8 Carsales SQL Interview Questions (Updated 2024)
At Carsales, SQL is used frequently for extracting and analyzing vehicle sales data to identify trends, and optimize the car-selling experience. Because of this, Carsales typically asks SQL query questions during interviews for Data Analyst, Data Science, and BI jobs.
So, to help you ace the Carsales SQL interview, we've curated 8 Carsales SQL interview questions – able to solve them?
8 Carsales SQL Interview Questions
SQL Question 1: Calculate the average price of cars sold per brand for each month
For Carsales, assume there is a table named which captures the car sales transactions data. This table includes columns for , , , , and .
The question is to write a SQL query that calculates the monthly average sale price for each car brand.
Example Input:
| sale_id | car_id | brand | sold_date | sale_price |
|---|---|---|---|---|
| 101 | 001 | Toyota | 2021-11-20 00:00:00 | 20000 |
| 202 | 002 | Toyota | 2021-11-25 00:00:00 | 22500 |
| 303 | 003 | Toyota | 2021-12-15 00:00:00 | 21000 |
| 404 | 004 | BMW | 2021-11-20 00:00:00 | 30000 |
| 505 | 005 | BMW | 2021-12-30 00:00:00 | 35000 |
Example Output:
| month_of_sale | brand | avg_sale_price |
|---|---|---|
| 11 | Toyota | 21250 |
| 11 | BMW | 30000 |
| 12 | Toyota | 21000 |
| 12 | BMW | 35000 |
Answer:
Consider using the function to truncate the date to monthly and the function to calculate the average sale price. Window functions are not really needed in that case, but you could extend this query to calculate a cumulative average per brand per month which would use window functions.
This query first aggregates the table by and , then it computes the average per brand per month. The results are ordered by and .
The window function is used here to calculate the partitioned average which will give us the cumulative average sale price per month per brand. This is a good illustration of how window functions can be used in SQL to provide deeper insights into our data.
Pro Tip: Window functions are a popular SQL interview topic, so practice all the window function problems on DataLemur
SQL Question 2: Calculate the Click-Through-Rate (CTR) for Ads
For a company like Carsales, understanding click-through rates can be crucial for optimizing their online advertising and improving customer conversions. In this question, you are given two tables: 'ad_clicks' which logs each time a user clicks on an ad, and 'ad_impressions' which logs each time an ad is displayed. Your task is to calculate the click-through rate (CTR) for each ad, which is the number of ad clicks divided by the number of ad impressions.
sample input:
| click_id | user_id | click_date | ad_id |
|---|---|---|---|
| 1 | 123 | 06/08/2022 00:00:00 | A543 |
| 2 | 265 | 06/10/2022 00:00:00 | Z897 |
| 3 | 362 | 06/18/2022 00:00:00 | B236 |
| 4 | 123 | 07/26/2022 00:00:00 | A543 |
| 5 | 981 | 07/05/2022 00:00:00 | B236 |
sample input:
| impression_id | user_id | impression_date | ad_id |
|---|---|---|---|
| 9786 | 123 | 06/06/2022 00:00:00 | A543 |
| 7645 | 362 | 06/17/2022 00:00:00 | B236 |
| 5293 | 150 | 06/18/2022 00:00:00 | Z897 |
| 7852 | 252 | 07/26/2022 00:00:00 | Z897 |
| 4517 | 981 | 07/05/2022 00:00:00 | A543 |
Answer:
This query first joins the 'ad_impressions' and 'ad_clicks' tables on 'ad_id' and 'user_id'. Then for each ad, it counts the number of click events and divides it by the total number of impressions to calculate the CTR. Impression events without corresponding click events are considered as not clicked thus contribute 0 to the numerator. The CTR is returned as a decimal value.
To solve another question about calculating rates, try this TikTok SQL Interview Question on DataLemur's interactive SQL code editor:
SQL Question 3: Why would it make sense to denormalize a database?
Imagine you've got giant AF jigsaw puzzle with thousands of pieces, and each piece represents a piece of data in your database. You've spent hours organizing the puzzle into neat little piles, with each pile representing a table in your database. This process is called normalization, and it's a great way to make your database efficient, flexible, and easy to maintain.
But what if you want to solve the puzzle faster (aka make your queries run faster?)?
That's where denormalization comes in – Denormalization is like the puzzle-solving equivalent of taking a shortcut!
Instead of putting all the pieces in separate piles, you might decide to clone some of the pieces, and then have that one puzzle piece be put into multiple piles. Clearly, we are breaking the rules of physics, but that's just like de-normalization because it breaks the normal rules of normalization (1st, 2nd, 3rd normal forms).
By adding redundant puzzle pieces, it can be easier to find the pieces you need, but it also means that you have to be extra careful when you're moving pieces around or adding new ones (aka INSERT/UPDATE commands become more complex).
On the plus side, denormalization can improve the performance of your database and make it easier to use. On the downside, it can make your database more prone to errors and inconsistencies, and it can be harder to update and maintain. In short, denormalization can be a helpful tool, but it's important to use it wisely!
SQL Question 4: Find the Maximum Selling Price for Each Car Brand
Assuming you are a data analyst at Carsales and you are given two tables: one listing information about individual sales records and another with metadata about cars . Your task is to write an SQL query that returns the maximum selling price for each car brand.
Example Input:
| sale_id | car_id | date | price |
|---|---|---|---|
| 1122 | 7905 | 03/08/2022 | $12,000 |
| 5783 | 3245 | 04/08/2022 | $6,500 |
| 9261 | 1183 | 07/08/2022 | $19,500 |
| 3355 | 4977 | 12/08/2022 | $16,000 |
Example Input:
| car_id | brand | model | year |
|---|---|---|---|
| 7905 | Toyota | Camry | 2018 |
| 3245 | Honda | Civic | 2019 |
| 1183 | Mercedes | C Class | 2020 |
| 4977 | BMW | 5 Series | 2022 |
Example Output:
| brand | max_price |
|---|---|
| Toyota | $12,000 |
| Honda | $6,500 |
| Mercedes | $19,500 |
| BMW | $16,000 |
Answer:
Each row in the output of this query represents a car brand and the maximum price at which a car from that brand was sold. The operation merges the and tables based on the field. The clause groups these joined records by , and the aggregate function retrieves the highest sale price from each group.
SQL Question 5: In what circumstances might you choose to denormalize a database?
Normalization can reduce redundancy and improve performance and flexibility in a database.
By dividing larger tables into smaller, more specific ones linked by foreign keys, it is often possible to improve query speed. Note: it is not always the case that query speed will be improved due to costly nature of joins. Still, we think normalization is a good idea because it can also increase flexibility by making it easier to alter individual tables without affecting others, which can be useful for adapting Carsales's database to ever-changing business needs.
SQL Question 6: Find Customers who bought Cars of a specific model in Carsales
As a part of understanding our customers' preferences at Carsales, we want you to find out the list of all customers who have purchased cars of a specific model. For instance, we want you to fetch the list of customers who have purchased cars where the car model contains 'Toyota'.
Example Input:
| sale_id | cust_id | purchase_date | car_id | price |
|---|---|---|---|---|
| 001 | 1258 | 01/10/2022 | C8789 | 20000 |
| 002 | 1350 | 02/14/2022 | B2456 | 15000 |
| 003 | 2589 | 03/05/2022 | T1234 | 22000 |
| 004 | 3529 | 04/02/2022 | T5678 | 25000 |
Example Input:
| car_id | model |
|---|---|
| C8789 | Honda Civic |
| B2456 | Mazda 3 |
| T1234 | Toyota Corolla |
| T5678 | Toyota Camry |
Example Output:
| cust_id |
|---|
| 2589 |
| 3529 |
Answer:
The above SQL query joins the 'Sales' and 'Cars' table using the 'car_id' field. It then filters out the records where the car model is like 'Toyota' using the LIKE operator. We use the DISTINCT keyword to ensure there are no duplicate customer IDs in the result.
SQL Question 7: What's the difference between and clause?
The clause is used to filter the groups created by the clause. It's similar to the clause, but it is used to specify conditions on the groups created by the clause, rather than on the individual rows of the table.
For example, say you were analyzing salaries for analytics employees at Carsales:
This query retrieves the total salary for each Analytics department at Carsales and groups the rows by the specific department (i.e. ""Marketing Analytics"", ""Business Analytics"", ""Sales Analytics"" teams).
The clause then filters the groups to include only Carsales departments where the total salary is greater than $1 million
SQL Question 8: Joining between Customers and Orders
As a Data Analyst at Carsales, you are tasked with finding out the highest selling car model for each customer in the database. For this task, you would be given two tables. The first table, , will contain the details of the customers including the as well as other attributes. The second table, , will contain details of the orders made by these customers. The columns in the table would include , , , and .
Write a SQL query that joins these tables and gives the maximum priced order (i.e., car model) for each customer.
Sample Input:
| customer_id | first_name | last_name | |
|---|---|---|---|
| 101 | John | Doe | john.doe@example.com |
| 102 | Jane | Smith | jane.smith@example.com |
| 103 | Patrick | Johnson | patrick.johnson@example.com |
Sample Input:
| order_id | customer_id | car_model | order_price |
|---|---|---|---|
| 501 | 101 | Toyota Camry | 25000 |
| 502 | 101 | Honda Accord | 27000 |
| 503 | 102 | Ford Focus | 18000 |
| 504 | 103 | Chevy Malibu | 23000 |
Example Output:
| customer_id | first_name | last_name | car_model | order_price | |
|---|---|---|---|---|---|
| 101 | John | Doe | john.doe@example.com | Honda Accord | 27000 |
| 102 | Jane | Smith | jane.smith@example.com | Ford Focus | 18000 |
| 103 | Patrick | Johnson | patrick.johnson@example.com | Chevy Malibu | 23000 |
Answer:
The query begins by grouping the table by and while selecting the highest for each . The output of this subquery is then joined with the table on to give the desired output.
Because joins come up frequently during SQL interviews, take a stab at this Spotify JOIN SQL question:
How To Prepare for the Carsales SQL Interview
The key to acing a Carsales SQL interview is to practice, practice, and then practice some more!
Besides solving the above Carsales SQL interview questions, you should also solve the 200+ DataLemur SQL Interview Questions which come from companies like Facebook, Google, and VC-backed startups.
Each interview question has hints to guide you, detailed solutions and most importantly, there's an interactive coding environment so you can right in the browser run your SQL query and have it graded.
To prep for the Carsales SQL interview you can also be wise to solve SQL questions from other tech companies like:
But if your SQL query skills are weak, forget about jumping right into solving questions – refresh your SQL knowledge with this interactive SQL tutorial.
This tutorial covers topics including GROUP BY and cleaning text data – both of these come up routinely during Carsales interviews.
Carsales Data Science Interview Tips
What Do Carsales Data Science Interviews Cover?
Besides SQL interview questions, the other types of problems tested in the Carsales Data Science Interview are:
- Statistics and AB Testing Questions
- Python or R Coding Questions
- Product Analytics Questions
- Machine Learning and Predictive Modeling Questions
- Behavioral & Resume-Based Questions
How To Prepare for Carsales Data Science Interviews?
To prepare for Carsales Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions taken from FAANG, tech startups, and Wall Street
- a crash course on Python, SQL & ML
- over 900+ reviews on Amazon & 4.5-star rating