At FLEETCOR Technologies, SQL is used to analyze payment transaction patterns for fraud detection by writing queries to identify anomalies and outliers, as well as to manipulate customer fleet data to tailor financial solutions by joining multiple tables and performing data aggregations. Because of this, FLEETCOR frequently asks SQL questions during interviews for Data Science, Data Engineering, and Data Analytics jobs.
Thus, to help you prep, we've curated 8 FLEETCOR Technologies SQL interview questions – can you solve them?
Given the table , write a SQL query that identifies Fleetcor's top 10 customers based on the total amount spent in 2021.
Each row in the table represents a single transaction, and includes a unique , a representing the customer making the transaction, a in YYYY-MM-DD format, and a representing the amount of the transaction.
transaction_id | customer_id | transaction_date | transaction_amount |
---|---|---|---|
999 | 67292 | 2021-10-08 | 200.00 |
782 | 5902 | 2021-09-11 | 50.00 |
771 | 67292 | 2021-05-18 | 80.00 |
234 | 10132 | 2021-03-26 | 300.00 |
876 | 10132 | 2021-12-05 | 100.00 |
This query is grouping by to calculate the total amount spent by each customer in 2021, which we achieve by using the clause to filter only for transactions from this year. It then orders the resulting rows in descending order based on the and limits the output to the top 10 customers.
To solve a similar VIP customer analysis question on DataLemur's free interactive coding environment, try this Microsoft Azure Cloud SQL Interview Question:
Imagine there was a table of FLEETCOR employee salary data. Write a SQL query to find the 2nd highest salary among all employees.
employee_id | salary |
---|---|
1 | 2500 |
2 | 800 |
3 | 1000 |
4 | 1200 |
second_highest_salary |
---|
1200 |
Write a SQL query for this problem and run your code right in the browser:
You can find a step-by-step solution here: 2nd Highest Salary.
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 FLEETCOR customers and a 2nd table of all purchases made with FLEETCOR. To find all customers who did not make a purchase, you'd use the following
This query returns all rows from the customers table, along with any matching rows from 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 , leaving only customers who have not made a purchase.
Based on the fleet data of FLEETCOR, we want to calculate the monthly fleet utilization rate. Fleet utilization is defined as the average number of days a fleet was rented out in a month. Assume that we have access to data such as fleet_id, rental start and end dates.
As a data analyst, your task is to write a SQL query to calculate fleet utilization over time. For each month, calculate the average number of rental days per fleet. If a fleet is not rented in a given month, its rental days should be considered as 0.
rental_id | fleet_id | start_date | end_date |
---|---|---|---|
101 | 1 | 2022-01-06 | 2022-01-12 |
234 | 1 | 2022-02-15 | 2022-02-25 |
678 | 2 | 2022-01-20 | 2022-01-29 |
950 | 3 | 2022-02-06 | 2022-02-09 |
352 | 1 | 2022-02-02 | 2022-02-04 |
month_year | avg_rental_days |
---|---|
01/2022 | 4.33 |
02/2022 | 6.00 |
This query creates a new group for each month and year and extracts the rental days for each rental by calculating the difference between and . Finally, it uses the AVG() function to take the average of all rental days of the fleets for each month.
Pro Tip: Window functions are a frequent SQL interview topic, so practice all the window function problems on DataLemur
Similar to the and / operators, the PostgreSQL INTERSECT operator combines result sets of two or more statements into a single result set. However, only returns the rows that are in BOTH select statements.
For a concrete example, say you were on the Sales Analytics team at FLEETCOR, and had data on sales leads exported from both HubSpot and Salesforce CRMs in two different tables. To write a query to analyze leads created after 2023 started, that show up in both CRMs, you would use the command:
Suppose you are given a database of all transactions occurred within a specific date range in FLEETCOR's system. The details of each transaction include transaction_id, customer_id, transaction_date, transaction_amount, and payment_status. FLEETCOR wants to analyze the payment behavior of their customers. Write a SQL query to filter down the customer transactions where:
transaction_id | customer_id | transaction_date | transaction_amount | payment_status |
---|---|---|---|---|
101 | 123 | 2022-06-16 | 1200 | Paid |
102 | 245 | 2022-06-25 | 800 | Paid |
103 | 387 | 2022-06-18 | 1500 | Not Paid |
104 | 123 | 2022-06-12 | 2000 | Paid |
105 | 487 | 2022-06-30 | 1100 | Paid |
This query fetches all columns of transactions that took place in June 2022, where the transaction amount was more than 1000, and the payment status is 'Paid'. The 'WHERE' clause is used to filter the records according to the conditions set, WHERE we use the 'AND' operator to meet all conditions simultaneously. If all conditions are met, the record is included in the result set.
In a database, an index is a data structure that improves the speed of data retrieval operations on a database table at the cost of additional writes and the use of more storage space to maintain the index data structure.
There are several types of indexes that can be used in a database:
Suppose you have a table that records all of FLEETCOR's customers, and a table that logs all transactions made by those customers. Write a SQL query to find out the total transaction amount for each customer for the last three months, and also display the customer's name and their email. Assume today's date is July 15, 2023.
customer_id | first_name | last_name | |
---|---|---|---|
102 | John | Doe | johndoe@email.com |
257 | Jane | Smith | janesmith@email.com |
908 | Sam | Brown | sambrown@email.com |
529 | Emma | Johnson | emmajohnson@email.com |
671 | Robert | Williams | robertwilliams@email.com |
trans_id | customer_id | trans_date | amount |
---|---|---|---|
5871 | 102 | 06/08/2023 | 500.00 |
7809 | 257 | 06/10/2023 | 700.00 |
5291 | 908 | 05/18/2023 | 1500.00 |
6350 | 529 | 07/12/2023 | 600.00 |
4510 | 671 | 07/05/2023 | 1200.00 |
2847 | 257 | 07/15/2023 | 800.00 |
This query joins the and tables on the column, and then filters for transactions that occurred in the last three months (from April 15, 2023 to July 15, 2023). The query then groups the results by , and for each group, calculates the total transaction amount and also selects the customer's name and email.
Because join questions come up routinely during SQL interviews, take a stab at this SQL join question from Spotify:
The best way to prepare for a FLEETCOR SQL interview is to practice, practice, practice. Besides solving the earlier FLEETCOR SQL interview questions, you should also solve the 200+ DataLemur SQL Interview Questions which come from companies like Amazon, Microsoft, Meta, and smaller tech companies.
Each SQL question has hints to guide you, step-by-step solutions and crucially, there is an interactive SQL code editor so you can instantly run your SQL query answer and have it graded.
To prep for the FLEETCOR SQL interview it is also useful to solve SQL problems from other fintech companies like:
Learn how FLEETCOR is harnessing the power of AI to drive efficiency and innovation in their operations!
However, if your SQL query skills are weak, forget about diving straight into solving questions – go learn SQL with this DataLemur SQL Tutorial.
This tutorial covers SQL concepts such as HAVING and WHERE with AND/OR/NOT – both of these pop up routinely during FLEETCOR interviews.
In addition to SQL query questions, the other types of questions tested in the FLEETCOR Data Science Interview include:
I believe the optimal way to prepare for FLEETCOR Data Science interviews is to read the book I wrote: Ace the Data Science Interview.
It solves 201 interview questions taken from Facebook, Google & startups. The book's also got a refresher on Stats, SQL & ML. And finally it's vouched for by the data community, which is why it's got over 1000+ 5-star reviews on Amazon.
While the book is more technical in nature, it's also key to prepare for the FLEETCOR behavioral interview. Start by understanding the company's unique cultural values.