Sabre Corporation employees use SQL all the damn time for extracting and analyzing customer booking data and managing their large-scale databases pertaining to travel analytics. Unsurprisingly this is why Sabre Corporation almost always asks SQL coding questions in interviews for Data Science and Data Engineering positions.
Thus, to help you prepare, we've collected 11 Sabre SQL interview questions – can you solve them?
Sabre Corporation is a travel technology company that provides airline booking services among others. Power users for Sabre may be individuals or corporations that make frequent bookings. For this question, let's assume power users are defined as those that have made a large number of bookings over the last 12 months.
Given two tables - , a record of all individual customer profiles, and , a record of all airline bookings made through Sabre - can you write a SQL query to identify power users?
You could identify power users by counting the number of bookings for each customer, sorted in descending order. This would be done by joining the two tables on the customer_id and counting the number of bookings per customer.
This query lists the first and last name of the power users along with the total number of their bookings in the past one year. It does this by joining on customer_id for customers and bookings tables, and by filtering for bookings only within the last year. It then groups the result by customer_id and orders them in descending order of the number of bookings.
To practice a related customer analytics question on DataLemur's free online SQL code editor, try this Microsoft SQL Interview problem:
Suppose you had a table of Sabre Corporation employee salary data. Write a SQL query to find the top 3 highest paid employees within each department.
employee_id | name | salary | department_id |
---|---|---|---|
1 | Emma Thompson | 3800 | 1 |
2 | Daniel Rodriguez | 2230 | 1 |
3 | Olivia Smith | 2000 | 1 |
4 | Noah Johnson | 6800 | 2 |
5 | Sophia Martinez | 1750 | 1 |
8 | William Davis | 6800 | 2 |
10 | James Anderson | 4000 | 1 |
department_id | department_name |
---|---|
1 | Data Analytics |
2 | Data Science |
department_name | name | salary |
---|---|---|
Data Analytics | James Anderson | 4000 |
Data Analytics | Emma Thompson | 3800 |
Data Analytics | Daniel Rodriguez | 2230 |
Data Science | Noah Johnson | 6800 |
Data Science | William Davis | 6800 |
Try this question directly within the browser on DataLemur:
We use the DENSE_RANK() window function to generate unique ranks for each employee's salary within their department, with higher salaries receiving lower ranks. Then, we wrap this up in a CTE and filter the employees with a ranking of 3 or lower.
If the code above is confusing, you can find a detailed solution here: Top 3 Department Salaries.
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 Sabre Corporation'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."
Sabre Corporation is a technology provider to the global travel industry. An important metric for any airline is the average delay time of its flights. Given a table of flight statistics, write a SQL query to calculate the average delay time for each airline each month. This needs to be calculated in two ways: over all flights and then over only delayed flights.
To complicate the question further, the company recently adopted a new regional structure. Now the management wants the delays to be calculated for each airline, per new region.
The flights table looks like this:
flight_id | airline_id | region | flight_date | delay_minutes |
---|---|---|---|---|
1 | AA | America | 01/01/2022 | 15 |
2 | AA | Europe | 01/02/2022 | 0 |
3 | BA | Europe | 01/03/2022 | 20 |
4 | AA | America | 01/04/2022 | 45 |
5 | BA | America | 01/05/2022 | 40 |
This query first extracts the month from using . It then calculates the average delay time for each airline for each month in each region using . To calculate the average delay time only for flights that were actually delayed, it uses a conditional average - . It then groups the results by , , and and orders the output by the same columns.
This kind of query is useful for airlines trying to analyze their performance in terms of delays, which can then help them to improve their operations and improve customer satisfaction. Comparing the two averages can show how severe delays tend to be when they occur.
p.s. Window functions show up pretty often during SQL interviews, so practice the 27+ window function questions on DataLemur
A database index is a data structure that improves the speed of data retrieval operations on a database table.
There are few different types of indexes that can be used in a database:
For a concrete example, say you had a table of Sabre Corporation customer payments with the following columns:
Here's what a clustered index on the column would look like:
A clustered index on the column would determine the physical order of the records in the table based on the . This means that the records with the earliest values would be stored together physically in the table, followed by records with later payment_date values. This speeds up queries that filter or sort the data based on the , as the records are already physically sorted in the table. For example, if you want to retrieve all the payments made in the month of June, the database can use the clustered index to quickly locate and retrieve the desired records without having to search through the entire table.
In a scenario where you work for Sabre Corporation, a technology provider to the global travel industry, you are asked to filter out customer records for a specific condition. You must determine customers who have booked flights in the last six months and have also rented a hotel in the same city, in order to understand booking patterns and optimize marketing efforts.
You are given two tables: and . The table contains the fields , , and . The table contains the fields , and .
customer_id | booking_date | destination_city |
---|---|---|
101 | 2021-06-01 | New York |
102 | 2021-07-15 | Los Angeles |
103 | 2022-01-20 | Chicago |
104 | 2022-02-01 | New York |
105 | 2021-08-10 | Los Angeles |
customer_id | booking_date | city |
---|---|---|
101 | 2021-06-03 | New York |
102 | 2021-07-16 | Los Angeles |
103 | 2022-01-22 | Houston |
104 | 2022-02-02 | Las Vegas |
106 | 2021-08-11 | Los Angeles |
Write a PostgreSQL query to find all customers who made both flight and hotel bookings in the same city within the past six months.
This PostgreSQL query uses an INNER JOIN to combine the and tables based on customer_id and city, and then filters for records where both the flight and hotel bookings were made in the past six months.
Database views are created to provide customized, read-only versions of your data that you can query just like a regular table. So why even use one if they're just like a regular table?
Views are useful for creating a simplified version of your data for specific users, or for hiding sensitive data from certain users while still allowing them to access other data.
As a software solutions provider for the travel industry, Sabre Corporation maintains a database for customer service tickets. For a particular interview, you might be asked:
"What is the average ticket resolution time by category at Sabre Corporation?"
Suppose we have a table, , that keeps track of each ticket created by a customer, the date it was created, the date it was resolved, and the category of the ticket.
ticket_id | created_date | resolved_date | category |
---|---|---|---|
1001 | 2020-01-01 | 2020-01-03 | Software Bug |
1002 | 2020-01-02 | 2020-01-08 | General Query |
1003 | 2020-02-01 | 2020-02-01 | Documentation |
1004 | 2020-02-02 | 2020-02-04 | Software Bug |
1005 | 2020-03-01 | 2020-03-03 | General Query |
We need to compute the average resolution time, which is the difference between the resolution date and the creation date, for each category.
This PostgreSQL query calculates the average number of days to resolve a ticket by category. It uses the function to get the difference in days between the and the . The function then computes the average of these differences for each unique ticket .
To practice a very similar question try this interactive Amazon Server Utilization Time Question which is similar for 'Calculating total time from timestamps' or this Facebook Average Post Hiatus (Part 1) Question which is similar for 'Computing average based on timestamps'.
Sabre Corporation is a technology provider to the global travel industry, offering applications, IT solutions, and consulting in the airline, hotel, and travel agency markets. For the purpose of this question, imagine that we have a SQL table recording the booking details of various airline flights, including the airline itself, the cost of the flight, and the date of booking.
Given the following booking details, find the average cost of a flight for each airline for the month of April.
booking_id | booking_date | airline | flight_cost |
---|---|---|---|
1732 | 04/01/2022 | American Airlines | 200 |
2901 | 04/02/2022 | Delta Airlines | 350 |
3629 | 04/10/2022 | American Airlines | 260 |
4920 | 04/16/2022 | Delta Airlines | 300 |
9326 | 04/28/2022 | American Airlines | 220 |
In PostgreSQL, you would write the following query:
This query analyses the flight_bookings table, grouping together rows by airline and by month which in this case is April 2022. For each group, it calculates the average cost of a flight (using AVG()). The WHERE clause is used to focus on bookings made in April 2022.
Month | airline | Average Flight Cost |
---|---|---|
4 | American Airlines | 226.67 |
4 | Delta Airlines | 325.00 |
The output lists each airline with its average flight cost for the month of April 2022.
A cross-join, also referred to as a cartesian join, is a type of JOIN that returns the cross-product of two tables. In a cross-join, each row from the first table is paired with every row from the second table, resulting in a new table with a row for each possible combination of rows from the two input tables.
For example, say you worked on the Marketing Analytics team at Sabre Corporation, and were tasked to understand what advertising copy (text) goes best with what advertising creative (the photo that gets used along with the text copy).
Here is an example of a cross-join between a table of ad_copy and a table of ad_creative:
You could this query to generate all possible combinations of ad copy and ad creative to help you create more effective ads for Sabre Corporation. Just be careful: if you had 1,000 rows of ad copy and 1,000 different image creatives, the resulting cross-join would have 1 million rows! As a result, it's important to use cross-joins judiciously, and make sure that you're not generating more data than you need to.
Sabre Corporation would often have to search for specific customer records based on some relevant criteria. For example, they might want to find all customers whose names start with a specific set of characters or whose email addresses contain specific strings.
Suppose you have a table with the columns , , , and . Write a SQL query that would find all customers whose starts with 'Jo' and whose ends with 'sabre.com'.
CustomerId | FirstName | LastName | Country | |
---|---|---|---|---|
C01 | James | Smith | jsmith@sabre.com | USA |
C02 | John | Doe | jdoe@sabre.com | UK |
C03 | Jane | Lee | jlee@gmail.com | Canada |
C04 | Jennifer | Mohan | jmohan@hotmail.com | India |
C05 | Joan | Perkins | jperkins@sabre.com | South Africa |
The SQL query to solve the problem is:
This query uses the LIKE keyword and the '%' wildcard to filter the customers based on the specified pattern. 'Jo%' matches any string that starts with 'Jo', and '%@sabre.com' matches any string that ends with '@sabre.com'.
CustomerId | FirstName | LastName | Country | |
---|---|---|---|---|
C02 | John | Doe | jdoe@sabre.com | UK |
C05 | Joan | Perkins | jperkins@sabre.com | South Africa |
In this case, the query filters out the customers whose starts with 'Jo' and ends with 'sabre.com', returning customer ids C02 and C05.
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Sabre Corporation SQL interview is to solve as many practice SQL interview questions as you can! In addition to solving the earlier Sabre Corporation SQL interview questions, you should also solve the 200+ SQL Interview Questions on DataLemur which come from companies like Google, Microsoft and Silicon Valley startups.
Each interview question has multiple hints, step-by-step solutions and best of all, there is an interactive coding environment so you can easily right in the browser your SQL query and have it graded.
To prep for the Sabre Corporation SQL interview you can also be wise to practice SQL questions from other tech companies like:
In case your SQL coding skills are weak, forget about jumping right into solving questions – improve your SQL foundations with this interactive SQL tutorial.
This tutorial covers SQL concepts such as AND/OR/NOT and math functions in SQL – both of these show up frequently during SQL interviews at Sabre Corporation.
Beyond writing SQL queries, the other types of problems covered in the Sabre Corporation Data Science Interview are:
I'm sorta biased, but I think the optimal way to prepare for Sabre Corporation Data Science interviews is to read the book I wrote: Ace the Data Science Interview.
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