9 NLV Financial SQL Interview Questions (Updated 2024)

Updated on

June 30, 2024

At NLV Financial, SQL is used frequently for analyzing financial trends in customer data and optimizing database operations for more efficient business performance. That's why NLV Financial frequently asks SQL questions during interviews for Data Science, Analytics, and & Data Engineering jobs.

Thus, to help you prep, we've curated 9 NLV Financial SQL interview questions – how many can you solve?

NLV Financial SQL Interview Questions

9 NLV Financial SQL Interview Questions

SQL Question 1: Analyzing Client Transaction Amounts

NLV Financial, a fintech firm, wants to analyze its clients' transactions within specific time windows. The objective is to calculate the rolling average transaction amount for each client over the last 'x' transactions (where 'x' might be 3, 5, 10, etc.).

The and tables structure is described as below:

Example Input:
Example Input:
100112306/08/2022 00:00:00500.01
100212307/15/2022 00:00:00752.99
100312308/22/2022 00:00:00320.25
100445607/10/2022 00:00:00698.52
100545607/26/2022 00:00:00421.76
100645608/14/2022 00:00:00649.80

Can you please write a SQL query to return a table with each client along with the rolling average of their last 3 transactions.

Expected Output:
12308/22/2022 00:00:00524.42
45608/14/2022 00:00:00590.03

Note: The average amount is rounded to the nearest cent


This PostgreSQL query uses the window function with the frame to calculate the rolling average of the amount over the last 3 transactions for each client, partitioned by and ordered by . The function is used to round the average values to the nearest cent. The WHERE clause ensures that only the results for the most recent transaction for each client are returned.

Pro Tip: Window functions are a popular SQL interview topic, so practice all the window function problems on DataLemur

DataLemur Window Function SQL Questions

Read about NLV Financial's recent financial statements and see how the company has performed over the years.

SQL Question 2: Second Highest Salary

Given a table of NLV Financial employee salaries, write a SQL query to find the 2nd highest salary at the company.

NLV Financial Example Input:


Example Output:


Check your SQL query for this interview question interactively on DataLemur:

2nd Highest Salary SQL Interview Question


You can find a detailed solution with hints here: 2nd Highest Salary.

SQL Question 3: How does differ from just ?

The operator merges the output of two or more statements into a single result set. It ignores duplicated rows, and makes sure each row in the result set is unique.

For a concrete example, say you were a Data Analyst at NLV Financial working on a Marketing Analytics project. If you needed to get the combined result set of both NLV Financial's Google and Facebook ads you could execute this SQL query:

The operator works in a similar way to combine data from multiple statements, but it differs from the operator when it comes to handling duplicate rows. Whereas filters out duplicates (so if the same ad_name is run on both Facebook and Google, it only shows up once), outputs duplicate rows.

NLV Financial SQL Interview Questions

SQL Question 4: Client Investment Portfolio Analysis

NLV Financial is a global investment company that manages portfolios for its clients. To serve its customers better, the company is interested in identifying the most heavily invested sectors by each client. The request is to identify for each client, which sector they have invested the most in.

Consider the following two tables, and .

Example Input:
Example Input:

The table logs the amount each client invests in different sectors. The table contains the investor's details. The aim is to write a SQL query that will list for each investor, the sector in which they have invested the most money.


In PostgreSQL, you would use the below query:

This query first calculates the total investment for each client in every sector in the CTE. Then it identifies the maximum investment per client in the CTE. The main query then joins the , , and tables to get the client name, the sector they've most heavily invested in, and the total amount invested in that sector.

SQL Question 5: What's a stored procedure, and why use one?

Stored procedures are like functions in Python – they can accept input params and return values, and are used to encapsulate complex logic.

For example, if you worked as a Data Analyst in support of the Marketing Analytics team at NLV Financial, a common task might be to find the conversion rate for your ads given a specific time-frame. Instead of having to write this query over-and-over again, you could write a stored procedure like the following:

To call this stored procedure, you'd execute the following query:

SQL Question 6: Calculate the Average Account Balance for Different Account Types

As a Database Analyst at NLV Financial, your task is to calculate the average account balance of customers for different account types. The company has two main types of accounts - 'Saving' and 'Checking'. You will be provided with two tables, and . The table has an , (either 'Saving' or 'Checking'), and . The table has a and , linking a customer with his/her account.

Example Input


Example Input


Example Output



In this query, an inner JOIN is made between and based on the . Then, GROUP BY is used on to separate 'Checking' and 'Saving' types. Finally, AVG function is used on the balance of grouped types to get the average balance.

To practice a very similar question try this interactive Amazon Average Review Ratings Question which is similar for computing averages from a type of entity or this McKinsey 3-Topping Pizzas Question which is similar for Calculating cost for variations of a product.

SQL Question 7: What does the SQL keyword do?

The clause in SQL allows you to select records that are unique, eliminating duplicates.

For example, if you had a table of NLV Financial employees, and wanted to see what are all the unique job titles that currently worked at the company, you could write the following query:

SQL Question 8: Analyzing Click-Through Conversion Rate

NLV Financial is interested in analyzing the click-through conversion rates of their financial products. Specifically, they want to examine the rate at which users who view a product add it to their cart.

You are given two tables:

This table records when a user views a product:

35232053000704/08/2022 10:30:00
73243079028304/08/2022 11:00:00
65126501300704/08/2022 11:30:00
85314229028304/08/2022 14:00:00
47105503000704/08/2022 14:10:00

This table records when a user adds a product to their cart:

51722053000704/08/2022 10:45:00
37203079028304/08/2022 11:10:00
43954229028304/08/2022 14:15:00
44826501300704/08/2022 18:00:00
51795503000704/08/2022 19:00:00

Write a SQL query that calculates the overall click-through conversion rate, defined as the proportion of views that led to a product being added to the cart.


This query joins and on , , and makes sure the product is added to the cart after it has been viewed.

It then calculates the conversion ratio by counting the distinct number of additions and dividing this by the distinct number of views. Since the conversion rate is usually a number less than 1, we cast the numerator as float to get a decimal number.

To practice a related problem on DataLemur's free interactive coding environment, attempt this Meta SQL interview question: Meta SQL interview question

SQL Question 9: Customers with multiple transactions and their account type

The marketing team at NLV Financial wants to conduct a study on the spending habits of their customers based on the account type they own. They are interested in customers who have made more than 5 transactions. Therefore, you are tasked to write a SQL query that will return the names of the customers who have more than 5 transactions, the type of account they own, and the total number of transactions they have made.

Sample tables:

1011001200.0006/20/2022 00:00:00
1021001150.5006/23/2022 00:00:00
1031002250.2509/15/2022 00:00:00
1041001150.0007/17/2022 00:00:00
1051003100.7506/26/2022 00:00:00
1061001200.0008/20/2022 00:00:00
1071002250.2510/15/2022 00:00:00


This SQL query joins the , , and tables based on . The statement organizes the data by , , and . The clause is used to filter the groups by customers with more than 5 transactions. This query will return the names of the customers who have more than 5 transactions, their account type, and the total number of transactions they have made.

Since joins come up frequently during SQL interviews, take a stab at an interactive SQL join question from Spotify: Spotify JOIN SQL question

Preparing For The NLV Financial SQL Interview

The key to acing a NLV Financial SQL interview is to practice, practice, and then practice some more! In addition to solving the above NLV Financial SQL interview questions, you should also solve the 200+ tricky sql questions on DataLemur which come from companies like FAANG (FB, Apple, Amazon, Netflix, Google). DataLemur Questions

Each exercise has multiple hints, detailed solutions and crucially, there is an online SQL coding environment so you can right online code up your SQL query and have it executed.

To prep for the NLV Financial SQL interview you can also be wise to practice interview questions from other banking & finanacial services companies like:

However, if your SQL foundations are weak, forget about diving straight into solving questions – refresh your SQL knowledge with this SQL interview tutorial.

SQL interview tutorial

This tutorial covers things like math functions and WHERE with AND/OR/NOT – both of these show up routinely in SQL job interviews at NLV Financial.

NLV Financial Data Science Interview Tips

What Do NLV Financial Data Science Interviews Cover?

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

NLV Financial Data Scientist

How To Prepare for NLV Financial Data Science Interviews?

The best way to prepare for NLV Financial Data Science interviews is by reading Ace the Data Science Interview. The book's got:

  • 201 Interview Questions from Facebook, Google & startups
  • A Refresher on SQL, Product-Sense & ML
  • Great Reviews (1000+ reviews, 4.5-star rating)

Ace the Data Science Interview by Nick Singh Kevin Huo