9 Delta Air Lines SQL Interview Questions (Updated 2024)
Data Science, Data Engineering and Data Analytics employees at Delta Air Lines use SQL to analyze passenger data and monitor ticket sales for predicting future revenue trends. That's why Delta Air Lines evaluates jobseekers on SQL interview questions.
To help you practice for the Delta Air Lines SQL interview, we've curated 9 Delta Air Lines SQL interview questions in this article.
9 Delta Air Lines SQL Interview Questions
SQL Question 1: Identify VIP Frequent Flyers in Delta Air Lines
Delta Air Lines is interested in identifying their most valuable customers, who they refer to as VIP Frequent Flyers. A VIP Frequent Flyer is defined as a customer who has not only taken a large number of flights with Delta, but also consistently books Business or First Class tickets.
Using the passenger_flights database with the columns , , , (Economy, Business, First), , , create a SQL query that identifies all VIP Frequent Flyers over the past year.
Example Input:
| flight_id | customer_id | flight_date | class_booked | flight_origin | flight_destination |
|---|---|---|---|---|---|
| 1 | 123 | 2021/07/20 | First | NYC | LA |
| 2 | 234 | 2021/07/24 | Economy | LA | NYC |
| 3 | 345 | 2021/07/26 | Business | NYC | LA |
| 4 | 123 | 2021/07/28 | First | LA | NYC |
| 5 | 123 | 2021/07/30 | Business | NYC | LA |
Answer:
This query first creates a set of flights from the past year where customers booked a First Class or Business Class ticket. From this set, it groups the results by . It then filters out customers who have taken less than 10% of the total number of flights, to isolate only the most frequent flyers. The result set is then ordered in descending order by the total number of flights taken.
To practice a similar customer analytics SQL question where you can code right in the browser and have your SQL code automatically checked, try this Walmart Labs SQL Interview Question:
SQL Question 2: 2nd Highest Salary
Given a table of Delta Air Lines employee salary data, write a SQL query to find the 2nd highest salary at the company.
Delta Air Lines Example Input:
| employee_id | salary |
|---|---|
| 1 | 2500 |
| 2 | 800 |
| 3 | 1000 |
| 4 | 1200 |
Example Output:
| second_highest_salary |
|---|
| 1200 |
Write a SQL query for this interview question interactively on DataLemur:
Answer:
You can find a detailed solution with hints here: 2nd Highest Salary.
SQL Question 3: Can you describe the role of the constraint and provide an example of a situation where it might be applied?
The CHECK constraint is used to enforce rules on the data in a specific column. If a row is inserted or updated with data that does not follow the CHECK constraint's rule, the operation will fail.
For example, say you had a marketing analytics database that stores ad campaign data from Delta Air Lines's Google Analytics account.
Here's what some constraints could look like:
The CHECK constraint is used in the above example to make sure that the "budget" and "cost_per_click" fields contain only positive values. This helps to maintain the validity and integrity of the data in the context of ad campaigns.
The CHECK constraint can also be used to enforce other specific conditions on data. For example, you can use it to ensure that the "start_date" for each ad campaign is earlier than the "end_date."
SQL Question 4: Analyze Flight Delays using Window Functions
Delta Air Lines wants to analyze the delay patterns per flight during the last month in order to improve its service. As an analyst, you are asked to determine the average delay of each flight over the last week, with the delay defined as the actual departure time minus the scheduled departure time.
Consider the following table:
Example Input:
| flight_number | scheduled_departure | actual_departure |
|---|---|---|
| DL101 | 2022-09-01 08:00:00 | 2022-09-01 08:17:00 |
| DL102 | 2022-09-01 12:00:00 | 2022-09-01 12:05:00 |
| DL101 | 2022-09-02 08:00:00 | 2022-09-02 08:00:00 |
| DL102 | 2022-09-02 12:00:00 | 2022-09-02 12:07:00 |
| DL101 | 2022-09-03 08:00:00 | 2022-09-03 09:00:00 |
| DL102 | 2022-09-03 12:00:00 | 2022-09-03 12:20:00 |
Assume that all times are in local time and that all dates fall within the last week. You can also assume that is always later than or equal to .
Answer:
This query uses PostgreSQL window functions to calculate the average delay for each flight. The expression calculates the delay of each flight in minutes, and the clause groups these results by to calculate the average delay for each flight.
Note: Here, is used to convert interval data type into seconds. We then divide by 60 to convert this into minutes.
Pro Tip: Window functions are a popular SQL interview topic, so practice every window function problem on DataLemur
SQL Question 5: What is the difference between SQL operators ‘BETWEEN’ and ‘IN’?
The and operators are both used to filter data based on certain criteria, but they work in different ways. is used to select values within a range, while is used to select values that match a list.
For instance, if you have a table called that contains the salary of each employee, along with which country they reside in, you could use the operator to find all employees who make between 160k:
To find all employees that reside in France and Germany, you could use the operator:
SQL Question 6: Average Flight Delay Time
Delta Air Lines wants to understand the duration of delays for their flights. Your primary task is to design tables to represent the key entities involved: Flights, Airports, and Flight Delays. Then the company wants you to calculate the average delay time per origin for the flights that take off from any particular airport.
Example Input:
Example Input:
Example Input:
Answer:
This query links the , , and tables using JOIN operations. It then groups the results by the origin airport and calculates the average delay time for each group.
SQL Question 7: Can you describe the meaning of a constraint in SQL in layman's terms?
Constraints are just rules your DBMS has to follow when updating/inserting/deleting data.
Say you had a table of Delta Air Lines products and a table of Delta Air Lines customers. Here's some example SQL constraints you'd use:
NOT NULL: This constraint could be used to ensure that certain columns in the product and customer tables, such as the product name and customer email address, cannot contain NULL values.
UNIQUE: This constraint could be used to ensure that the product IDs and customer IDs are unique. This would prevent duplicate entries in the respective tables.
PRIMARY KEY: This constraint could be used to combine the NOT NULL and UNIQUE constraints to create a primary key for each table. The product ID or customer ID could serve as the primary key.
FOREIGN KEY: This constraint could be used to establish relationships between the Delta Air Lines product and customer tables. For example, you could use a foreign key to link the customer ID in the customer table to the customer ID in the product table to track which products each customer has purchased.
CHECK: This constraint could be used to ensure that certain data meets specific conditions. For example, you could use a CHECK constraint to ensure that Delta Air Lines product prices are always positive numbers.
DEFAULT: This constraint could be used to specify default values for certain columns. For example, you could use a DEFAULT constraint to set the customer registration date to the current date if no value is provided when a new customer is added to the database.
SQL Question 8: Average Passengers per Flight for Delta Air Lines
You're analyzing flight data for Delta Air Lines. Write a query that determines the average number of passengers per flight over the past year. Assume each record in the table represents a flight and its details, including the , , and the final for each flight.
Example Input:
| id | flight_number | flight_date | passengers_count |
|---|---|---|---|
| 1 | DL200 | 2021-08-01 | 120 |
| 2 | DL210 | 2021-09-25 | 150 |
| 3 | DL330 | 2021-11-01 | 100 |
| 4 | DL200 | 2021-08-02 | 140 |
| 5 | DL210 | 2021-09-26 | 160 |
| 6 | DL330 | 2021-11-02 | 120 |
Example Output:
The output would be a single number indicating the average passengers per flight.
Answer:
Explanation:
This query calculates the average of the column (which represents the number of passengers on each flight) from the table. The function automatically takes care of the division by the number of flights. The keyword renames the column in the output for easier interpretation.
To practice a very similar question try this interactive Alibaba Compressed Mean Question which is similar for calculating average from a dataset or this Amazon Highest-Grossing Items Question which is similar for finding top entries in a dataset.
SQL Question 9: Calculate Click-Through-Rates For Delta Air Lines
Delta Air Lines often send out email marketing campaigns to customers promoting flight rates. Each email includes various flight deals, and a given user might click on multiple deals before finally making a booking. We want to calculate the click-through-rate (CTR) as part of our analysis of the effectiveness of these campaigns. The CTR is calculated as the number of clicks on a deal that led to a booking divided by the total number of unique clicks on deals.
Example Input:
| email_id | user_id | click_date | deal_id |
|---|---|---|---|
| 2341 | 101 | 03/08/2022 | 65321 |
| 3612 | 102 | 03/09/2022 | 58901 |
| 1459 | 103 | 03/09/2022 | 69921 |
| 2341 | 101 | 03/10/2022 | 65321 |
| 1459 | 104 | 03/11/2022 | 69921 |
Example Input:
| booking_id | user_id | booking_date | deal_id |
|---|---|---|---|
| 7217 | 101 | 03/10/2022 | 65321 |
| 8902 | 102 | 03/11/2022 | 58901 |
Answer:
This query uses two CTEs (total_clicks and booked_clicks) to initially group and count the amount of unique clicks and bookings for each deal respectively. Then, we left join the two CTEs based on deal_id and calculate the CTR by dividing the num_bookings by num_clicks, eventually listing out the CTR for each deal. The use of in the denominator guards against division by zero.
To practice a similar problem about calculating rates, try this TikTok SQL question within DataLemur's interactive SQL code editor:
Preparing For The Delta Air Lines SQL Interview
The best way to prepare for a Delta Air Lines SQL interview is to practice, practice, practice.
Besides solving the earlier Delta Air Lines SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like Google, Uber, and Microsoft.
Each SQL question has multiple hints, detailed solutions and most importantly, there is an online SQL code editor so you can easily right in the browser your SQL query and have it executed.
To prep for the Delta Air Lines SQL interview you can also be useful to solve SQL questions from other airlines like:
But if your SQL skills are weak, forget about diving straight into solving questions – strengthen your SQL foundations with this free SQL tutorial.
This tutorial covers SQL concepts such as CTEs and different types of joins – both of these show up routinely during Delta Air Lines SQL interviews.
Delta Air Lines Data Science Interview Tips
What Do Delta Air Lines Data Science Interviews Cover?
In addition to SQL query questions, the other types of problems to practice for the Delta Air Lines Data Science Interview include:
- Experimental Design & AB Testing Questions
- Coding Questions in Python or R
- Product-Sense and Business Case Questions
- ML Modelling Questions
- Resume-Based Behavioral Questions
I also recommend taking a look at Delta's Corporate Stats and Facts to understand what the company prioritizes and their goals for the future.
How To Prepare for Delta Air Lines Data Science Interviews?
The best way to prepare for Delta Air Lines Data Science interviews is by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from FAANG & startups
- A Refresher on Python, SQL & ML
- Great Reviews (1000+ reviews, 4.5-star rating)