8 Ford SQL Interview Questions (Updated 2024)
At Ford Motor, SQL is used for analyzing manufacturing data for quality control, and managing the large data sets for predictive maintenance. So, it shouldn't surprise you that Ford typically asks SQL questions in interviews for Data Science, Analytics, and & Data Engineering jobs.
Thus, to help prep you for the Ford SQL interview, we've collected 8 Ford Motor SQL interview questions can you solve them?
8 Ford Motor SQL Interview Questions
SQL Interview Question 1: Monthly Car Sales Analysis
Ford wants to analyze the monthly car sales by different car models. More specifically, the company is interested in knowing the monthly total sales and the month-to-month growth rate (in percentage) for each car model.
Here are two sample tables pertaining to cars and sales:
Example Input:
| car_id | model |
|---|---|
| 1 | Mustang |
| 2 | Explorer |
| 3 | F-150 |
| 4 | Escape |
| 5 | Bronco |
Example Input:
| sales_id | car_id | sale_date | quantity |
|---|---|---|---|
| 101 | 1 | 05/01/2022 | 20 |
| 102 | 1 | 06/01/2022 | 25 |
| 103 | 2 | 05/01/2022 | 15 |
| 104 | 2 | 06/01/2022 | 18 |
| 105 | 3 | 05/01/2022 | 22 |
| 106 | 3 | 07/01/2022 | 26 |
The output should look like this:
Example Output:
| mth | model | total_sales | growth_rate |
|---|---|---|---|
| 5 | Mustang | 20 | N/A |
| 5 | Explorer | 15 | N/A |
| 5 | F-150 | 22 | N/A |
| 6 | Mustang | 25 | 25.00% |
| 6 | Explorer | 18 | 20.00% |
| 7 | F-150 | 26 | 18.18% |
Answer:
Here's a PostgreSQL query to solve the problem:
This query first groups the sales data by month and car model, then calculates the total sales in each month for every car model. It then calculates the growth rate from the previous month's sales, and joins the resulting table with the cars table to display the model names. The result is ordered by car model and month.
Pro Tip: Window functions are a popular SQL interview topic, so practice every window function problem on DataLemur
SQL Question 2: Employees Earning More Than Their Boss
Given a table of Ford employee salaries, write a SQL query to find employees who make more than their direct boss.
Ford 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.
Try 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 code above is hard to understand, you can find a step-by-step solution with hints here: Employees Earning More Than Managers.
SQL Question 3: What are the similarities and differences between a clustered and non-clustered index?
Here is an example of a clustered index on the column of a table of Ford customer transactions:
This creates a clustered index on the column, which will determine the physical order of the data rows in the table.
Here is an example of a non-clustered index on the column of the same table:
This will create a non-clustered index on the column, which will not affect the physical order of the data rows in the table.
In terms of query performance, a clustered index is usually faster for searches that return a large number of records, while a non-clustered index is faster for searches that return a small number of records. However, updates to a clustered index are slower, as they require the data rows to be physically rearranged, while updates to a non-clustered index are faster, as they only require the index data structure to be updated.
SQL Question 4: Car Sales Monitoring
As a Data Analyst for Ford, you were assigned to monitor the inventory and sales of Ford car models across different branches. You have two tables and .
The table structure is as below with sample data:
Example Input:
| inventory_id | branch_id | model_id | count |
|---|---|---|---|
| 1 | 101 | 2001 | 250 |
| 2 | 102 | 2002 | 300 |
| 3 | 101 | 2001 | 150 |
| 4 | 102 | 2003 | 200 |
| 5 | 103 | 2002 | 400 |
In this table, is the unique identifier. and refer to branch and car model identifiers and indicates the number of cars available in inventory.
The table structure is as below with sample data:
Example Input:
| sale_id | branch_id | model_id | sale_date | number_sold |
|---|---|---|---|---|
| 9001 | 101 | 2001 | 06/08/2022 | 50 |
| 9002 | 102 | 2002 | 06/10/2022 | 60 |
| 9003 | 101 | 2001 | 06/18/2022 | 40 |
| 9004 | 102 | 2003 | 07/26/2022 | 45 |
| 9005 | 103 | 2002 | 07/05/2022 | 75 |
Here, is the unique identifier and refers to the number of cars sold for a specific model from a specific branch.
Write a SQL query to determine the remaining inventory for each car model per branch after their corresponding sales.
Answer:
This PostgreSQL query first makes a of and on and , then groups the result by and . It calculates the remaining inventory by subtracting the sum of from the sum of the count in the inventory, using the function to handle cases where there is no sale and is .
SQL Question 5: What's a database view, and what's it used for?
Views are a lot like virtual tables, where you can take a base table and customize it (such as by hiding some data from non-admin users, or removing some random columns/rows based on business requirements).
Here's the PostgreSQL syntax for creating a view based on data in the table:
SQL Question 6: Ford Digital Advertisement Click-Through Conversion Rate
Given the digital advertisement and the vehicle information, calculate the click-through conversion rate, from viewing to adding a vehicle to the enquiry list in a certain month.
Images of Ford vehicles are scattered across web pages as digital ads. When visitors click on these ads, they are redirected to Ford's website where they can view details about that specific vehicle. The visitors can then add the vehicle to a list of enquiries.
Example Input:
| click_id | user_id | click_date | vehicle_id |
|---|---|---|---|
| 101 | 123 | 06/08/2022 00:00:00 | A1 |
| 202 | 265 | 06/10/2022 00:00:00 | B1 |
| 303 | 362 | 06/18/2022 00:00:00 | A1 |
| 404 | 192 | 07/26/2022 00:00:00 | C1 |
| 505 | 981 | 07/05/2022 00:00:00 | B1 |
Example Input:
| enquiry_id | user_id | enquiry_date | vehicle_id |
|---|---|---|---|
| 909 | 123 | 06/08/2022 00:05:00 | A1 |
| 808 | 362 | 06/18/2022 00:10:00 | A1 |
| 707 | 192 | 07/26/2022 00:20:00 | C1 |
Example Output:
| month | vehicle | click_through_conversion_rate |
|---|---|---|
| 6 | A1 | 1.00 |
| 6 | B1 | 0.00 |
| 7 | C1 | 1.00 |
| 7 | B1 | 0.00 |
Answer:
In this solution, we first separate the and table by the month. Then, we count the number of conversions in each month for each vehicle. Finally, we calculate the click-through conversion rate by dividing the number of conversions by the total clicks for that vehicle in each month.
To practice another question about calculating rates, solve this TikTok SQL question within DataLemur's online SQL coding environment:
SQL Question 7: What's a self-join, and when would you use one?
A self-join is a type of JOIN where a table is joined to itself. To execute a self-join, you must include the table name twice in the FROM clause and assign a different alias to each instance. You can then join the two copies of the table using a JOIN clause, and use a WHERE clause to specify the relationship between the rows.
One common use case for self-joins is to compare data within a single table. For example, you might use a self-join to compare the salaries of employees within the same department, or to identify pairs of products that are frequently purchased together (like in this real Walmart SQL interview question)[https://datalemur.com/questions/frequently-purchased-pairs].
For a more concrete example, imagine you had website visitor data for Ford, exported from the company's Google Analytics account. To help the web-dev team, you needed to analyze pairs of pages for UX or navigational issues. As part of that analysis, you wanted to produce all pairs of URLs, but needed to exclude pairs where both URLs were the same since that is not a valid pair.
You could use the following self-join:
This query retrieves the url of each page () along with the url of the page that referred to it (). The self-join is performed using the , which identifies the id of the page that referred the visitor to the current page, and excludes any pages that referred to themselves (i.e., data anomalies since pages shouldn't refer to themself).
Make sure you take the time to learn about the company's vision and goals! Read about how Ford motors has helped US veterans over the years.
SQL Question 8: Joining and Analyzing Customer and Vehicle Sales Data
Given a table that lists all customers along with their details such as , , , and a table that records all vehicle sales along with details such as , , , and .
Write a SQL query to find out the total number of vehicles sold per location in the year 2020.
Example Input:
| customer_id | name | location |
|---|---|---|
| 001 | John Doe | New York |
| 002 | Jane Smith | Los Angeles |
| 003 | Mary Johnson | New York |
| 004 | James Brown | Chicago |
| 005 | Jennifer Davis | Los Angeles |
Example Input:
| sale_id | vehicle_id | customer_id | sale_date |
|---|---|---|---|
| 101 | A01 | 001 | 2020-05-09 |
| 102 | A02 | 002 | 2020-03-10 |
| 103 | B01 | 003 | 2020-07-22 |
| 104 | C01 | 004 | 2020-10-15 |
| 105 | A03 | 004 | 2019-06-28 |
| 106 | B02 | 005 | 2020-09-04 |
Answer:
Your query would look something like this:
This query will first join the and tables using the field in both tables. It then filters the results to only include sales from 2020. Lastly, it groups the results by customer location and counts the number of vehicles sold in each location.
Because joins come up so often during SQL interviews, take a stab at this interactive Snapchat JOIN SQL interview question:
Ford SQL Interview Tips
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Ford SQL interview is to solve as many practice SQL interview questions as you can!
Beyond just solving the above Ford SQL interview questions, you should also solve the 200+ SQL coding questions which come from companies like FAANG and tech startups.
Each exercise has multiple hints, detailed solutions and most importantly, there is an interactive coding environment so you can easily right in the browser your query and have it checked.
To prep for the Ford SQL interview it is also helpful to solve SQL questions from other automotive companies like:
But if your SQL foundations are weak, don't worry about going right into solving questions – improve your SQL foundations with this DataLemur SQL tutorial.
This tutorial covers things like SQL joins with practice exercises and Self-Joins – both of these come up often during Ford interviews.
Ford Motor Data Science Interview Tips
What Do Ford Data Science Interviews Cover?
In addition to SQL query questions, the other topics covered in the Ford Data Science Interview include:
- Probability & Stats Questions
- Python or R Coding Questions
- Product Data Science Interview Questions
- ML Interview Questions
- Resume-Based Behavioral Questions
How To Prepare for Ford Data Science Interviews?
I'm a bit biased, but I believe the best way to prep for Ford Data Science interviews is to read my book Ace the Data Science Interview.
The book solves 201 interview questions taken from companies like Microsoft, Google & Amazon. It also has a refresher covering Python, SQL & ML. And finally it's vouched for by the data community, which is why it's earned 1000+ reviews on Amazon and a 4.5-star rating.