10 CarMax SQL Interview Questions (Updated 2024)
CarMax employees use SQL daily for analyzing sales data to forecast trends and managing customer databases to enhance personalized marketing strategies. That's the reason behind why CarMax frequently asks SQL questions during interviews for Data Analyst, Data Science, and BI jobs.
So, to help you study, we've collected 10 CarMax SQL interview questions – how many can you solve?
10 CarMax SQL Interview Questions
SQL Question 1: Identify Frequent Car Buyers at CarMax
A key metric for a car dealership like CarMax to identify their power users or VIP users is the frequency and volume of car purchases. Customers who frequently purchase cars or purchase at high volumes contribute significantly to the business. Suppose our dataset records all sales transactions at CarMax.
The task is, using SQL, write a query to identify the customers (by their customer_id) who have purchased more than 5 cars within the last year.
Example Input:
| sales_id | customer_id | sale_date | car_id | sale_price |
|---|---|---|---|---|
| 7632 | 211 | 01/08/2021 | 30001 | $15,000 |
| 7839 | 345 | 02/10/2021 | 35002 | $20,000 |
| 5294 | 211 | 02/25/2021 | 40001 | $18,000 |
| 6346 | 192 | 03/26/2021 | 45002 | $16,000 |
| 4571 | 981 | 04/45/2021 | 50002 | $19,000 |
| 5276 | 211 | 05/14/2021 | 50001 | $17,000 |
| 6348 | 211 | 09/15/2021 | 35001 | $15,000 |
| 5295 | 211 | 10/22/2021 | 32002 | $14,000 |
| 6349 | 211 | 11/27/2021 | 43001 | $14,000 |
| 4581 | 981 | 12/19/2021 | 45003 | $16,500 |
Answer:
This query selects the customer_id and counts the distinct car_ids (number of cars) purchased by each customer in the past year. It groups the results by customer_id to count purchases individually for each customer. The HAVING clause filters out customers that have purchased more than 5 cars, and the results are sorted in descending order by number of cars purchased.
To work on another SQL customer analytics question where you can solve it interactively and have your SQL solution automatically checked, try this Walmart Labs SQL Interview Question:
SQL Question 2: Department vs. Company Salary
You're given a table of CarMax 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.
Try this problem and run your code right in DataLemur's online SQL environment:
The answer is LONG – 30+ lines of SQL. You can find a step-by-step solution here: Department vs. Company Salary.
Check out the CarMax career page to see where you might fit best!
SQL Question 3: Why is database normalization a good idea?
Database normalization is helpful because it improves the performance, flexibility, and scalability of a database. Some specific benefits of normalization include:
-
Reducing Redundancy: Normalization helps to minimize redundancy by breaking down a larger general table into smaller, more specific tables. This can often reduce the amount of data that needs to be accessed for particular queries.
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Improving Data Integrity: Normalization helps to ensure the integrity of the data by minimizing the risk of data inconsistencies and anomalies. It does this by establishing clear relationships between the tables via primary and foreign keys.
-
Increasing Flexibility: Normalization makes it easier to modify the structure of the database, as there is less redundancy, so it allows you to make changes to one table without affecting others. This makes it easier to adapt the database to changing business needs (and change is basically constant at CarMax!)
-
Improving Performance: Normalization can improve the performance of the database by reducing the amount of data that needs to be stored and accessed in a single table. This can result in faster query times and better overall performance.
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Easier Data Modeling: Normalization can make it easier to model the data in a logical and intuitive way, since less random data is commingled together, which improves the overall database design.
SQL Question 4: Average Car Sale Price Per Month
CarMax needs to monitor the performance of their sales. One important metric they might want to track is the average sale price for each car model in each month of the year.
Given a table that details transactions with as the unique identifier of each sale, as the date of sale, as the make and model of the car and as the price at which the car was sold.
Write a SQL query that will return the average sale price per car model each month.
Example Input:
| sale_id | sale_date | car_model | sale_price |
|---|---|---|---|
| 1 | 2022-01-05 | Toyota Corolla | 18000 |
| 2 | 2022-01-15 | Honda Civic | 20000 |
| 3 | 2022-02-03 | Toyota Corolla | 19000 |
| 4 | 2022-02-20 | Honda Civic | 22000 |
| 5 | 2022-02-25 | Toyota Corolla | 17500 |
Example Output:
| month_of_year | car_model | average_sale_price |
|---|---|---|
| 1 | Toyota Corolla | 18000.00 |
| 1 | Honda Civic | 20000.00 |
| 2 | Toyota Corolla | 18250.00 |
| 2 | Honda Civic | 22000.00 |
Answer:
In this SQL query, we group by both the month of the sale_date and the car_model, then compute the average sale price within these groups. The is used to get the month number from the sale_date. The resulting table shows the average sale price for each car model in each month of the year.
For more window function practice, try this Uber SQL Interview Question within DataLemur's interactive SQL code editor:
SQL Question 5: How do and differ when it comes to ranking rows in a result set?
In SQL, both and are used to rank rows within a result set. The key difference between the two functions is how deal with two or more rows having the same value in the ranked column (aka how the break ties).
RANK(): When there's a tie, leaves a gap in the ranking. For example, if three rows are tied for 2nd place, the RANK() function will assign a rank of 2 to the first of these rows, a rank of 3 to the second row, and a rank of 4 to the third row.
DENSE_RANK(): For ties, does not leave a gap in the ranking. Instead, it assigns the same rank to all tied rows, and then makes the next row 1 bigger. Confusing, I know, but here's an example to make it more clear: if three rows are tied for 3rd place, the function will assign a rank of 3 to all three rows, and then assign a rank of 4 to the next row.
SQL Question 6: Calculate Monthly Sale of Each Car Model
CarMax, a nationwide used car retailer, is utilizing a database to track its sales across the United States. The database has two main tables, and . The table records the car_id, sale date, and sale price for each car sold. The table stores information about each car's model and brand.
CarMax is interested in finding out the monthly sales figures for each car model. Specifically, the company is looking for the total number of cars sold and the total revenue generated per model, per month.
Example Input:
| sale_id | car_id | sale_date | sale_price |
|---|---|---|---|
| 1032 | 65 | 06/18/2022 00:00:00 | 17800 |
| 1543 | 34 | 06/20/2022 00:00:00 | 23000 |
| 1756 | 89 | 07/02/2022 00:00:00 | 14999 |
| 1983 | 65 | 07/11/2022 00:00:00 | 17800 |
| 2022 | 89 | 07/31/2022 00:00:00 | 14999 |
Example Input:
| car_id | model | brand |
|---|---|---|
| 34 | Camry | Toyota |
| 65 | CR-V | Honda |
| 89 | Civic | Honda |
Answer:
In this query, we join the and tables on the column. We extract the month from the sale date, and for each month and car model, we count the total number of cars sold and sum the sale price to calculate the total revenue. The results are ordered by month and total sales (in descending order) to present the car models with the highest revenues first.
SQL Question 7: What's a correlated sub-query? How does it differ from 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.
Here is an example of a correlated sub-query:
This query selects the and total sales of all CarMax customers in the sales table whose are greater than the average of their own customer group. The sub-query in this case is correlated with the outer query, as it references the column from the outer query ().
Here is an example of a non-correlated sub-query:
The sub-query in this case is non-correlated, as it does not reference any columns from the outer query.
Correlated sub-queries are slower to execute, as they have to be re-evaluated for each row of the outer query, while non-correlated sub-queries are faster, as they only have to be executed once.
SQL Question 8: Filter Customer Records of CarMax
CarMax is interested in analysing their customer records to drive future business decisions. They want to retrieve a list of customers from a certain city who purchased at least one car above the average price in the last year and did not return it. They also want to know the total spending of each customer.
For this question, use the following tables:
Example Input:
| customer_id | first_name | last_name | city |
|---|---|---|---|
| 1001 | John | Doe | San Francisco |
| 1002 | Jane | Smith | New York |
| 1003 | Max | Miller | San Francisco |
Example Input:
| purchase_id | customer_id | purchase_date | car_id | price | returned? |
|---|---|---|---|---|---|
| 12001 | 1001 | 05/08/2021 | 50001 | 30000 | false |
| 12002 | 1002 | 06/10/2021 | 69852 | 40000 | false |
| 12003 | 1001 | 06/18/2021 | 50001 | 35000 | false |
| 12004 | 1003 | 07/26/2021 | 69852 | 45000 | false |
| 12005 | 1003 | 07/05/2021 | 69852 | 20000 | true |
To achieve the above, write a PostgreSQL query that:
- Filters out customers from the specific city ("San Francisco").
- Retrieves customers who bought at least 1 car that was priced above the overall average price.
- Excludes customers if they returned the car.
- Finally, calculates the total amount each customer spent.
Answer:
This query starts by determining the average price of purchased cars within the last year that were not returned. It then filters the customers from "San Francisco" who have purchased at least one car with a price higher than the calculated overall average. The query also counts how many cars each customer bought and filters out records if a car was returned. Lastly, it calculates the total amount of money each customer has spent. This query helps identify valuable customers from a certain city.
SQL Question 9: Calculate the Average Car Price in Each Year Model
As a data analyst in CarMax, you're asked to find the average selling price of each car model per year. You have been given dataset in the table below which consists of , , and . Create an SQL query to find the average selling price for each and .
Example Input:
| car_id | year_model | car_model | selling_price |
|---|---|---|---|
| 12453 | 2010 | Ford Explorer | 12000 |
| 93247 | 2010 | Ford Explorer | 15000 |
| 32954 | 2010 | Honda Accord | 9000 |
| 49348 | 2011 | Honda Accord | 9500 |
| 78994 | 2011 | Ford Explorer | 16000 |
| 61232 | 2011 | Ford Explorer | 17000 |
Example Output:
| year_model | car_model | avg_selling_price |
|---|---|---|
| 2010 | Ford Explorer | 13500 |
| 2010 | Honda Accord | 9000 |
| 2011 | Honda Accord | 9500 |
| 2011 | Ford Explorer | 16500 |
Answer:
This SQL query groups the data by and and calculates the average for each group. As a result we get the average selling price for each model per year as required.
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for requiring SQL aggregation to analyze sales data, or this Wayfair Y-on-Y Growth Rate Question which is similar for applying SQL querying on data grouped by year.
SQL Question 10: Can you describe the role of the constraint and provide an example of a situation where it might be applied?
The CHECK constraint is used to enforce rules on the data in a specific column. If a row is inserted or updated with data that does not follow the CHECK constraint's rule, the operation will fail.
For example, say you had a marketing analytics database that stores ad campaign data from CarMax's Google Analytics account.
Here's what some constraints could look like:
The CHECK constraint is used in the above example to make sure that the "budget" and "cost_per_click" fields contain only positive values. This helps to maintain the validity and integrity of the data in the context of ad campaigns.
The CHECK constraint can also be used to enforce other specific conditions on data. For example, you can use it to ensure that the "start_date" for each ad campaign is earlier than the "end_date."
How To Prepare for the CarMax SQL Interview
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.
Besides solving the above CarMax SQL interview questions, you should also solve the 200+ SQL coding questions which come from companies like Netflix, Airbnb, and Amazon.
Each exercise has hints to guide you, step-by-step solutions and most importantly, there is an online SQL code editor so you can instantly run your SQL query and have it graded.
To prep for the CarMax SQL interview it is also wise to solve interview questions from other automotive companies like:
But if your SQL coding 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 4 types of JOINS and math functions – both of which show up frequently in SQL job interviews at CarMax.
CarMax Data Science Interview Tips
What Do CarMax Data Science Interviews Cover?
Besides SQL interview questions, the other topics to practice for the CarMax Data Science Interview are:
- Statistics and Probability Questions
- Python Pandas or R Coding Questions
- Analytics and Product-Metrics Questions
- ML Interview Questions
- Behavioral Interview Questions
How To Prepare for CarMax Data Science Interviews?
I'm a tad biased, but I believe the optimal way to prep for CarMax Data Science interviews is to read the book I wrote: Ace the Data Science Interview.
The book covers 201 interview questions taken from companies like Google, Tesla, & Goldman Sachs. It also has a crash course covering Stats, ML, & Data Case Studies. And finally it's helped thousands of people land their dream job in data, which is why it's earned 1000+ reviews on Amazon and a 4.5-star rating.