At Mobileye, SQL does the heavy lifting for analyzing traffic and driver behavior data, and managing databases for autonomous vehicle development. That's why Mobileye asks SQL problems during interviews for Data Analyst, Data Science, and BI jobs.
To help you ace the Mobileye SQL interview, we've curated 10 Mobileye SQL interview questions – how many can you solve?
Mobileye is a mobility company that provides vehicle safety and autonomous driving solutions. One of the important aspects of the business is monitoring which customers are utilizing their services most often. A "power user" in this case, can be measured by the frequency of usage of their Mobileye product, their driving habits, or their interaction with the platform or services.
Suppose we have a table and a table that captures this data:
log_id | user_id | product_id | usage_date | mileage_covered |
---|---|---|---|---|
101 | 1 | 1001 | 01/10/2022 | 200 |
102 | 2 | 1001 | 01/10/2022 | 80 |
103 | 3 | 1002 | 02/10/2022 | 110 |
104 | 1 | 1001 | 02/10/2022 | 250 |
105 | 2 | 1002 | 03/10/2022 | 120 |
user_id | name | registration_date |
---|---|---|
1 | Alice | 01/01/2022 |
2 | Bob | 01/03/2022 |
3 | Charlie | 01/06/2022 |
The task is to write a SQL query that identifies power users as those who have covered more than 200 miles in a day more than 5 times.
The inner query selects entries from where the mileage covered exceeds 200 miles, groups them by and counts the number of times this event occurs. If the count is more than 5, the is selected.
The outer query simply joins the resulting s with the table to display the name of the power users.
To solve a similar power-user data analysis problem question on DataLemur's free online SQL code editor, try this recently asked Microsoft SQL interview question:
Mobileye is a technology company that develops vision-based advanced driver-assistance systems (ADAS) providing warnings for collision prevention, facilitates autonomous driving and fleet management systems.
In this question, assume you are given a dataset where each row represents a driving event for a fleet of vehicles with the speed of the vehicle, timestamp of event, and vehicle identifier. The task is to write SQL queries to find the monthly average driving speed for each vehicle using window functions.
Here is the example table and data:
event_id | vehicle_id | event_timestamp | speed_kph |
---|---|---|---|
8724 | 1 | 2022-06-08 14:00:00 | 60 |
9842 | 1 | 2022-06-12 09:00:00 | 65 |
1832 | 2 | 2022-07-19 19:30:00 | 80 |
7624 | 1 | 2022-07-21 09:50:00 | 70 |
3927 | 2 | 2022-07-23 14:00:00 | 75 |
This query uses the function to calculate average speed, and to derive the month from the timestamp. The window function ensures we get the average for each month per vehicle.
vehicle_id | month | avg_speed |
---|---|---|
1 | 6 | 62.50 |
1 | 7 | 70.00 |
2 | 7 | 77.50 |
Pro Tip: Window functions are a popular SQL interview topic, so practice all the window function problems on DataLemur
Constraints are just rules for your DBMS to follow when updating/inserting/deleting data.
Say you had a table of Mobileye employees, and their salaries, job titles, and performance review data. Here's some examples of SQL constraints you could implement:
NOT NULL: This constraint could be used to ensure that certain columns in the employee table, such as the employee's first and last name, cannot contain NULL values.
UNIQUE: This constraint could be used to ensure that the employee ID is unique. This would prevent duplicate entries in the employee table.
PRIMARY KEY: This constraint could be used to combine the NOT NULL and UNIQUE constraints to create a primary key for the employee table. The employee ID could serve as the primary key.
FOREIGN KEY: This constraint could be used to establish relationships between the employee table and other tables in the database. For example, you could use a foreign key to link the employee ID to the department ID in a department table to track which department each employee belongs to.
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 salary values 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 employee hire date to the current date if no value is provided when a new employee is added to the database.
Mobileye, an Intel company, is a leader in providing driving assistance systems and working towards autonomous vehicles. Let's say Mobileye has a fleet of test vehicles that collect various data points when they are out for a run. The company tracks the following data - vehicle information, run details, and various sensor stats during each run.
The vehicles table has the below columns - vehicle id (unique identifier), model, and manufacturer. The runs table has the below columns - run id (unique identifier), vehicle id, start time, end time, and total distance covered. The sensor_stats table has the below columns - run id, timestamp, and various sensor statistics.
Design the aforementioned tables and their relationships. Using this, find out which vehicle model has the highest average total distance covered per run.
vehicle_id | model | manufacturer |
---|---|---|
101 | Model S | Tesla |
102 | Model X | Tesla |
103 | I-PACE | Jaguar |
104 | EQS | Mercedes-Benz |
run_id | vehicle_id | start_time | end_time | total_distance |
---|---|---|---|---|
201 | 101 | 06/01/2022 08:00:00 | 06/01/2022 10:00:00 | 120 |
202 | 101 | 06/02/2022 08:00:00 | 06/02/2022 11:00:00 | 150 |
203 | 102 | 06/01/2022 09:00:00 | 06/01/2022 12:00:00 | 160 |
204 | 103 | 06/02/2022 08:00:00 | 06/02/2022 10:00:00 | 100 |
205 | 104 | 06/02/2022 05:00:00 | 06/02/2022 07:00:00 | 110 |
This SQL query combines the vehicles and runs tables based on the common column, vehicle_id. It then groups the data by vehicle model and calculates the average distance travelled for each model. The result is ordered in descending order of the average distance, and the top result is returned, which represents the vehicle model with the highest average total distance covered per run.
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 so similar to a regular table?
Views are advantageous for several reasons:
As a data analyst at Mobileye, a company that develops vision-based advanced driver-assistance systems (ADAS) providing warnings for collision prevention and mitigation, you're tasked with an important job. Using the data collected from our devices installed in various vehicles, we need to understand the average speed that our devices detect to further our understanding of vehicle behavior on the roads.
Given a table that contains data from these devices, find the average speed detected per device. The table has the following schema:
|id|device_id|reading_time|speed_kmh| |:----|:---- |:---- |:---- |:---- | |1|1001|2023-01-01 10:10:10|60| |2|1001|2023-01-01 10:10:20|70| |3|1002|2023-01-01 10:10:30|30| |4|1002|2023-01-01 10:10:40|40| |5|1001|2023-01-02 10:10:50|80| |6|1002|2023-01-02 10:11:00|50|
This table represents a series of speed readings, indicated by , taken by each Mobileye device, identified by , at a certain .
Use this data to find the average speed detected by each device.
The PostgreSQL query to answer this task could look like this:
This query first groups the readings by . For each , it then calculates the average speed () using the AVG function.
So for the given input data, the expected output would be:
device_id | avg_speed |
---|---|
1001 | 70.0 |
1002 | 40.0 |
This represents the average speed as detected by each device. This data could help Mobileye in various ways, such as understanding typical speeds of vehicles and identifying if any particular device is consistently reporting high or low speeds.
To practice a very similar question try this interactive Amazon Server Utilization Time Question which is similar for calculating total calculated metrics or this Alibaba Compressed Mean Question which is similar for calculating mean values.
The clause in SQL allows you to select records that are unique, eliminating duplicates.
For example, if you had a table of Mobileye employees, and wanted to see what are all the unique job titles that currently worked at the company, you could write the following query:
Mobileye is a company that specializes in the design and development of self-driving car and advanced driver-assistance systems. For their sales department, which hosts ads and product display in their applications, understanding the click-through rates from viewing a product to adding the product to the cart is really important.
view_id | user_id | view_date | product_id |
---|---|---|---|
1141 | 945 | 2022-10-01 00:00:00 | 1001 |
2332 | 672 | 2022-10-02 00:00:00 | 1002 |
3763 | 925 | 2022-10-03 00:00:00 | 1003 |
4581 | 428 | 2022-10-04 00:00:00 | 1001 |
5182 | 672 | 2022-10-05 00:00:00 | 1002 |
cart_id | user_id | cart_date | product_id |
---|---|---|---|
3451 | 672 | 2022-10-02 00:30:00 | 1002 |
7235 | 428 | 2022-10-04 00:45:00 | 1001 |
8642 | 672 | 2022-10-05 00:30:00 | 1002 |
9673 | 945 | 2022-10-01 00:20:00 | 1001 |
10528 | 342 | 2022-10-09 00:00:00 | 1003 |
Question: Write a SQL query to calculate the click-through conversion rate for each product as a percentage. Assume that the user adds to cart on the same day of viewing.
This query joins the views and cart_additions tables on the user_id, product_id and makes sure that the add to cart action is performed on the same day as the view_date. The click-through conversion rate is calculated as the ratio of distinct users who added the product to the cart to the distinct users who viewed the product. All results are grouped by the product_id, and as a result, we get the click-through rate for each product.
To practice a similar problem about calculating rates, try this TikTok SQL question on DataLemur's online SQL coding environment:
You are tasked with analyzing the customer database at Mobileye. Your main focus is on the products customers buy and their demographic information. Write a SQL query that will join a customers table with a purchases table, and will provide the average age of customers who purchase each individual product.
customer_id | first_name | last_name | date_of_birth |
---|---|---|---|
1001 | John | Doe | 1985-07-20 |
1002 | Jane | Smith | 1990-11-30 |
1003 | Bruce | Wayne | 1985-02-19 |
1004 | Peter | Parker | 1997-08-15 |
1005 | Tony | Stark | 1970-05-29 |
purchase_id | customer_id | product_id | purchase_date |
---|---|---|---|
2001 | 1001 | 3001 | 2021-07-20 |
2002 | 1002 | 3002 | 2022-01-30 |
2003 | 1003 | 3001 | 2022-02-19 |
2004 | 1001 | 3003 | 2022-08-15 |
2005 | 1005 | 3001 | 2022-05-29 |
The SQL query begins by selecting the product_id from the purchases table. It also calculates the average age of the customer at the time of the purchase. This is done by subtracting the customer’s birth year from the current year. The customers and purchases tables are joined using a JOIN ON clause, where purchases made by the same customer_id are grouped together. The GROUP BY keyword is used at the end to organize the output by product_id. The resulting table will show the average age of customers for each product sold by Mobileye.
Since joins come up so often during SQL interviews, try this interactive Snapchat JOIN SQL interview question:
A primary key is a column (or set of columns) in a table that uniquely identifies each row in the table. It cannot contain null values and must be unique across all rows in the table.
A foreign key is a column (or set of columns) in a table that references the primary key of another table. It is used to establish a relationship between the two tables. A foreign key can contain null values, and multiple rows in the referencing table can reference the same row in the referenced table.
For example, consider a database with two tables: and . The Mobileye customers table might have a primary key column called , while the Mobileye orders table might have a foreign key column called that references the column in the table. This establishes a relationship between the two tables, such that each row in the orders table corresponds to a specific Mobileye customer.
The best way to prepare for a SQL interview, besides making sure you have strong SQL fundamentals, is to practice a ton of real SQL questions that were asked in recent job interviews. Beyond just solving the above Mobileye SQL interview questions, you should also solve the 200+ tricky sql questions on DataLemur which come from companies like FAANG (FB, Apple, Amazon, Netflix, Google).
Each DataLemur SQL question has hints to guide you, full answers and best of all, there is an online SQL code editor so you can right online code up your query and have it executed.
To prep for the Mobileye SQL interview it is also a great idea to solve SQL problems from other tech companies like:
However, if your SQL skills are weak, don't worry about diving straight into solving questions – go learn SQL with this SQL interview tutorial.
This tutorial covers SQL concepts such as aggregate functions like SUM()/COUNT()/AVG() and UNION – both of which show up routinely during SQL interviews at Mobileye.
Besides SQL interview questions, the other types of questions tested in the Mobileye Data Science Interview are:
The best way to prepare for Mobileye Data Science interviews is by reading Ace the Data Science Interview. The book's got: