10 Asbury Automotive SQL Interview Questions (Updated 2024)

Data Science, Data Engineering and Data Analytics employees at Asbury Automotive use SQL for analyzing sales trends across dealerships and managing customer data for targeted car marketing campaigns. That's why Asbury Automotive frequently asks jobseekers SQL interview questions.

To help you study for the Asbury Automotive SQL interview, we've collected 10 Asbury Automotive SQL interview questions in this blog.

10 Asbury Automotive SQL Interview Questions

SQL Question 1: Calculate Monthly Average Sales Per Car Model

Asbury Automotive Group is one of the largest automotive retailers in the U.S. with lots of car sales data. Write a SQL query to calculate the monthly average sales per car model using the table. The table has four columns: , , , and . Group them by month and car model. Month should be represented using a whole integer where January is represented as 1, February as 2, and so on. Within each month, order the results by car model in alphabetical order.

Example Input:

Answer:

The above SQL query creates a new column named by extracting the month information from the column. Then, the window function is employed to compute the average for each combination of and . The clause is used to group the records by and , and the clause is used to sort the output by and in ascending order.

Example Output:

Pro Tip: Window functions are a frequent SQL interview topic, so practice all the window function problems on DataLemur

SQL Question 2: Department vs. Company Salary

You're given a table of Asbury Automotive employee and department salary data. Write a SQL query to find departments where the departments average salary is higher than the company's average salary.

Solve this problem interactively on DataLemur:

The solution is LONG – 30+ lines of SQL. You can find a step-by-step solution with hints here: Department vs. Company Salary.

SQL Question 3: Can you describe the difference between a correlated and a non-correlated sub-query?

hile a correlated subquery relies on columns in the main query's FROM clause and cannot function independently, a non-correlated subquery operates as a standalone query and its results are integrated into the main query.

An example correlated sub-query:

This correlated subquery retrieves the names and salaries of Asbury 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 Asbury Automotive employees who make more than the average salary for the Data Science department (which honestly should be very few people since Data Scientists are awesome and deserve to be paid well).The subquery is considered independent of the main query can stand alone. Its output (the average salary for the Data Science department) is then used in the main query to filter the rows of the Asbury Automotive employees table.

SQL Question 4: Automotive Sales and Inventory Management

Asbury Automotive is an automotive retailer. A part of their business is to sell cars from various manufacturers and models. You are a data analyst tasked with designing a database to help manage their inventory and sales transactions.

The database should consist of three tables: , , and . The table should contain data about the cars currently in inventory such as model, year, color, and manufacturer. The table should contain information about the car manufacturers like their name and country of origin. The table should record the transactions, keeping track of which car(s) a customer bought, when the purchase took place, and the sales amount.

Given this premise, answer the following question: How many cars of each model were sold in the month of August 2022?

Example Input:

Example Input:

Example Input:

Answer:

This query joins the and tables on the field, filters the sales data for August 2022, and then counts how many cars of each model were sold that month. It uses the function to extract the month and year from the field.

SQL Question 5: What does the SQL function do?

The COALESCE() function can be used to replace NULL values with a specified value. For instance, if you are a Data Analyst at a company and are working on a customer analytics project to identify the most engaged customers, you may have access to the following data in the table:

Before you could procede with your analysis, you would need to remove the NULLs, and replace them with the default value for email engagement (not_active), and the default sms_engagement level (not_opted_in).

To do this, you'd run the following query:

This would get you the following output:

SQL Question 6: Filter Customers by Recent Purchase and Geographic Location

Asbury Automotive Group is one of the largest automotive retailers in the U.S. They need to retrieve a list of customers from their database who have made a purchase in the last year and are located in the Southeast region, specifically in the states of Georgia and Florida. The main focus is customers who have bought either a new or used vehicle.

Example Input:

Answer:

This PostgreSQL query will filter down the customer records in the table to only include customers located in Georgia (GA) or Florida (FL), who made a car purchase (either new or used) during the past year.

SQL Question 7: What's the main difference between the 'BETWEEN' and 'IN' operators in SQL?

The and operators are both used to filter data based on certain criteria, but they work in different ways. is used to select values within a range, while is used to select values that match a list.

For instance, if you have a table called that contains the salary of each employee, along with which country they reside in, you could use the operator to find all employees who make between $130k and$160k:

To find all employees that reside in France and Germany, you could use the operator:

SQL Question 8: Find the Average Sale Price Per Car Make

Asbury Automotive is a Fortune 500 auto retailer company in the US that has various brand dealerships. They are interested in knowing the average sale price of the cars across different makes to aid in their decision-making process. Write a SQL query to find the average sale price per car make for the year 2022.

Example Input:

Example Output:

Answer:

In this query, first, we filter out the records for the current year 2022 using the WHERE clause. Then, using the GROUP BY statement, we divide the records according to car_make. For each car_make, we calculate the average sale_price using the AVG function. The result will be the average sale price per car make for the year 2022.

To practice a very similar question try this interactive Wayfair Y-on-Y Growth Rate Question which is similar for calculating grouping based on sales or this Amazon Average Review Ratings Question which is similar for calculating averages grouped by a specific category.

SQL Question 9: Find the Average Sales Price by Vehicle Type

As part of the analytic team at Asbury Automotive, you are asked to analyze sales data to gain insights into how different types of vehicles are sold. Write a SQL query to find the average sales price of each type of vehicle (SUV, Sedan, Truck, etc.) sold in the month of July 2022.

Example Input:

Example Output:

Answer:

This SQL query works by first filtering for all the sales that happened in July 2022. This is achieved using the function within the clause which rounds down the to the nearest month.

Then the statement groups the sales by .

Finally, the function is used to find the average sales price within each group.

SQL Question 10: Can you describe the difference between a clustered and a non-clustered index?

Clustered indexes have a special characteristic in that the order of the rows in the database corresponds to the order of the rows in the index. This is why a table can only have one clustered index, but it can have multiple non-clustered indexes.

The main difference between clustered and non-clustered indexes is that the database tries to maintain the order of the data in the database to match the order of the corresponding keys in the clustered index. This can improve query performance as it provides a linear-access path to the data stored in the database.

BTW Asbury Automotive has awesome career programs!! See which one might help you reach your career goals.

Asbury Automotive SQL Interview Tips

The best way to prepare for a Asbury Automotive SQL interview is to practice, practice, practice. Beyond just solving the above Asbury Automotive SQL interview questions, you should also solve the 200+ tricky sql questions on DataLemur which come from companies like Google, Uber, and Microsoft.

Each interview question has hints to guide you, full answers and best of all, there's an interactive SQL code editor so you can easily right in the browser your query and have it checked.

To prep for the Asbury Automotive SQL interview it is also a great idea to solve SQL problems from other automotive companies like:

• Sonic Automotive SQL questions

• Carvana SQL questions

• CarMax SQL questions

• Lithia Motors SQL questions

But if your SQL skills are weak, forget about going right into solving questions – strengthen your SQL foundations with this free SQL for Data Analytics course.

This tutorial covers things like math functions and filtering data with WHERE – both of which pop up routinely during SQL job interviews at Asbury Automotive.

Asbury Automotive Data Science Interview Tips

What Do Asbury Automotive Data Science Interviews Cover?

Beyond writing SQL queries, the other types of questions covered in the Asbury Automotive Data Science Interview are:

• Probability & Statistics Questions
• Python Pandas or R Coding Questions
• Product Data Science Interview Questions
• Machine Learning Questions
• Behavioral & Resume-Based Questions

How To Prepare for Asbury Automotive Data Science Interviews?

The best way to prepare for Asbury Automotive Data Science interviews is by reading Ace the Data Science Interview. The book's got:

• 201 Interview Questions from FAANG, tech startups, and Wall Street
• A Refresher covering Stats, SQL & ML
• Amazing Reviews (1000+ reviews, 4.5-star rating)