9 United Airlines SQL Interview Questions (Updated 2024)
At United Airlines data analysts use SQL for analyzing flight data trends for optimal route planning, and managing customer information to improve marketing strategies. Which is why United Airlines evaluates jobseekers on SQL coding interview questions.
To help you study for the United Airlines SQL interview, we'll cover 9 United Airlines SQL interview questions – scroll down to start solving them!
9 United Airlines SQL Interview Questions
SQL Question 1: Identify the Power Customers for United Airlines
United Airlines is keen on identifying their power customers. By definition, a power customer is a passenger who has flown more than 10 flights in the past 30 days. Your task is to provide a list of power customers based on the available data.
Transactions Example Input:
| transaction_id | customer_id | flight_date | flight_number |
|---|---|---|---|
| 1876 | 512 | 07/12/2022 00:00:00 | UA902 |
| 2345 | 755 | 07/15/2022 00:00:00 | UA354 |
| 3221 | 512 | 07/18/2022 00:00:00 | UA354 |
| 1234 | 839 | 07/21/2022 00:00:00 | UA902 |
| 3466 | 512 | 07/25/2022 00:00:00 | UA124 |
Example Output:
| customer_id | flight_count_last_30_days |
|---|---|
| 512 | 3 |
Answer:
This SQL query identifies the power users for United Airlines. It works by counting the number of transactions (flight bookings) by each customer over the last 30 days, and then filters the result to include only those users who have more than ten transactions in this period.
To practice a related customer analytics question on DataLemur's free interactive coding environment, try this Microsoft Teams Power User SQL Interview Question:
SQL Question 2: 2nd Largest Salary
Given a table of United Airlines employee salaries, write a SQL query to find the 2nd highest salary at the company.
Also see the United Digitial Technology Job Board to get more context on the salaries they pay.
United Airlines Example Input:
| employee_id | salary |
|---|---|
| 1 | 2500 |
| 2 | 800 |
| 3 | 1000 |
| 4 | 1200 |
Example Output:
| second_highest_salary |
|---|
| 1200 |
Check your SQL query for this problem and run your code right in DataLemur's online SQL environment:
Answer:
You can find a detailed solution here: 2nd Highest Salary.
SQL Question 3: How would you go about optimizing a slow SQL query?
First things first, figure out why the query is slow! You can use and commands in PostgreSQL to identify any performance bottlenecks. You might discover that your query is inefficient, or that there are many database writes at the same time you are doing a read, or maybe too many people are concurrently running queries on the same database server.
For Data Analyst and Data Science positions, knowing the ins-and-outs of SQL performance tuning is out-of-scope for the SQL interview round. However, knowing that joins are expensive, and indexes can speed up queries, is generally enough of an answer for United Airlines SQL interviews.
SQL Question 4: Analyze Flight Delay Patterns
United Airlines management wants to analyze the daily delay patterns of the flights for optimization. Specifically, they want to note the average delay of each flight per day, along with its ranking in terms of delay among all flights on the same day.
They also want to note the cumulative average delay for each particular flight by day, which is the average delay a given flight has considering all the earlier occurrences of the flight up to the current day.
Please write SQL queries to obtain this information.
For this problem, let's assume United Airlines has a table.
Example Input:
| flight_id | depart_date | delay_minutes |
|---|---|---|
| UA101 | 2021-12-01 | 10 |
| UA105 | 2021-12-01 | 15 |
| UA101 | 2021-12-02 | 20 |
| UA105 | 2021-12-02 | 5 |
| UA101 | 2021-12-03 | 15 |
| UA105 | 2021-12-03 | 25 |
Example Output:
| flight_id | depart_date | avg_daily_delay | delay_rank | cumulative_avg_delay |
|---|---|---|---|---|
| UA101 | 2021-12-01 | 10.00 | 1 | 10.00 |
| UA105 | 2021-12-01 | 15.00 | 2 | 15.00 |
| UA101 | 2021-12-02 | 15.00 | 2 | 15.00 |
| UA105 | 2021-12-02 | 10.00 | 1 | 10.00 |
| UA101 | 2021-12-03 | 15.00 | 2 | 15.00 |
| UA105 | 2021-12-03 | 20.00 | 1 | 16.67 |
Answer:
The below SQL query will give the desired result:
This PostgreSQL query uses window functions.
- The RANK() function is used to give a ranking to the average delay each flight has on a given day among all flights on the same day.
- The AVG() function with a window clause of "PARTITION BY flight_id ORDER BY depart_date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW" is used to calculate the rolling average delay for each flight up to the current day.
- The two uses of the AVG() function, one for daily average delay and one for cumulative average delay, necessitate the use of two window clauses in the query.
Pro Tip: Window functions are a frequent SQL interview topic, so practice all the window function problems on DataLemur
SQL Question 5: How does and differ?
The clause is used to filter the groups created by the clause. It's similar to the clause, but it is used to specify conditions on the groups created by the clause, rather than on the individual rows of the table.
For example, say you were analyzing salaries for analytics employees at United Airlines:
This query retrieves the total salary for each Analytics department at United Airlines and groups the rows by the specific department (i.e. ""Marketing Analytics"", ""Business Analytics"", ""Sales Analytics"" teams).
The clause then filters the groups to include only United Airlines departments where the total salary is greater than $1 million
SQL Question 6: Flight and Customer Database Design
United Airlines often needs to keep track of various pieces of data about their flights and passengers. They're particularly interested in tracking the following information:
- Flight details, including flight number, departure airport, destination airport and date of the flight
- Customer details, passengers travelling, including name, contact number and Users ID.
- Ticket details: Ticket ID, User ID, Flight ID, Class Type(Economy/Premium/Business), Price, Flight Date
The data needs to be organized in such a way that it's easy for the United Airlines staff to track which passengers are on which flights and what type of ticket they have purchased.
Design database tables in a way that you can perform the following operation:
How many passengers are expected to travel in each type of class from each departure city for a particular date?
Sample Data
| flight_id | flight_number | departure_airport | destination_airport | flight_date |
|---|---|---|---|---|
| 101 | UA1001 | JFK | LAX | 06/08/2022 |
| 102 | UA1040 | JFK | ATL | 06/08/2022 |
| 103 | UA1050 | JFK | FFO | 06/08/2022 |
| 104 | UA1030 | ORD | LAX | 06/08/2022 |
| 105 | UA1020 | ORD | EWR | 06/08/2022 |
| user_id | name | contact_number |
|---|---|---|
| 1 | John Doe | +1234567890 |
| 2 | Jane Doe | +2345678901 |
| 3 | Alex Smith | +3456789012 |
| 4 | Maria Garcia | +4567890123 |
| ticket_id | user_id | flight_id | class_type | price | flight_date |
|---|---|---|---|---|---|
| 201 | 1 | 101 | Economy | 200.00 | 06/08/2022 |
| 202 | 2 | 101 | Premium | 300.00 | 06/08/2022 |
| 203 | 3 | 102 | Business | 500.00 | 06/08/2022 |
| 204 | 4 | 103 | Economy | 200.00 | 06/08/2022 |
Answer:
This query joins the flights and tickets tables on the flight_id. It then counts the number of passengers (tickets) by departure airport and class type for the date '06/08/2022'. This allows to evaluate the number of passengers by flight class and departure airport for the given date.
SQL Question 7: What is the purpose of a primary key in a database?
A primary key is a column or group of columns that uniquely identifies a row in a table. For example, say you had a database of United Airlines marketing campaigns data:
In this United Airlines example, the CampaignID column is the primary key of the MarketingCampaigns table. The constraint ensures that no two rows have the same CampaignID. This helps to maintain the integrity of the data in the table by preventing duplicate rows.
SQL Question 8: Analyzing Click-Through-Conversions for United Airlines
Imagine you work in the digital marketing department of United Airlines. Your team launched several digital marketing campaigns last month. Now, they would like to analyze how these campaigns have performed in terms of click-through and conversions.
Specifically, they want to know the click-through-conversion rates from seeing an advertisement (called an "impression") to clicking on the advertisement (a "click") to purchasing a ticket (a "rail"). For consistency, all the rates should be calculated as "per 1000" impressions or clicks.
Consider the following tables:
Example Input:
| ad_id | launch_date | airline_code |
|---|---|---|
| 101 | 04/15/2022 00:00:00 | UA |
| 102 | 04/18/2022 00:00:00 | UA |
| 103 | 04/20/2022 00:00:00 | DL |
| 104 | 04/22/2022 00:00:00 | UA |
| 105 | 04/25/2022 00:00:00 | AA |
Example Input:
| impression_id | ad_id | impression_time |
|---|---|---|
| 2001 | 101 | 04/15/2022 01:00:00 |
| 2002 | 102 | 04/18/2022 02:00:00 |
| 2003 | 101 | 04/24/2022 01:30:00 |
| 2004 | 103 | 04/28/2022 12:00:00 |
| 2005 | 104 | 04/22/2022 03:00:00 |
Example Input:
| click_id | impression_id | click_time |
|---|---|---|
| 3001 | 2001 | 04/15/2022 01:05:00 |
| 3002 | 2002 | 04/18/2022 02:10:00 |
| 3003 | 2004 | 04/28/2022 12:05:00 |
| 3004 | 2005 | 04/22/2022 03:05:00 |
| 3005 | 2003 | 04/30/2022 08:10:00 |
Example Input:
| sale_id | click_id | sale_time |
|---|---|---|
| 4001 | 3001 | 04/15/2022 01:07:00 |
| 4002 | 3002 | 04/18/2022 02:15:00 |
| 4003 | 3003 | 04/30/2022 08:20:00 |
| 4004 | 3004 | 04/22/2022 03:18:00 |
| 4005 | 3005 | 05/02/2022 01:12:00 |
Answer:
The SQL to solve this problem would look something like this:
The query calculates the click and sales conversion rates per 1000 impressions and clicks respectively. Then, it calculates the average of these rates for 'United Airlines' (code 'UA'). Now, your team can better understand the effectiveness of their digital marketing campaigns.
To solve a similar SQL problem on DataLemur's free interactive coding environment, attempt this SQL interview question asked by Facebook:
SQL Question 9: Average Passengers per Flight
Considering you work for United Airlines, imagine that you have a "flights" table with details about each flight and a "passengers" table with information on each passenger. You are tasked to identify the average number of passengers by flight destination over a certain period.
Example Input:
| flight_id | date | destination |
|---|---|---|
| A1 | 06/18/2022 | New York |
| A2 | 06/18/2022 | Los Angeles |
| A3 | 07/15/2022 | New York |
| A4 | 07/20/2022 | Los Angeles |
| A5 | 07/26/2022 | Miami |
Example Input:
| flight_id | passenger_id |
|---|---|
| A1 | P1 |
| A2 | P2 |
| A1 | P3 |
| A4 | P4 |
| A4 | P5 |
| A5 | P6 |
The question is to find out the average number of passengers per destination for June - July 2022.
Answer:
In the query above, we first join the "flights" table with a subquery that counts the number of passengers for each flight. We only consider flights in June and July 2022. Finally, we group by destination and calculate the average number of passengers for each.
Preparing For The United Airlines SQL Interview
The best way to prepare for a United Airlines SQL interview is to practice, practice, practice.
Beyond just solving the earlier United Airlines SQL interview questions, you should also solve the 200+ SQL questions on DataLemur which come from companies like Amazon, Microsoft, Meta, and smaller tech companies.
Each DataLemur SQL question has hints to guide you, full answers and most importantly, there is an online SQL coding environment so you can right in the browser run your SQL query answer and have it graded.
To prep for the United Airlines SQL interview you can also be wise to practice SQL questions from other airlines like:
However, if your SQL query skills are weak, don't worry about jumping right into solving questions – strengthen your SQL foundations with this interactive SQL tutorial.
This tutorial covers SQL topics like Self-Joins and CASE/WHEN/ELSE statements – both of which pop up often in SQL job interviews at United Airlines.
United Airlines Data Science Interview Tips
What Do United Airlines Data Science Interviews Cover?
Besides SQL interview questions, the other types of questions to practice for the United Airlines Data Science Interview are:
- Experimental Design & AB Testing Questions
- Coding Questions in Python or R
- Product Metrics Interview Questions
- ML Interview Questions
- Behavioral & Resume-Based Questions
How To Prepare for United Airlines Data Science Interviews?
To prepare for United Airlines Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions sourced from companies like Microsoft, Google & Amazon
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