10 Sonic Automotive SQL Interview Questions (Updated 2024)
Data Analysts and Data Engineers at Sonic Automotive write SQL for analyzing sales patterns and customer behavior, and optimizing inventory and supply chain management based on real-time data. For this reason Sonic Automotive asks prospective hires SQL coding interview questions.
So, to help you study for the Sonic Automotive SQL interview, here’s 10 Sonic Automotive SQL interview questions – scroll down to start solving them!
10 Sonic Automotive SQL Interview Questions
SQL Question 1: Identify the Power Users at Sonic Automotive
Sonic Automotive is an automotive retailer company with a wide customer base. In order to establish an exploration strategy that prioritizes high-value customers (power users), they need to analyze their sales database and identify the customers who have made the most purchases in the last year. A "power user" is defined as a customer who has made more than 40 purchases in the last year.
Example Input:
| purchase_id | customer_id | purchase_date | vehicle_id | sales_value |
|---|---|---|---|---|
| 101 | 456 | 01/08/2021 | 768 | 20000 |
| 202 | 789 | 04/05/2021 | 967 | 15000 |
| 303 | 123 | 10/02/2021 | 564 | 35000 |
| 404 | 654 | 04/26/2021 | 432 | 22000 |
| 505 | 789 | 05/01/2021 | 860 | 19000 |
Example Output:
| customer_id | total_purchases |
|---|---|
| 789 | 2 |
| 456 | 1 |
| 123 | 1 |
| 654 | 1 |
Answer:
This query will fetch the list of customers that made more than 40 purchases throughout 2021. By identifying these power users, Sonic Automotive can further evaluate their buying patterns and consider them when making business decisions to improve sales and customer experience.
To solve 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 Managers
Given a table of Sonic Automotive employee salary data, write a SQL query to find all employees who make more money than their own manager.
Sonic 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.
Check your SQL query for this problem 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 solution above is hard to understand, you can find a step-by-step solution with hints here: Highly-Paid Employees.
SQL Question 3: What does do?
The UNION operator combines the results from multiple SELECT statements into a single result set.
Here's an example of using the UNION operator to combine the results of two SELECT statements that retrieve data from tables of Sonic Automotive's Facebook ads and their Google ads:
This statement would return a combined result set of both Facebook and Google ads that have a spend greater than $200. The result set would include the following columns: ad_id, ad_name, ad_type, impressions, clicks, and spend.
Note that the two SELECT statements within the UNION must have the same number of columns and the columns must have similar data types. In this example, both SELECT statements have the same number of columns and the data types of the columns are all compatible.
Check out the Sonic Automotive career page and see which role might be the best fir for you!
SQL Question 4: Finding the Last Service for Each Car
Imagine you are a data analyst at Sonic Automotive and you have been asked to analyze a dataset of all car services to determine the latest service date and the kind of service each car had.
The dataset is structured as such:
Example Input:
| service_id | car_id | service_type | service_date |
|---|---|---|---|
| 101 | 2001 | Oil Change | 2022-05-14 |
| 102 | 2001 | Tyre Change | 2022-06-01 |
| 103 | 2002 | Engine Check | 2022-07-10 |
| 104 | 2003 | Tyre Change | 2022-01-18 |
| 105 | 2003 | Oil Change | 2022-06-30 |
Each row in the table represents a service, with the service id (), the car id (), the type of service (), and the date of the service ().
Using this data, write a SQL query that will return a table with each car and its latest service, including the type of service. The output should be sorted by .
Example Output:
| car_id | latest_service_date | service_type |
|---|---|---|
| 2001 | 2022-06-01 | Tyre Change |
| 2002 | 2022-07-10 | Engine Check |
| 2003 | 2022-06-30 | Oil Change |
Answer:
Here's a SQL query using PostgreSQL that makes use of window functions to solve this problem:
This query uses the window function, which assigns a unique row number to each row within each "window" (in this case, rows with the same car id). Rows are numbered according to the clause within the clause, meaning the row with the most recent service date gets the row number 1.
The outer query then selects only those rows where the row number is 1, i.e., the rows with the most recent service date for each car. These rows are then returned in the result, sorted by .
p.s. Window functions show up pretty often during SQL interviews, so practice the 27+ window function questions on DataLemur
SQL Question 5: What's the purpose of the function in SQL?
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:
| customer_id | email_engagement | sms_engagement |
|---|---|---|
| 101 | moderately_active | not_opted_in |
| 201 | un-subscribed | NULL |
| 301 | NULL | not_opted_in |
| 401 | not_active | very_active |
| 501 | very_active | very_active |
| 601 | NULL | NULL |
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:
| customer_id | email_engagement | sms_engagement |
|---|---|---|
| 101 | moderately_active | not_opted_in |
| 102 | un-subscribed | not_opted_in |
| 103 | not_active | not_opted_in |
| 104 | not_active | very_active |
| 105 | very_active | very_active |
| 106 | not_active | not_opted_in |
SQL Question 6: Filter Sonic Automotive's Customer Records Based on Multiple Conditions
Given a customer records database for Sonic Automotive, write a SQL query that returns all the customers who purchased a car in 2020, are from California and either purchased a 'Sedan' or 'SUV' type of car. The database table 'customers' contains the customerID, purchaseDate, state, and carType among other customer information.
For this problem, let's consider the following 'customers' table:
Example Input:
| customerID | purchaseDate | state | carType |
|---|---|---|---|
| 1201 | 2019-03-21 | California | Sedan |
| 2342 | 2020-05-17 | California | SUV |
| 9812 | 2020-08-30 | New York | SUV |
| 3452 | 2020-12-15 | California | Sedan |
| 2651 | 2018-07-07 | California | Pickup |
The output will be:
Example Output:
| customerID | purchaseDate | state | carType |
|---|---|---|---|
| 2342 | 2020-05-17 | California | SUV |
| 3452 | 2020-12-15 | California | Sedan |
Answer:
This query first filters down the customers who purchased a car in 2020 and are from California by using the WHERE clause. Then, it filters down those who purchased either a 'Sedan' or 'SUV' type of car by using the AND operator for condition clustering and the OR operator for specifying multiple conditions in the same clause. The result of this query is the list of customers who meet all these conditions.
SQL Question 7: What's the purpose of the the command?
When using , only rows that are identical in both sets will be returned.
For a concrete example, say you were a Data Analyst supporting the Sales Analytics team at Sonic Automotive, and data on potential sales leads lived in both Salesforce and Hubspot CRMs. To write a query to analyze leads created before 2023 started, that show up in BOTH CRMs, you would use the command:
SQL Question 8: Average Selling Price of Vehicles by Make for Sonic Automotive
For Sonic Automotive, a leading auto dealer in the US, they might be interested in the average selling price of their vehicles by make. This provides insights into which car brands are generating the most revenue in terms of their average selling price. Here's a question and sample data for this scenario.
Question: Using the Sonic Automotive's sales database, can you write a SQL query to find the average selling price of each vehicle make sold in the year 2022?
Sample Input:
| sale_id | vehicle_id | sale_date | sale_price |
|---|---|---|---|
| 1 | 123 | 01/07/2022 00:00:00 | 30200 |
| 2 | 265 | 01/14/2022 00:00:00 | 40750 |
| 3 | 362 | 01/21/2022 00:00:00 | 35990 |
| 4 | 192 | 01/28/2022 00:00:00 | 27450 |
| 5 | 981 | 02/04/2022 00:00:00 | 38500 |
Sample Input:
| vehicle_id | make | model |
|---|---|---|
| 123 | Toyota | Camry |
| 265 | Honda | Accord |
| 362 | Honda | Civic |
| 192 | Toyota | Corolla |
| 981 | Ford | Mustang |
Example Output:
| make | avg_sale_price |
|---|---|
| Toyota | 28825 |
| Honda | 38370 |
| Ford | 38500 |
Answer:
In this SQL statement, we are joining the and tables on their common attribute, . We are then grouping by vehicle make and finding the average sale price for each make. We restrict our data to the year 2022 using the WHERE clause. The function gives us the year from the sale date, and we compare this to 2022 to ensure we only look at sales from that year.
The two most similar questions to your query about the average selling price of vehicles by make are:
-
"Highest-Grossing Items" from Amazon Reason: This problem also involves identifying revenue-generating items (in this case, products rather than car brands).
-
"Average Review Ratings" from Amazon. Reason: This problem also asks for the calculation of an average (in this case, review ratings rather than the selling price) grouped by a specific category (products per month rather than make of the car).
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for computing revenue generating items category-wise or this Amazon Average Review Ratings Question which is similar for computing average of a feature grouped by category.
SQL Question 9: Calculate Click-Through Conversion Rates for Sonic Automotive
You are a Data Analyst at Sonic Automotive, a Fortune 500 company and one of the largest automotive retailers in the United States. Sonic Automotive sells both new and used vehicles from a variety of brands.
The Marketing team has recently launched several online ad campaigns to draw traffic to vehicle product pages on the Sonic Automotive website. These product pages detail specific vehicles (both used and new) that are available for purchase at any of the company's dealership locations.
Your task is to calculate the Click-Through Conversion Rate (CTR) from viewing a vehicle product page to adding the vehicle to cart, and ultimately, purchasing it. The aim is to understand which vehicles customers are engaging with and successfully purchasing through these online ad campaigns.
You have access to two tables - and .
Example Input:
| view_id | user_id | view_date | vehicle_id |
|---|---|---|---|
| 1347 | 456 | 2022-06-08 | 32001 |
| 1594 | 789 | 2022-06-10 | 41006 |
| 1893 | 654 | 2022-06-18 | 32001 |
| 2075 | 321 | 2022-07-26 | 41006 |
| 2310 | 987 | 2022-07-05 | 41006 |
Example Input:
| purchase_id | user_id | purchase_date | vehicle_id |
|---|---|---|---|
| 9671 | 456 | 2022-06-10 | 32001 |
| 10078 | 789 | 2022-06-30 | 41006 |
| 10504 | 654 | 2022-07-18 | 32001 |
| 10962 | 321 | 2022-07-26 | 41006 |
| 11238 | 987 | 2022-07-05 | 41006 |
Answer:
In this SQL query, we are joining the and tables on and to correlate page views and purchases for each vehicle. We then calculate the Clickthrough Conversion Rate by dividing the count of unique users who made a purchase by the count of unique users who viewed the vehicle. Resulting CTR is sorted in descending order to focus on vehicles with highest conversion rates first.
To practice another question about calculating rates, try this SQL interview question from TikTok within DataLemur's online SQL coding environment:
SQL Question 10: How do you determine which records in one table are not present in a second table?
To find records in one table that aren't in another, you can use a and check for values in the right-side table.
Here's an example using two tables, Sonic Automotive employees and Sonic Automotive managers:
This query returns all rows from Sonic Automotive employees where there is no matching row in managers based on the column.
You can also use the operator in PostgreSQL and Microsoft SQL Server to return the records that are in the first table but not in the second. Here is an example:
This will return all rows from employees that are not in managers. The operator works by returning the rows that are returned by the first query, but not by the second.
Note that isn't supported by all DBMS systems, like in MySQL and Oracle (but have no fear, since you can use the operator to achieve a similar result).
Preparing For The Sonic Automotive SQL Interview
The key to acing a Sonic Automotive SQL interview is to practice, practice, and then practice some more!
Beyond just solving the earlier Sonic Automotive SQL interview questions, you should also solve the 200+ DataLemur interview questions which come from companies like Facebook, Google, and VC-backed startups.
Each exercise has hints to guide you, fully explained answers along with a discussion board to see how others solved it and crucially, there's an online SQL coding environment so you can right in the browser run your SQL query answer and have it executed.
To prep for the Sonic Automotive SQL interview you can also be a great idea to solve SQL questions from other automotive companies like:
In case your SQL coding skills are weak, forget about diving straight into solving questions – strengthen your SQL foundations with this DataLemur SQL tutorial.
This tutorial covers SQL concepts such as CASE/WHEN statements and joining a table to itself – both of which pop up routinely in SQL job interviews at Sonic Automotive.
Sonic Automotive Data Science Interview Tips
What Do Sonic Automotive Data Science Interviews Cover?
Besides SQL interview questions, the other topics tested in the Sonic Automotive Data Science Interview include:
- AB Testing Questions
- Python or R Coding Questions
- Analytics and Product-Metrics Questions
- Machine Learning and Predictive Modeling Questions
- Resume-Based Behavioral Questions
How To Prepare for Sonic Automotive Data Science Interviews?
To prepare for Sonic Automotive Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions sourced from FAANG tech companies
- a refresher covering Python, SQL & ML
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