Data Scientists, Analysts, and Data Engineers at Lumen Technologies code up SQL queries all the damn time for work. They use SQL for analyzing network traffic data for predictions, and shaping dataset frameworks for the telecommunication model updates. For this reason Lumen Technologies frequently asks jobseekers SQL coding interview questions.
To help you prep for the Lumen Technologies SQL interview, we've collected 8 Lumen SQL interview questions can you solve them?
Given a dataset which contains data usage of each user of Lumen Technologies on a daily basis in the past year. Write an SQL query to calculate the total monthly data usage for each user using SQL window function.
Below are the markdown-formatted sample tables you can use to formulate your query:
user_id | usage_date | data_usage(GB) |
---|---|---|
5756 | 01/05/2022 | 10 |
9321 | 01/15/2022 | 15 |
5756 | 01/20/2022 | 20 |
9321 | 02/02/2022 | 18 |
5756 | 02/10/2022 | 14 |
5756 | 02/20/2022 | 12 |
9321 | 02/25/2022 | 17 |
5756 | 03/01/2022 | 15 |
9321 | 03/05/2022 | 22 |
5756 | 03/15/2022 | 18 |
month | user_id | total_data_usage(GB) |
---|---|---|
1 | 5756 | 30 |
1 | 9321 | 15 |
2 | 5756 | 26 |
2 | 9321 | 35 |
3 | 5756 | 33 |
3 | 9321 | 22 |
In this query, we first use the function to extract the month from the column for each user. Next, we use a window function to sum the data usage of each user for each month. Finally, the results are grouped by user_id and month and then ordered by user_id and month.
p.s. Window functions show up pretty frequently during SQL interviews, so practice the 27+ window function questions on DataLemur
Given a table of Lumen Technologies employee salaries, 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 |
Test your SQL query for this interview question interactively on DataLemur:
You can find a detailed solution with hints here: 2nd Highest Salary.
A cross-join, also known 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 that has a row for each possible combination of rows from the two input tables.
For example, say you worked on the Marketing Analytics team at Lumen Technologies, and needed to understand what advertising copy (text) goes best with what advertising creative (the photo that gets posted with the advertisement 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 Lumen Technologies. 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 Database Design specialist at Lumen Technologies, your job is to design and set up a performance database that would keep track of all the services provided my Lumen, its usage data by the customers and the location details. Create tables , , and . Define relationships between these tables and design columns as needed considering performance and analysis.
Alongside, consider that the Management wants to get insights about the usage of services by location at the end of each month. Significantly, they want to find out the Total Usage and Number of Users per Service for each Location.
Providing Sample data for tables -
service_id | service_name |
---|---|
101 | Internet |
102 | TV |
103 | VoIP |
record_id | user_id | service_id | usage_time_hours | record_date |
---|---|---|---|---|
1 | 1001 | 101 | 3 | 2022-01-01 |
2 | 1002 | 102 | 4 | 2022-01-01 |
3 | 1001 | 103 | 1 | 2022-01-01 |
4 | 1002 | 101 | 2 | 2022-01-02 |
5 | 1003 | 102 | 3 | 2022-02-01 |
location_id | user_id | city | state | zip |
---|---|---|---|---|
1 | 1001 | Denver | Colorado | 80202 |
2 | 1002 | Boulder | Colorado | 80302 |
3 | 1003 | Denver | Colorado | 80202 |
This query first converts the date to 'YYYY-MM' format to get the month of the record. It uses JOIN to connect with and tables. The result comprises of month, service_name, city, state, number_of_users, and total_usage_hours. The result is grouped by Month, service_name, city, and state and finally sorted by Month, service_name, and total_usage_hours.
Both the and window functions are used to access a row at a specific offset from the current row.
However, the function retrieves a value from a row that follows the current row, whereas the function retrieves a value from a row that precedes the current row.
Say you had a table of salary data for Lumen Technologies employees:
name | salary |
---|---|
Amanda | 130000 |
Brandon | 90000 |
Carlita | 80000 |
You could use the function to output the salary of each employee, along with the next highest-paid employee:
This would yield the following output:
name | salary | next_salary |
---|---|---|
Amanda | 130000 | 90000 |
Brandon | 90000 | 80000 |
Carlita | 80000 | NULL |
Swapping for would get you the salary of the person who made just more than you:
name | salary | next_salary |
---|---|---|
Amanda | 130000 | NULL |
Brandon | 90000 | 130000 |
Carlita | 80000 | 90000 |
Lumen Technologies is interested in understanding the effectiveness of their digital marketing strategies. Specifically, they want to understand the ratio of users that click on their digital ads and then go on to add a product to their cart.
We have two tables. The table marks each instance of a user clicking on one of Lumen Technologies' digital ads. The table records each time a user adds a product to their virtual cart.
click_id | user_id | click_date |
---|---|---|
1001 | 123 | 08/01/2022 |
1002 | 456 | 08/01/2022 |
1003 | 789 | 08/01/2022 |
1004 | 123 | 08/01/2022 |
1005 | 987 | 08/02/2022 |
add_id | user_id | add_date |
---|---|---|
5001 | 123 | 08/01/2022 |
5002 | 456 | 08/02/2022 |
5003 | 789 | 08/02/2022 |
5004 | 123 | 08/01/2022 |
5005 | 321 | 08/02/2022 |
To analyze this, we want to find the click-through conversion rate for each day. The click-through conversion rate is defined as the number of distinct users who add a product to the cart after clicking an ad divided by the total number of distinct users who clicked an ad on the same day.
This PostgreSQL query groups the data by the date of clicking on an ad. For each group, it calculates the ratio of distinct users who have added a product to the cart to the distinct users who clicked an ad. It uses a LEFT JOIN to ensure that all ad clicks are included in the calculation, even if there is no corresponding cart add.
To practice another question about calculating rates, solve this SQL interview question from TikTok on DataLemur's online SQL coding environment:
The clause in SQL allows you to select records that are unique, eliminating duplicates.
For example, if you had a table of Lumen Technologies employees, and wanted to see what are all the unique job titles that currently worked at the company, you could write the following query:
Given two tables, and , write a SQL query that retrieves the customer's name, their total number of orders, and the total order amount for customers who have made more than five purchases. The output should be ordered by the customers with the highest order amount.
Here are the sample tables for this problem:
customer_id | first_name | last_name |
---|---|---|
1 | John | Doe |
2 | Jane | Smith |
3 | Sam | Williams |
4 | Anna | Johnson |
order_id | customer_id | order_amount |
---|---|---|
101 | 1 | 25.50 |
102 | 2 | 33.00 |
103 | 1 | 44.10 |
104 | 3 | 20.00 |
105 | 2 | 61.50 |
106 | 3 | 36.00 |
107 | 4 | 82.30 |
108 | 1 | 15.00 |
109 | 2 | 50.50 |
110 | 1 | 30.00 |
Here is a PostgreSQL query that would solve the given problem:
This query joins the customers and the orders tables on the customer_id. It then groups the data by customer_id, and the customers' first and last names. We also use the HAVING clause to ensure we only retrieve customers with more than five orders. Finally, the ORDER BY clause is used to sort the data by the total order amount in descending order.
Because joins come up frequently during SQL interviews, try this SQL join question from Spotify:
The key to acing a Lumen Technologies SQL interview is to practice, practice, and then practice some more! In addition to solving the above Lumen Technologies SQL interview questions, you should also solve the 200+ SQL Interview Questions on DataLemur which come from companies like Facebook, Google, and VC-backed startups.
Each interview question has hints to guide you, detailed solutions and best of all, there is an online SQL coding environment so you can right online code up your query and have it graded.
To prep for the Lumen Technologies SQL interview it is also a great idea to solve SQL problems from other tech companies like:
However, if your SQL coding skills are weak, forget about diving straight into solving questions – go learn SQL with this interactive SQL tutorial.
This tutorial covers SQL topics like filtering on multiple conditions using AND/OR/NOT and sorting data with ORDER BY – both of which show up frequently during SQL job interviews at Lumen Technologies.
In addition to SQL query questions, the other types of questions tested in the Lumen Technologies Data Science Interview are:
I'm sorta biased, but I believe the optimal way to prep for Lumen Technologies Data Science interviews is to read the book I wrote: Ace the Data Science Interview.
The book covers 201 interview questions taken from tech companies like Google & Microsoft. It also has a crash course on Stats, SQL & ML. And finally it's vouched for by the data community, which is why it's earned 1000+ reviews on Amazon and a 4.5-star rating.