11 Penske Automotive SQL Interview Questions (Updated 2024)
At Penske Automotive, SQL is used frequently for analyzing vehicle sales data and managing inventory databases. So, it shouldn't surprise you that Penske Automotive almost always evaluates jobseekers on SQL query questions during interviews for Data Science, Data Engineering and Data Analytics jobs.
Thus, to help you practice, we've curated 11 Penske Automotive SQL interview questions – able to solve them?
11 Penske Automotive SQL Interview Questions
SQL Question 1: Identify Penske Automotive's "Power" Customers
Suppose Penske Automotive would like to identify its power users, specifically the customers who purchase vehicles most frequently. A "power" customer for Penske Automotive can be defined as a customer who has made more than 5 purchases in the past year.
Our customer data is stored in a 'purchases' table.
Example Input:
| purchase_id | customer_id | purchase_date | vehicle_id | price |
|---|---|---|---|---|
| 1 | 101 | 01/15/2022 | A200 | 40000 |
| 2 | 102 | 02/10/2022 | B300 | 50000 |
| 3 | 103 | 03/20/2022 | C400 | 45000 |
| 4 | 101 | 04/12/2022 | D500 | 40000 |
| 5 | 101 | 06/01/2022 | E600 | 35000 |
| 6 | 102 | 07/18/2022 | F700 | 60000 |
| 7 | 101 | 08/02/2022 | G800 | 38000 |
| 8 | 101 | 09/12/2022 | H900 | 42000 |
| 9 | 104 | 10/17/2022 | I1000 | 45000 |
| 10 | 101 | 11/30/2022 | J1100 | 46000 |
Our goal is to write a query that will produce a list of power customers, including the customer_id and the number of purchases they've made within the past year.
Answer:
Here is a PostgreSQL query to solve the problem:
This query starts by selecting the customer_id and a count of all rows in the 'purchases' table, which represents the number of purchases made by each customer. The WHERE clause limits the data to only the purchases made within the past year. It then groups the data by customer_id and keeps only the groups where the count of purchases is greater than 5, which are our "power" customers. The result is a list of power customers and the number of purchases they've made within the past year.
To practice a related customer analytics question on DataLemur's free interactive SQL code editor, try this Microsoft SQL Interview problem:
SQL Question 2: Employees Earning More Than Their Boss
Given a table of Penske Automotive employee salary information, write a SQL query to find employees who make more money than their own manager.
Penske Automotive Example Input:
| employee_id | name | salary | department_id | manager_id |
|---|---|---|---|---|
| 1 | Emma Thompson | 3800 | 1 | |
| 2 | Daniel Rodriguez | 2230 | 1 | 10 |
| 3 | Olivia Smith | 8000 | 1 | 8 |
| 4 | Noah Johnson | 6800 | 2 | 8 |
| 5 | Sophia Martinez | 1750 | 1 | 10 |
| 8 | William Davis | 7000 | 2 | NULL |
| 10 | James Anderson | 4000 | 1 | NULL |
Example Output:
| employee_id | employee_name |
|---|---|
| 3 | Olivia Smith |
This is the output because Olivia Smith earns $8,000, surpassing her manager, William Davis who earns 7,800.
Solve this question directly within the browser on DataLemur:
Answer:
First, we perform a SELF-JOIN where we treat the first table () as the managers' table and the second table () as the employees' table. Then we use a clause to filter the results, ensuring we only get employees whose salaries are higher than their manager's salary.
If the code above is tough, you can find a detailed solution here: Well Paid Employees.
Check out the Penske Automotive career page and see what role might be the best fit for you!
SQL Question 3: Can you provide an example of two entities that have a one-to-one relationship, and another example of a one-to-many relationship?
In database schema design, a one-to-one relationship is when each entity is associated with only one instance of the other. For instance, a US citizen's relationship with their social-security number (SSN) is one-to-one because each citizen can only have one SSN, and each SSN belongs to one person.
A one-to-many relationship, on the other hand, is when one entity can be associated with multiple instances of the other entity. An example of this is the relationship between a person and their email addresses - one person can have multiple email addresses, but each email address only belongs to one person.
SQL Question 4: Sales Performance Analysis
At Penske Automotive, vehicle sales are key. An interview question could therefore be related to analyzing the sales performance of different vehicle makes over time.
Let's analyze the monthly sales numbers of different vehicle makes. More specifically, write a SQL query which calculates the total sales and the percentage increase or decrease in sales compared to the previous month for each vehicle make.
Assuming we have a table structured as below:
Example Input:
| sales_id | sales_date | vehicle_make | price |
|---|---|---|---|
| 1 | 2022-06-01 | BMW | 45000 |
| 2 | 2022-06-05 | Mercedes | 50000 |
| 3 | 2022-06-20 | Mercedes | 60000 |
| 4 | 2022-07-02 | BMW | 47000 |
| 5 | 2022-07-15 | BMW | 48000 |
| 6 | 2022-07-22 | Mercedes | 55000 |
The columns in the dataset are:
- : An identifier for each individual sale.
- : The date the vehicle was sold.
- : The make of the vehicle that was sold.
- : The sale price of the vehicle.
Answer:
In the above SQL query, a CTE (Common Table Expression) named is first used to aggregate the total sales for each vehicle make for each month. The function is used to truncate the value to a monthly basis.
Subsequently, the window function is then used to compute the sales growth. It retrieves the value from the previous row (month), subtracts it from the current row's to get the difference, and then divides this by the from the previous row to get a proportion. This is multiplied by 100 to convert it to a percentage. The function is operated over rows partitioned by and ordered by , ensuring that the change in sales are computed correctly for each individual make on a monthly basis.
Pro Tip: Window functions are a frequent SQL interview topic, so practice every window function problem on DataLemur
SQL Question 5: What's a stored procedure, and why use one?
Stored procedures are like functions in Python – they can accept input params and return values, and are used to encapsulate complex logic.
For example, if you worked as a Data Analyst in support of the Marketing Analytics team at Penske Automotive, a common task might be to find the conversion rate for your ads given a specific time-frame. Instead of having to write this query over-and-over again, you could write a stored procedure like the following:
To call this stored procedure, you'd execute the following query:
SQL Question 6: Most Frequent Car Model Sold
Penske Automotive specializes in the sale of new and used vehicles. The company wishes to understand which car models are the most popular among customers. Based on data from the table, create a query that reveals which car models were the most frequently sold in 2022.
Example Input:
| sale_id | car_model_id | sale_date | customer_id |
|---|---|---|---|
| 1 | 1001 | 01/05/2022 00:00:00 | 2001 |
| 2 | 1002 | 02/23/2022 00:00:00 | 2002 |
| 3 | 1002 | 03/21/2022 00:00:00 | 2003 |
| 4 | 1003 | 04/12/2022 00:00:00 | 2004 |
| 5 | 1001 | 05/06/2022 00:00:00 | 2005 |
| 6 | 1001 | 06/04/2022 00:00:00 | 2006 |
Example Input:
| car_model_id | make | model |
|---|---|---|
| 1001 | Toyota | Corolla |
| 1002 | Dodge | Charger |
| 1003 | Jeep | Grand Cherokee |
Example Output:
| make | model | quantity |
|---|---|---|
| Toyota | Corolla | 3 |
| Dodge | Charger | 2 |
| Jeep | Grand Cherokee | 1 |
Answer:
This query first joins the and tables on the column, then groups the records by and . The COUNT function is used to calculate the quantity of each car model sold in 2022. The result is sorted in descending order of quantity to list the most frequently sold car models at the top.
SQL Question 7: Can you describe the difference between a correlated and a non-correlated sub-query?
A correlated sub-query is one that relies on the outer query and cannot be run independently. It uses the outer query to filter or transform data by referencing a column from it, while the outer query uses the results of the inner query. On the other hand, a non-correlated sub-query is not linked to the outer query and can be run on its own. It does not reference any columns from the outer query and is used to retrieve additional data for the outer query.
An example correlated sub-query:
This correlated subquery retrieves the names and salaries of Penske Automotive employees who make more than the average salary for their department. The subquery references the department column in the main query's FROM clause (e1.department) and uses it to filter the rows of the subquery's FROM clause (e2.department).
An example non-correlated sub-query:
This non-correlated subquery retrieves the names and salaries of Penske Automotive employees who make more than the average salary for the Data Analytics department (which honestly should be very few people since Data Analysts are awesome).
Anways, the subquery is independent of the main query and can be considered a standalone query. Its output (the average salary for the Data Analytics department) is then used in the main query to filter the rows of the table.
SQL Question 8: Click-through and Conversion Rates for Penske Automotive Campaigns
Penske Automotive has multiple campaigns for advertising their range of cars. For each ad, they monitor how many people view the ad (impressions), and of these how many go on to click on the ad (clicks). Once a person has clicked on the ad, Penske tracks how many of those people go on to make a purchase (conversions).
Your task is to write a SQL query to calculate the click-through rate (CTR) for each campaign (the proportion of viewers who click the ad) and the conversion rate (CR) (the proportion of people who click on the ad and then make a purchase).
The table has the following schema:
Example Input:
| campaign_id | impressions | clicks | conversions |
|---|---|---|---|
| 50001 | 15000 | 3000 | 1000 |
| 50002 | 20000 | 2500 | 500 |
| 50003 | 12000 | 1800 | 200 |
| 50004 | 18000 | 2000 | 600 |
| 50005 | 8000 | 1000 | 300 |
Your task is to provide the click-through and conversion rates for each campaign.
Example Output:
| campaign_id | ctr | cr |
|---|---|---|
| 50001 | 0.2 | 0.33 |
| 50002 | 0.125 | 0.2 |
| 50003 | 0.15 | 0.11 |
| 50004 | 0.111 | 0.3 |
| 50005 | 0.125 | 0.3 |
Answer:
This query uses the mathematical formula for click-through rate (clicks/impressions) and conversion rate (conversions/clicks). In order to avoid division by zero errors, it's important to use decimal to ensure we are getting accurate results. ROUND function is used to limit the results to three decimal places.
To solve a similar problem about calculating rates, try this SQL interview question from TikTok on DataLemur's online SQL coding environment:
SQL Question 9: Maximum Sales Per Car Model
As a data analyst at Penske Automotive, you are tasked with analyzing the company's sales data. You need to write an SQL query that will find the car model that has brought the maximum total sales (price * quantity) for each year.
Example Input:
| sale_id | model_id | sale_date | quantity | price |
|---|---|---|---|---|
| 3140 | 865 | 01/05/2020 | 8 | 32000 |
| 8975 | 789 | 03/09/2019 | 7 | 25000 |
| 6953 | 865 | 05/15/2019 | 4 | 32000 |
| 7852 | 372 | 09/30/2020 | 3 | 45000 |
| 4126 | 789 | 11/20/2020 | 6 | 25000 |
Example Output:
| year | model | max_sales |
|---|---|---|
| 2019 | 865 | 288000 |
| 2020 | 372 | 135000 |
Answer:
In this query, we extract the year from and then group the records by year and car model. For each grouping, we calculate the total sales (price * quantity) and select the maximum total sales per year and per model.
SQL Question 10: When would you use the constraint?
A is a field in a table that references the of another table. It creates a link between the two tables and ensures that the data in the field is valid.
Say for example you had sales analytics data from Penske Automotive's CRM (customer-relationship management) tool.
The FOREIGN KEY constraint ensures that the data in the field of the "opportunities" table is valid, and prevents the insertion of rows in the table that do not have corresponding entries in the table. It also helps to enforce the relationship between the two tables and can be used to ensure that data is not deleted from the accounts table if there are still references to it in the opportunities` table.
SQL Question 11: Analysis of Penske Automotive Customers and Transactions
As a Data Analyst in Penske Automotive, your task is to identify the most profitable car model based on recent transactions in the last quarter of the year. To do this, you'll have to join the Customer and Transaction tables, group by model, and calculate the sum of transaction amounts.
Given are the following tables:
Example Input:
| customer_id | first_name | last_name | |
|---|---|---|---|
| 001 | John | Doe | johndoe@example.com |
| 002 | Jane | Smith | janesmith@example.com |
| 003 | Tom | Hanks | tomhanks@example.com |
| 004 | Mary | Johnson | maryjohnson@example.com |
| 005 | James | Brown | jamesbrown@example.com |
Example Input:
| transaction_id | customer_id | car_model | transaction_date | transaction_amount |
|---|---|---|---|---|
| T001 | 001 | Honda Civic | 10/05/2021 | 20000 |
| T002 | 003 | Toyota Corolla | 11/16/2021 | 18000 |
| T003 | 002 | Honda Civic | 12/22/2021 | 21000 |
| T004 | 005 | Ford F-150 | 10/31/2021 | 26000 |
| T005 | 004 | Toyota Corolla | 12/15/2021 | 19000 |
Answer:
This query will join the and tables on the field and then filter for transactions that took place in the fourth quarter. The records are then grouped by car model and the total transaction amounts (revenue) for each model are calculated. The result is a list of car models and their corresponding total sales, ordered in descending order of sales. This will help in determining which car model is the most profitable.
Because join questions come up frequently during SQL interviews, practice this interactive Snapchat JOIN SQL interview question:
Penske Automotive SQL Interview Tips
The best way to prepare for a Penske Automotive SQL interview is to practice, practice, practice.
Beyond just solving the above Penske Automotive SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like Netflix, Google, and Amazon.
Each exercise has multiple hints, detailed solutions and crucially, there's an interactive coding environment so you can right in the browser run your query and have it executed.
To prep for the Penske Automotive SQL interview you can also be wise to practice interview questions from other automotive companies like:
However, if your SQL skills are weak, forget about jumping right into solving questions – go learn SQL with this interactive SQL tutorial.
This tutorial covers topics including aggregate functions and turning a subquery into a CTE – both of these show up routinely during Penske Automotive SQL interviews.
Penske Automotive Data Science Interview Tips
What Do Penske Automotive Data Science Interviews Cover?
Beyond writing SQL queries, the other topics to prepare for the Penske Automotive Data Science Interview include:
- Probability & Stats Questions
- Python or R Programming Questions
- Product Metrics Interview Questions
- ML Modelling Questions
- Behavioral Based Interview Questions
How To Prepare for Penske Automotive Data Science Interviews?
I'm a tad biased, but I believe the best way to prep for Penske Automotive Data Science interviews is to read my book Ace the Data Science Interview.
The book solves 201 data interview questions sourced from tech companies like Google & Microsoft. It also has a refresher on Stats, SQL & ML. And finally it's helped thousands of people land their dream job in data, which is how it's gotten to be an Amazon best-seller with over 1000+ 5-star reviews.