants you to analyze their database and provide vital insights. Your task is to write a SQL query to calculate the average account balance per financial advisor. The two tables of interest are the table and the table. The table logs the account balance for each customer, and each customer is linked to a financial advisor via the advisor's ID.
Example Input:
| advisor_id | advisor_name | region |
|---|---|---|
| 1001 | John Doe | West |
| 1002 | Jane Smith | East |
| 1003 | Richard Roe | South |
| 1004 | Mary Major | North |
| 1005 | Alan Minor | Midwest |
Example Input:
| customer_id | advisor_id | account_balance |
|---|---|---|
| 2001 | 1001 | 12000.00 |
| 2002 | 1001 | 15000.00 |
| 2003 | 1002 | 9000.00 |
| 2004 | 1003 | 8000.00 |
| 2005 | 1004 | 22000.00 |
| 2006 | 1005 | 10000.00 |
| 2007 | 1005 | 12000.00 |
Example Output:
| advisor_name | avg_account_balance |
|---|---|
| John Doe | 13500.00 |
| Jane Smith | 9000.00 |
| Richard Roe | 8000.00 |
| Mary Major | 22000.00 |
| Alan Minor | 11000.00 |
Answer:
This query joins the table with the table on the field. It then groups the data by , and for each group, it calculates the average account balance (). The result is then sorted in descending order by to display the financial advisors with the highest average account balance first.
Since join questions come up so often during SQL interviews, try an interactive Spotify JOIN SQL question:
Preparing For The LPL Financial SQL Interview
The key to acing a LPL Financial SQL interview is to practice, practice, and then practice some more! In addition to solving the above LPL Financial SQL interview questions, you should also solve the 200+ DataLemur SQL Interview Questions which come from companies like FAANG (FB, Apple, Amazon, Netflix, Google).
Each exercise has multiple hints, step-by-step solutions and most importantly, there is an interactive SQL code editor so you can right online code up your query and have it checked.
To prep for the LPL Financial SQL interview it is also useful to solve SQL questions from other insurance companies like:
- Genworth SQL questions
- Gallagher US SQL questions
- FM Global SQL questions
- First American Financial SQL questions
Discover how LPL Financial is leveraging AI to help investors thrive in a rapidly changing market!
But if your SQL skills are weak, don't worry about going right into solving questions – improve your SQL foundations with this DataLemur SQL tutorial.
This tutorial covers SQL topics like sorting results with ORDER BY and RANK vs. DENSE RANK – both of which pop up routinely during LPL Financial SQL interviews.
LPL Financial Holdings Data Science Interview Tips
What Do LPL Financial Data Science Interviews Cover?
In addition to SQL query questions, the other types of problems to prepare for the LPL Financial Data Science Interview include:
- Probability & Statistics Questions
- Python Pandas or R Coding Questions
- Analytics and Product-Metrics Questions
- Machine Learning Questions
- Behavioral Based Interview Questions
How To Prepare for LPL Financial Data Science Interviews?
To prepare for the LPL Financial Data Science interview have a firm understanding of the company's culture and values – this will be key to acing the behavioral interview. For technical interviews prepare by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from tech companies like Netflix, Google, & Airbnb
- A Refresher covering Stats, SQL & ML
- Amazing Reviews (1000+ 5-star reviews on Amazon)