9 Spirit Airlines SQL Interview Questions (Updated 2024)
At Spirit Airlines, SQL is used all the time for analyzing flight data for optimization and managing customer booking information for personalized marketing strategies. Because of this, Spirit Airlines almost always evaluates jobseekers on SQL query questions during interviews for Data Analytics, Data Science, and Data Engineering jobs.
Thus, to help you study, we've collected 9 Spirit Airlines SQL interview questions – can you answer each one?
9 Spirit Airlines SQL Interview Questions
SQL Question 1: Passenger Load Factor Analysis
As a data analyst for Spirit Airlines, you are tasked with analyzing the passenger load factor for the airline's various routes. The passenger load factor is a key metric used to measure the occupancy of flights. It is calculated as the number of passengers on a flight divided by the total available seats on that flight.
You have a dataset containing information about the flights and passengers. In the table, each row represents a single flight, with the , , and each flight has. In the table, each row represents a passenger, with , , and .
Write a SQL query that returns, for each route and month, the average passenger load factor. Also, rank the routes based on the average passenger load factor within each month in descending order.
Example Input:
| flight_id | route | total_seats |
|---|---|---|
| 1 | NYC-LAX | 180 |
| 2 | MIA-ATL | 150 |
| 3 | NYC-LAX | 180 |
| 4 | MIA-ATL | 150 |
| 5 | LAX-SEA | 80 |
Example Input:
| passenger_id | flight_id | flight_date |
|---|---|---|
| 1 | 1 | 2022-01-05 |
| 2 | 1 | 2022-01-05 |
| 3 | 2 | 2022-01-06 |
| 4 | 2 | 2022-01-06 |
| 5 | 2 | 2022-01-06 |
| 6 | 5 | 2022-01-07 |
| 7 | 5 | 2022-01-07 |
Answer:
This query first calculates the passenger load factor for each route and month by grouping on these two values. Then, it calculates the average passenger load factor for each route using a window function. Finally, it uses another window function to rank the routes based on the average load factor within each month.
To practice a similar window function interview problem which uses RANK() on DataLemur's free interactive coding environment, solve this Amazon SQL Interview Question:
SQL Question 2: Department Salaries
Suppose there was a table of Spirit Airlines employee salary data, along with which department they were in. Write a query to compare the average salary of employees in each department to the company's average salary. Return the comparison result as 'higher', 'lower', or 'same' for each department. Display the department ID, payment month (in MM-YYYY format), and the comparison.
Write a SQL query for this problem interactively on DataLemur:
The solution is LONG – 30+ lines of SQL. You can find a detailed solution with hints here: Department Salaries.
Also check out their latest earning releases, to see some additional data!
SQL Question 3: Can you describe the difference between a clustered and a non-clustered index?
Both clustered and non-clustered indexes are used to improve query performance, but they have different characteristics.
A clustered index determines the physical order of the data rows in a table, while a non-clustered index does not.This means that a table can have only one clustered index, but it can have multiple non-clustered indexes.
In terms of query performance, a clustered index is usually faster for searches that return a large number of records, while a non-clustered index is faster for searches that return a small number of records. However, updates to a clustered index are slower, as they require the data rows to be physically rearranged, while updates to a non-clustered index are faster, as they only require the index data structure to be updated.
SQL Question 4: Filter Spirit Airlines Passengers by Multiple Conditions
Spirit Airlines wants to know the number of passengers transporting on each day who have chosen a specific service(Level), belong to their frequent flier program and are from a particular region. The condition is that the region should be 'North America' and the chosen service level should be 'Gold' or 'Platinum'. Use the following tables and for the task.
Example Input:
| passenger_id | passenger_name | region | service_level | frequent_flier |
|---|---|---|---|---|
| 101 | John | North America | Gold | Yes |
| 102 | Marry | South America | Platinum | No |
| 103 | Tina | North America | Gold | Yes |
| 104 | Mark | North America | Silver | No |
| 105 | Nick | Asia | Gold | Yes |
Example Input:
| flight_id | passenger_id | date |
|---|---|---|
| 501 | 101 | 06/08/2022 |
| 502 | 102 | 06/10/2022 |
| 503 | 103 | 06/18/2022 |
| 504 | 104 | 07/26/2022 |
| 505 | 105 | 07/05/2022 |
Example Output:
| date | passengers_count |
|---|---|
| 06/08/2022 | 1 |
| 06/18/2022 | 1 |
Answer:
In the above query, we are joining the and tables on . Then we filter the records where the region is 'North America', the service level is either 'Gold' or 'Platinum', and are part of the frequent flier program. We then group the results by date and count the number of passengers for each date.
SQL Question 5: What are some different ways you can identify duplicate rows in a table?
One way to find duplicate records in a table is by using , and then seeing which groups have more than one occurence:
Another way is by using the operator:
SQL Question 6: Average Delayed Flights
For Spirit Airlines, you are asked to find the average delay time in minutes of all flights leaving from a specific airport on a given day.
Example Input:
| flight_id | origin_airport | destination_airport | departure_date | delay_min |
|---|---|---|---|---|
| FK001 | LAX | JFK | 06/11/2022 06:30:00 | 10 |
| FK002 | LAX | ORD | 06/11/2022 08:00:00 | 5 |
| FK003 | LAX | MIA | 06/11/2022 10:00:00 | 15 |
| FK004 | JFK | LAX | 06/11/2022 12:00:00 | 0 |
| FK005 | LAX | JFK | 06/11/2022 14:00:00 | 20 |
Example Output:
| depart_date | origin | avg_delay |
|---|---|---|
| 06/11/2022 | LAX | 12.5 |
Answer:
In this question, we calculate the average flight delay time in minutes for all flights leaving from a specific airport on a given day. The AVG function calculates the average value of a numeric column. The DATE function extracts the date from a timestamp value. The GROUP BY statement groups rows that have the same values in specified columns. The WHERE clause is used to filter records.
To practice a very similar question try this interactive Amazon Average Review Ratings Question which is similar for calculating averages based on time or this Alibaba Compressed Mean Question which is similar for finding a mean and rounding the results.
SQL Question 7: What distinguishes a left join from a right join?
Both left and right joins in SQL allow you to combine data from different tables based on a shared key or set of keys. For a concrete example of the difference between these two join types, say you had sales data exported from Spirit Airlines's Salesforce CRM stored in a PostgreSQL database, and had access to two tables: and .
: retrieves all rows from the left table (in this case, the sales table) and any matching rows from the right table (the table). If there is no match in the right table, NULL values will be returned for the right table's columns.
- : retrieves all rows from the right table (in this case, the customers table) and any matching rows from the left table (the sales table). If there is no match in the left table, NULL values will be returned for the left table's columns.
SQL Question 8: Retrieve Spirit Airlines Passengers Holding Premier Status
Spirit Airlines is interested in analyzing their customer database, specifically they would like to identify the passengers who hold a Premier status, signified by their passenger ID containing the string "PREM". The company will use this data to send these individuals targeted promotions.
Tables:
Example Input:
| passenger_id | name | status | |
|---|---|---|---|
| PREM4567 | john@example.com | John Doe | Premier |
| ECON7891 | jane@example.com | Jane | Economy |
| PREM8523 | bob@example.com | Bob | Premier |
| ECO1234 | alice@example.com | Alice | Economy |
Example Output:
| passenger_id | name | status | |
|---|---|---|---|
| PREM4567 | john@example.com | John Doe | Premier |
| PREM8523 | bob@example.com | Bob | Premier |
Answer:
This query uses the SQL clause to filter the table for records where the starts with "PREM". In SQL, the percent sign (%) is used as a wildcard character to represent any possible character that might appear before or after the characters specified. In this case, 'PREM%' represents any string that starts with "PREM".
SQL Question 9: Analysis of Frequent Flyer Member Behavior
Suppose you are a data analyst for Spirit Airlines and tasked with the following:
You have been given two tables - one table contains member information for Spirit Airlines' frequent flyer program (titled ) and another table contains flight booking data (). You have been asked to write a SQL query that joins these two tables and gives a count of the total number of bookings each member has made.
Here are the tables:
Example Input:
| member_id | first_name | last_name | join_date | |
|---|---|---|---|---|
| 112 | John | Doe | johndoe@email.com | 01/01/2020 |
| 113 | Jane | Smith | janesmith@email.com | 06/10/2019 |
| 114 | Sam | Johnson | samjohnson@email.com | 11/15/2021 |
| 115 | Emma | Brown | emmabrown@email.com | 01/18/2020 |
| 116 | Robert | Davis | robertdavis@email.com | 03/20/2019 |
Example Input:
| booking_id | flight_id | member_id | booking_date |
|---|---|---|---|
| 2011 | 9001 | 112 | 01/02/2020 |
| 2012 | 9002 | 113 | 01/05/2020 |
| 2013 | 9003 | 112 | 01/10/2020 |
| 2014 | 9004 | 115 | 02/01/2020 |
| 2015 | 9005 | 116 | 02/05/2020 |
| 2016 | 9006 | 112 | 02/10/2020 |
Answer:
A SQL query to solve this problem in PostgreSQL would look as follows:
The SQL query above first joins the table with the table on the basis of the field, which is common in both tables. This join allows us to analyze the booking behavior of each member. The function is then used to count the number of bookings each member has made, as recorded in the 'flight_bookings' table. The results are grouped by , , and to get a count for each individual member. Finally, the results are ordered in descending order based on the total number of bookings ().
Since joins come up frequently during SQL interviews, take a stab at an interactive Spotify JOIN SQL question:
Preparing For The Spirit Airlines SQL Interview
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Spirit Airlines SQL interview is to solve as many practice SQL interview questions as you can!
In addition to solving the earlier Spirit Airlines SQL interview questions, you should also solve the 200+ SQL questions on DataLemur which come from companies like Microsoft, Google, and Facebook.
Each interview question has multiple hints, full answers and crucially, there's an interactive SQL code editor so you can right online code up your SQL query answer and have it executed.
To prep for the Spirit Airlines SQL interview it is also useful to practice interview questions from other airlines like:
In case your SQL query skills are weak, forget about jumping right into solving questions – go learn SQL with this interactive SQL tutorial.
This tutorial covers SQL concepts such as joining a table to itself and HAVING – both of which come up routinely in SQL interviews at Spirit Airlines.
Spirit Airlines Data Science Interview Tips
What Do Spirit Airlines Data Science Interviews Cover?
Beyond writing SQL queries, the other types of problems covered in the Spirit Airlines Data Science Interview are:
- Statistics and AB Testing Questions
- Python Pandas or R Coding Questions
- Product Data Science Interview Questions
- Machine Learning Questions
- Behavioral & Resume-Based Questions
How To Prepare for Spirit Airlines Data Science Interviews?
To prepare for Spirit Airlines Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions sourced from Facebook, Google, & Amazon
- a refresher on Stats, SQL & ML
- over 1000+ 5-star reviews on Amazon