9 Paccar SQL Interview Questions (Updated 2024)
Paccar employees write SQL queries daily for analyzing truck performance data across various models and predicting vehicle maintenance needs based on historical data patterns. For this reason Paccar asks SQL problems in interviews for Data Science, Analytics, and & Data Engineering jobs.
Thus, to help prep you for the Paccar SQL interview, here’s 9 Paccar SQL interview questions can you solve them?
9 Paccar SQL Interview Questions
SQL Question 1: Determining VIP Customers for Paccar
Paccar, a Fortune 500 company, is among the largest manufacturers of medium- and heavy-duty commercial vehicles in the world. Therefore, identifying "power users" or "VIP customers" for Paccar could mean pinpointing those customers who purchase vehicles most frequently.
Given two tables:
-
with sample data formatted as:
sales_id customer_id sales_date vehicle_id price 1001 A01 06/02/2022 V5001 50000 1002 B01 06/15/2022 V5002 65000 1003 A01 06/30/2022 V5003 85000 1004 C01 07/10/2022 V5004 70000 1005 B01 07/25/2022 V5002 65000 -
with sample data formatted as:
customer_id customer_name contact email A01 Alan Smith 1234567890 alan@email.com B01 Betty Johnson 0987654321 betty@email.com C01 Charlie Brown 1230984567 charlie@email.com
Write a SQL query to identify VIP customers, defined as those who have made more than one purchase in the past two months.
Answer:
Here is a PostgreSQL query to solve this problem:
This query first creates a temporary table that counts the number of purchases for each customer within the last two months. It then joins this table with the table to get the customer names, filtering only those customers who have more than one purchase, and sorting them in descending order of the number of purchases.
To solve a related super-user data analysis question on DataLemur's free online SQL coding environment, try this Microsoft Teams Power User SQL Interview Question:
SQL Question 2: Department vs. Company Salary
Imagine there was a table of Paccar employee salary data, along with which department they belonged to. Write a query to compare the average salary of employees in each department to the company's average salary. Return the comparison result as 'higher', 'lower', or 'same' for each department. Display the department ID, payment month (in MM-YYYY format), and the comparison.
Solve this question interactively on DataLemur:
The answer is LONG – 30+ lines of SQL. You can find a detailed solution here: Department Salaries.
SQL Question 3: What are SQL constraints, and can you give some examples?
Constraints are just rules your DBMS has to follow when updating/inserting/deleting data.
Say you had a table of Paccar products and a table of Paccar customers. Here's some example SQL constraints you'd use:
NOT NULL: This constraint could be used to ensure that certain columns in the product and customer tables, such as the product name and customer email address, cannot contain NULL values.
UNIQUE: This constraint could be used to ensure that the product IDs and customer IDs are unique. This would prevent duplicate entries in the respective tables.
PRIMARY KEY: This constraint could be used to combine the NOT NULL and UNIQUE constraints to create a primary key for each table. The product ID or customer ID could serve as the primary key.
FOREIGN KEY: This constraint could be used to establish relationships between the Paccar product and customer tables. For example, you could use a foreign key to link the customer ID in the customer table to the customer ID in the product table to track which products each customer has purchased.
CHECK: This constraint could be used to ensure that certain data meets specific conditions. For example, you could use a CHECK constraint to ensure that Paccar product prices are always positive numbers.
DEFAULT: This constraint could be used to specify default values for certain columns. For example, you could use a DEFAULT constraint to set the customer registration date to the current date if no value is provided when a new customer is added to the database.
SQL Question 4: Monthly Average Truck Ratings
Paccar, being a major truck manufacturer globally, keep track of their user ratings for different truck models each month. Suppose, the Paccar team has a table that holds reviews data for different truck models they manufacture.
They would like you to write a SQL query to find out the monthly average star rating ( column) for each truck model ( column) to determine their performance over time.
Example Input:
| review_id | user_id | submit_date | product_id | stars |
|---|---|---|---|---|
| 101 | 208 | 2022-01-15 | T680 | 4 |
| 102 | 215 | 2022-01-20 | T680 | 5 |
| 103 | 309 | 2022-01-25 | T880 | 2 |
| 104 | 412 | 2022-02-05 | T680 | 3 |
| 105 | 415 | 2022-02-20 | T880 | 4 |
| 106 | 520 | 2022-02-25 | T680 | 3 |
| 107 | 625 | 2022-03-05 | T880 | 4 |
| 108 | 625 | 2022-03-10 | T680 | 5 |
| 109 | 712 | 2022-03-15 | T880 | 4 |
| 110 | 815 | 2022-03-20 | T680 | 4 |
Answer:
In this query, the function is used to truncate to the month part of the , making it easier to group by month. The average value is calculated for each product in each month using the window function, and then grouped and ordered by . This SQL query provides a clear view of the monthly average ratings of each truck model.
Pro Tip: Window functions are a frequent SQL interview topic, so practice every window function problem on DataLemur
BTW you should also learn about Paccar's products and services before you enter the interview!!
SQL Question 5: Why would you use the SQL constraint?
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 database that stores ad campaign data from Paccar's Google Analytics account.
Here's what some constraints could look like:
In this example, the constraint is used to ensure that the "budget" and "cost_per_click" fields have positive values. This helps to ensure that the data in the database is valid and makes sense in the context of ad campaigns.
You can also use the constraint to ensure that data meets other specific conditions. For example, you could use a constraint to ensure that the "start_date" is before the "end_date" for each ad campaign.
SQL Question 6: Filter Paccar Customer Records
You are an analyst at Paccar. Our table contains important information about Paccar's customer base. You are tasked with identifying customers from the table based on the following conditions:
Create a SQL query to identify those customers.
Here's an example of how the customer records table looks like:
Table:
| customer_id | name | country | purchase_date | truck_model |
|---|---|---|---|---|
| 1 | John Doe | USA | 2020-07-17 | MX-13 |
| 2 | Jane Smith | USA | 2021-05-01 | PX-9 |
| 3 | Mary Johnson | USA | 2019-12-25 | MX-11 |
| 4 | James Brown | Canada | 2021-11-30 | MX-13 |
| 5 | Patricia Davis | USA | 2021-06-14 | PX-7 |
Answer:
This query filters the table based on the three given conditions. It uses the WHERE clause to filter records where the 'country' is 'USA', the 'purchase_date' is later than '2020-01-01', and the truck model is anything but 'MX-13'. If a record satisfies all these conditions, it will be included in the result.
SQL Question 7: Can you explain the purpose of the constraint?
A is a column or set of columns in a table that references the primary key of another table. It is used to link the two tables together, and to ensure that the data in the foreign key column is valid.
The constraint helps to ensure the integrity of the data in the database by preventing the insertion of rows in the foreign key table that do not have corresponding entries in the primary key table. It also helps to enforce the relationship between the two tables, and can be used to ensure that data is not deleted from the primary key table if there are still references to it in the foreign key table.
For example, if you have a table of Paccar customers and an orders table, the customer_id column in the orders table could be a that references the id column (which is the primary key) in the Paccar customers table.
SQL Question 8: Filter PACCAR's Customer Records Using LIKE Keyword
PACCAR is a global leader in the design, manufacture and customer support of high-quality premium trucks. As a team member, you're asked to filter down customer records to find all customers whose names start with 'John'. You are given a table and your task is to write a SQL query to return all records where the starts with John. Assume all names are oriented in the 'Firstname Lastname' format.
Example Input:
| customer_id | customer_name | signup_date | location | trucks_purchased |
|---|---|---|---|---|
| 32819 | John Smith | 08/05/2022 00:00:00 | Seattle, WA | 3 |
| 84235 | John Doe | 01/12/2022 00:00:00 | Bellevue, WA | 1 |
| 92738 | Bob Jones | 10/14/2022 00:00:00 | Redmond, WA | 2 |
| 39219 | Sarah Green | 07/05/2022 00:00:00 | Tacoma, WA | 1 |
| 12521 | John Harris | 06/21/2022 00:00:00 | Kirkland, WA | 4 |
Adjusted for filtering names starting with 'John':
Example Output:
| customer_id | customer_name | signup_date | location | trucks_purchased |
|---|---|---|---|---|
| 32819 | John Smith | 08/05/2022 00:00:00 | Seattle, WA | 3 |
| 84235 | John Doe | 01/12/2022 00:00:00 | Bellevue, WA | 1 |
| 12521 | John Harris | 06/21/2022 00:00:00 | Kirkland, WA | 4 |
Answer:
This PostgreSQL query filters the table and retrieves all records where begins with 'John'. The keyword is used here with a wildcard (), which represents zero, one, or multiple characters. Therefore, at the end of 'John' will select all customers whose names start with 'John' regardless of what follows after that.
SQL Question 9: Retrieve Sales Information Joined With Customers Data
As part of the data analytics team at PACCAR, you have been asked to write a SQL Query to retrieve and analyze the sales and customer data. You need to join the table and the table using the relevant fields, with an aim to understand the top-selling truck models and their corresponding customer demographics.
Consider that the table and the table are structured as shown below:
Example Input:
| sale_id | customer_id | sale_date | truck_model_id | quantity |
|---|---|---|---|---|
| 4171 | 120 | 08/10/2022 | Z100 | 1 |
| 8160 | 269 | 07/14/2022 | P987 | 3 |
| 6187 | 162 | 07/29/2022 | Z100 | 2 |
| 9220 | 203 | 08/01/2022 | C501 | 1 |
| 3190 | 215 | 07/23/2022 | P987 | 4 |
Example Input:
| customer_id | name | region | company_type |
|---|---|---|---|
| 120 | Company A | West | Logistics |
| 269 | Company B | East | Retail |
| 162 | Company C | South | Logistics |
| 203 | Company D | North | Construction |
| 215 | Company E | West | Retail |
Answer:
This query will return a table containing the truck model id, total quantity sold, region and company type. The data is grouped by truck model id, region and company type. The result is ordered in descending order by the total quantity sold which will let us focus on the top-selling truck models.
Because join questions come up so often during SQL interviews, practice an interactive Spotify JOIN SQL question:
How To Prepare for the Paccar SQL Interview
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Paccar SQL interview is to solve as many practice SQL interview questions as you can!
Beyond just solving the earlier Paccar SQL interview questions, you should also solve the 200+ tricky sql questions on DataLemur which come from companies like Google, Uber, and Microsoft.
Each SQL question has hints to guide you, full answers and most importantly, there's an interactive SQL code editor so you can instantly run your SQL query answer and have it checked.
To prep for the Paccar SQL interview it is also useful to solve SQL problems from other automotive companies like:
But if your SQL coding skills are weak, don't worry about going right into solving questions – refresh your SQL knowledge with this SQL tutorial for Data Analytics.
This tutorial covers topics including math functions like CEIL()/FLOOR() and handling date/timestamp data – both of which pop up routinely in Paccar SQL interviews.
Paccar Data Science Interview Tips
What Do Paccar Data Science Interviews Cover?
Beyond writing SQL queries, the other types of questions to prepare for the Paccar Data Science Interview are:
- Probability & Statistics Questions
- Python Pandas or R Coding Questions
- Product Analytics Questions
- ML Interview Questions
- Behavioral & Resume-Based Questions
How To Prepare for Paccar Data Science Interviews?
To prepare for Paccar Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions taken from Facebook, Google, & Amazon
- a refresher covering Python, SQL & ML
- over 1000+ 5-star reviews on Amazon