Data Analysts & Data Scientists at Luminar write ad-hoc SQL queries daily as part of their job. They use SQL for managing and querying LiDAR sensor data stored in relational databases and analyzing traffic and user behavior data for autonomous vehicle development. That's the reason behind why Luminar Technologies typically asks folks interviewing for data jobs SQL coding interview questions.
Thus, to help you prep for the Luminar Technologies SQL interview, we've curated 8 Luminar Technologies SQL interview questions can you solve them?
Luminar Technologies heavily utilizes lidar (light detection and ranging) technology. Lidar devices produce large volumes of sensor data. Suppose we're given tables of lidar devices and their daily sensor usage.
device_id | install_date | vehicle_id |
---|---|---|
42 | 02/01/2021 | 108 |
53 | 05/01/2021 | 192 |
64 | 07/18/2020 | 158 |
usage_id | device_id | usage_date | distance_scanned_km |
---|---|---|---|
178 | 42 | 08/01/2022 | 252 |
199 | 42 | 08/02/2022 | 298 |
203 | 64 | 08/01/2022 | 158 |
207 | 64 | 08/02/2022 | 167 |
214 | 53 | 08/01/2022 | 213 |
Your task is to write a SQL query to calculate the daily average distance scanned (in km) by all devices, sorted by date. Also, calculate a running total of the daily average scanned distance.
Use PostgreSQL and its window functions to answer this question.
In the answer, we first create a CTE () where we calculate the average distance scanned per day () and a running total of this daily average (). This is achieved by using window functions where rows are partitioned by . We then select from this CTE and order by .
To solve a related window function SQL problem on DataLemur's free interactive SQL code editor, try this Google SQL Interview Question:
Suppose you had a table of Luminar employee salary data. Write a SQL query to find the top three highest paid employees in 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 |
Code your solution to this question interactively 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 tough, you can find a detailed solution here: Top 3 Department Salaries.
is used to combine the results of multiple statements into a single result set.
Suppose you were doing an HR Analytics project for Luminar Technologies, and needed to analyze both Luminar Technologies's contractors and employees. You could use in the following way:
This statement would return a combined result set of Luminar Technologies contractors and employees who were hired after the start of the year 2023.
For Luminar, a company that specializes in providing autonomous vehicle software, let's consider you have a customer database. This database logs all the purchases of software license upgrades made by customers. As a data analyst, you are asked to filter out the customers from the USA whose most recent purchase was a full self-driving (FSD) upgrade, and who have made more than 5 purchases in total.
You are given two tables: and .
customer_id | first_name | last_name | country |
---|---|---|---|
101 | Jane | Doe | USA |
102 | John | Doe | USA |
103 | Alice | Smith | Canada |
104 | Bob | Johnson | USA |
purchase_id | customer_id | product | purchase_date |
---|---|---|---|
201 | 101 | Lidar | 2021-01-01 |
202 | 102 | FSD | 2021-02-01 |
203 | 101 | FSD | 2021-03-01 |
204 | 102 | FSD | 2021-04-01 |
205 | 103 | Lidar | 2021-02-01 |
206 | 104 | FSD | 2021-03-01 |
207 | 101 | AP | 2021-04-01 |
208 | 102 | Fsd | 2021-05-01 |
209 | 101 | Fsd | 2021-05-01 |
210 | 104 | FSD | 2021-06-01 |
In the above query, the JOIN operation is used to combine rows from and tables based on their customer_id. The WHERE clause filters out customers who are from 'USA' and bought the 'FSD' product as their most recent purchase (as determined by the subquery for max purchase_date). The HAVING clause filters the results of the GROUP BY operation to leave only those customers who have made more than 5 purchases. The result shows the customer id, first name, last name, and the total number of purchases for every customer who meets these criteria.
A database index is a way to optimize the performance of a database by reducing the amount of data that needs to be searched to retrieve a record.
There are several types of indexes:
Luminar Technologies specializes in lidar technology. Lidar sensors are used in self-driving cars to create a 3D map of their surroundings. This map is generated by sending out light signals and measuring how long it takes for them to return, with each returned light signal representing a "point" in the 3D map.
In this scenario, you are tasked to find the average number of lidar points detected per test run. You have one table , with each row representing a single test run and includes the columns (unique identifier for each test run), (unique identifier for each self-driving car), (date of the test run), and (number of lidar points detected during the test run).
test_id | car_id | run_date | points_detected |
---|---|---|---|
1001 | 50 | 06/08/2022 | 50000 |
1002 | 70 | 06/10/2022 | 69850 |
1003 | 50 | 06/18/2022 | 50010 |
1004 | 70 | 07/26/2022 | 69800 |
1005 | 50 | 07/05/2022 | 50030 |
car_id | avg_points |
---|---|
50 | 50013.33 |
70 | 69825.00 |
This query calculates the average number of lidar points detected for each self-driving car. It utilizes the AVG function of SQL to get the average of the 'points_detected' column after grouping the rows by 'car_id'. Notice that the result is rounded off to 2 decimals to make it easier to read.
To practice a very similar question try this interactive Google Odd and Even Measurements Question.
The CHECK constraint is used to set a rule for the data in a column. If a row is inserted or updated and the data in the column does not follow the rule specified by the CHECK constraint, the operation will be unsuccessful.
For example, say you had Luminar Technologies customer data. You could use a CHECK constraint to ensure that the email column contains only properly formatted email addresses, or that the age column contains only positive integer. Here's an example of that:
Luminar Technologies would like to analyze their customer records to find those customers who have mentioned "LiDAR" in their customer notes. You have been given the task to find all customers where their notes contain the word "LiDAR". Write a SQL query to filter out these customers.
customer_id | first_name | last_name | notes | |
---|---|---|---|---|
761 | John | Doe | johndoe@example.com | Interested in LiDAR technology for his vehicle |
965 | Sara | Smith | sara@example.com | Looking for radar solution, not LiDAR |
314 | Peter | Parker | peter@example.com | LiDAR technology was recommended by a friend |
589 | Jane | Doe | jane@example.com | Prefers camera-based solution over LiDAR |
847 | Tony | Moss | tony@example.com | Need information about LiDAR products |
customer_id | first_name | last_name | notes | |
---|---|---|---|---|
761 | John | Doe | johndoe@example.com | Interested in LiDAR technology for his vehicle |
314 | Peter | Parker | peter@example.com | LiDAR technology was recommended by a friend |
847 | Tony | Moss | tony@example.com | Need information about LiDAR products |
This SQL query uses the operator along with (wildcard character) to filter out those customers whose notes contain the word "LiDAR". It searches for "LiDAR" within the column and returns all columns () for the matching records.
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Luminar Technologies SQL interview is to solve as many practice SQL interview questions as you can! Beyond just solving the earlier Luminar Technologies SQL interview questions, you should also solve the 200+ tricky sql questions on DataLemur which come from companies like Amazon, Microsoft, Meta, and smaller tech companies.
Each SQL question has hints to guide you, fully explained answers along with a discussion board to see how others solved it and best of all, there's an online SQL code editor so you can instantly run your SQL query and have it graded.
To prep for the Luminar Technologies SQL interview you can also be a great idea to solve SQL problems from other tech companies like:
But if your SQL skills are weak, don't worry about going right into solving questions – improve your SQL foundations with this DataLemur SQL tutorial.
This tutorial covers things like handling timestamps and handling NULLs in SQL – both of which show up frequently in Luminar Technologies interviews.
In addition to SQL interview questions, the other question categories to prepare for the Luminar Technologies Data Science Interview include:
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