# 10 Fidelity National Financial SQL Interview Questions (Updated 2024)

Updated on

June 30, 2024

Fidelity National Financial employees use SQL often for analyzing complex financial datasets for risk assessment. That's why Fidelity National Financial almost always evaluates jobseekers on SQL questions during interviews for Data Science and Data Engineering positions.

Thus, to help you ace the Fidelity National Financial SQL interview, here's 10 Fidelity National Financial SQL interview questions in this blog.

## 10 Fidelity National Financial SQL Interview Questions

### SQL Question 1: Identify VIP Clients at Fidelity National Financial

Fidelity National Financial is a financial services company that caters to a large customer base offering products and services like mortgage, investment and other financial products. The company outlines the importance of recognizing their most engaged and active customers, those who perform high-volume or high-value transactions frequently. As a data analyst, your task is to identify these VIP clients.

For this exercise, assume we have two tables available:

##### Example Input:
1John Doe03/01/2020
2Jane Smith05/29/2020
3William Brown09/12/2020
##### Example Input:
transaction_iduser_idtransaction_datetransaction_value
1106/05/2022120000
2206/10/202225000
3106/18/2022100000
4106/30/2022200000
5307/01/20225000

Write a SQL query to identify the VIP users who have a total transaction_value more than a given threshold (let's say 200000 for this example) within the last six months.

The query starts by joining and tables on . It then filters out transactions that occurred in the last six months. After grouping by , it calculates the sum of transaction values per user and returns users who have a total transaction value more than the given threshold.

To solve a similar VIP customer analysis question on DataLemur's free interactive coding environment, try this Microsoft SQL Interview problem:

### SQL Question 2: Employees Earning More Than Managers

Imagine you had a table of Fidelity National Financial employee salary data. Write a SQL query to find all employees who earn more than their own manager.

#### Fidelity National Financial Example Input:

employee_idnamesalarydepartment_idmanager_id
1Emma Thompson38001
2Daniel Rodriguez2230110
3Olivia Smith800018
4Noah Johnson680028
5Sophia Martinez1750110
8William Davis70002NULL
10James Anderson40001NULL

#### Example Output:

employee_idemployee_name
3Olivia Smith

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

Try this problem directly within the browser on DataLemur:

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

If the code above is confusing, you can find a step-by-step solution with hints here: Highly-Paid Employees.

Check out this job listing for a Full Stack Engineer at Fidelity and see how your skills match up.

### SQL Question 3: How do you determine which records in one table are not present in a second table?

To discover records in one table that are not present in another, you can utilize a and filter out any values in the right-side table.

For example, say you had a table of Fidelity National Financial customers and a 2nd table of all purchases made with Fidelity National Financial. To find all customers who did not make a purchase, you'd use the following

This query fetches all rows from the customers table, along with any rows that match in the purchases table. If there is no matching row in the purchases table, values will be returned for all of the right table's columns. The clause then filters out any rows where the purchases.id column is NULL, leaving only customers who have not made a purchase.

### SQL Question 4: Calculate the monthly average transactions

Given a table of transactions made by Fidelity National Financial, write a SQL query to calculate the average transactions per client, associated with each specific product, on a monthly basis.

Assume the table has the following columns:

• (INT): a unique ID for each transaction
• (INT): a unique ID for each client
• (DATE): the date a transaction has been made
• (INT): a unique ID for each product
• (DECIMAL): the amount of each transaction
##### Sample Input:
transaction_idclient_idtransaction_dateproduct_idamount
101454102/05/2020600012500
102548702/25/2020600013000
103657202/05/2020800021000
104454103/15/2020600012000
105548703/25/2020800024000
106657203/15/2020600012500
107454104/05/2020800021500
108548704/25/2020600013500
109657204/15/2020800021500

The output of this query should return the month, product_id and the average transaction amount per client for the said month and product.

##### Example Output:
monthproduct_idavg_transaction
2600012750.00
2800021000.00
3600012250.00
3800024000.00
4800021500.00
4600013500.00

This PostgresSQL statement extracts the month from each transaction date, groups by the month and product_id, and calculates the average of the transaction amount per each group. It then orders the results by the fields: month and product_id.

For more window function practice, try this Uber SQL Interview Question on DataLemur's online SQL coding environment:

### SQL Question 5: Can you describe the different types of joins in SQL?

In SQL, a join is used to combine rows from different tables based on a shared key or set of keys, resulting in a single merged table of data.

There are four distinct types of JOINs: , , , and .

(INNER) JOIN: Retrieves records that have matching values in both tables involved in the join.

LEFT (OUTER) JOIN: Retrieves all the records/rows from the left and the matched records/rows from the right table.

RIGHT (OUTER) JOIN: Retrieves all the records/rows from the right and the matched records/rows from the left table.

FULL (OUTER) JOIN: Retrieves all the records where there is a match in either the left or right table.

### SQL Question 6: Filter Customer Records

As a data analyst at Fidelity National Financial, you are given a task to filter out the customer records whose 'active_status' is true and the 'account_balance' is greater than 5000. The records should also include customers who have their 'loan_status' as 'Default'.

##### Example Input:
customer_idfirst_namelast_nameaccount_balanceactive_statusloan_status
1JohnDoe7000TrueGood Standing
3EmilyWatson6000TrueGood Standing
4RobertSmith2000TrueDefault

Note:

In this table:

• active_status (True indicates the account is active, False indicates the account is not active)
• loan_status (Default indicates the customer's loan is in default, Good Standing indicates the customer is not in default)
##### Expected Output:
customer_idfirst_namelast_nameaccount_balanceactive_statusloan_status
1JohnDoe7000TrueGood Standing
3EmilyWatson6000TrueGood Standing
4RobertSmith2000TrueDefault

This PostgreSQL query uses a combination of WHERE, AND, OR commands to filter the data. It checks if a customer's active_status is True and account_balance is greater than 5000. It also includes entries where the loan_status is 'Default'. The result will be a list of customers with the specified conditions.

### SQL Question 7: What is normalization?

Normalizing a database involves dividing a large table into smaller and more specialized ones, and establishing relationships between them using foreign keys. This reduces repetition, resulting in a database that is more adaptable, scalable, and easy to maintain. Additionally, it helps to preserve the accuracy of the data by reducing the likelihood of inconsistencies and problems.

### SQL Question 8: Find the Average Number of Insurance Policies Sold Per Agent

At Fidelity National Financial, one important metric would be the average number of insurance policies sold by each agent. You have access to a 'policies' table, where each row represents an insurance policy sold, and a 'agents' table, where each row represents an agent in the company.

Write a SQL query to find the average number of insurance policies sold by each agent.

##### Sample Input:
policy_idagent_idsell_datepolicy_type
892100101/22/2023 00:00:00Car
925200201/24/2023 00:00:00Life
852600301/30/2023 00:00:00Home
871000101/31/2023 00:00:00Life
896300402/01/2023 00:00:00Car
##### Sample Input:
agent_idagent_namehire_date
001James01/02/2020 00:00:00
002Samantha03/15/2019 00:00:00
003Robert04/30/2021 00:00:00
004Linda07/28/2022 00:00:00

This query first groups the policies by agent, counting the number of policies sold per agent. Then it joins this result with the agents table on the agent_id, to get the agent_name for each agent. Finally, it finds the average number of policies sold per agent by grouping the joined result by agent_name. The AVG aggregate function is used to compute the average number of policies sold.

To practice a very similar question try this interactive Twitter Histogram of Tweets Question which is similar for requiring aggregation per user or this Amazon Average Review Ratings Question which is similar for calculating averages within groups.

### SQL Question 9: Searching for Customer Records with Specific Pattern

Fidelity National Financial often needs to filter out customer records based on specific patterns in their communication. Imagine you are given a customer table with columns including , , and . The company would like you to find all the records where contains the keyword "insurance". How would you achieve this using SQL?

##### Example Input:

Here is the SQL query in PostgreSQL you would use to filter out the required customer records:

This SQL query will provide all columns ( means all) from the where the contains the string . The character in the LIKE predicate is a wildcard character that matches any string of zero or more characters. As a result, the query would return the rows for John Doe, Bob Martin and Charlie Brown since those are the messages which contain the word "insurance".

### SQL Question 10: What do stored procedures do, and when would you use one?

Stored procedures are a lot like functions in programming. They're used to encapsulate and organize business logic into one unit of code, and they can accept multiple input parameters and return multiple output values.

For example, if you were a Data Analyst at Fidelity National Financial working on a HR analytics project, you might create a stored procedure to calculate the average salary for a given department:

To call this stored procedure and find the average salary for the Data Analytics department you'd write the following query:

### Fidelity National Financial SQL Interview Tips

The best way to prepare for a Fidelity National Financial SQL interview is to practice, practice, practice. In addition to solving the above Fidelity National Financial SQL interview questions, you should also solve the 200+ DataLemur SQL Interview Questions which come from companies like Google, Microsoft and Silicon Valley startups.

Each exercise has hints to guide you, full answers and crucially, there is an interactive coding environment so you can easily right in the browser your query and have it executed.

To prep for the Fidelity National Financial SQL interview you can also be a great idea to solve SQL problems from other mortgage & insurance companies like:

But if your SQL coding skills are weak, forget about jumping right into solving questions – improve your SQL foundations with this DataLemur SQL Tutorial.

This tutorial covers topics including how window functions work and RANK vs. DENSE RANK – both of these pop up often in Fidelity National Financial SQL assessments.

### Fidelity National Financial Data Science Interview Tips

#### What Do Fidelity National Financial Data Science Interviews Cover?

Besides SQL interview questions, the other question categories to practice for the Fidelity National Financial Data Science Interview include:

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