At Trimble, SQL is used day-to-day for managing geospatial data, and analyzing transportation and logistics data for efficiency improvements. Unsurprisingly this is why Trimble frequently asks SQL query questions during interviews for Data Science and Data Engineering positions.
To help you ace the Trimble SQL interview, we've collected 10 Trimble SQL interview questions – how many can you solve?
You are a data analyst in Trimble Inc, a company that provides GPS, laser, optical, and inertial technologies, as well as wireless communications and application specific software to located, measure, monitor, and assess.
Trimble wants to distinguish their most active customers, referred to as 'whale' users, that frequently purchase high-dollar solutions. Write a SQL query that identifies these 'whale' customers. To be a 'whale', a customer should have spent more than $50,000 within a month at least twice in the year.
order_id | user_id | purchase_date | product_id | purchase_amount |
---|---|---|---|---|
101 | 321 | 01/05/2022 10:00:00 | 30001 | 30000 |
202 | 657 | 01/20/2022 14:00:00 | 30002 | 55000 |
303 | 321 | 02/05/2022 10:00:00 | 30001 | 30000 |
404 | 123 | 02/15/2022 16:00:00 | 30002 | 60000 |
505 | 657 | 03/10/2022 10:00:00 | 30001 | 30000 |
user_id | number_of_whale_months |
---|---|
321 | 2 |
657 | 1 |
Here's the SQL query:
This statement first groups the orders table by users and month, then calculates the monthly purchases for each user. It selects only the records where the total monthly spending is greater than $50,000, which are stored as a subquery. Then it groups these records by users and counts the number of months that each user is considered a 'whale'. It finally selects the users who have been 'whales' for more than two months in the year.
To practice a similar power-user data analysis problem question on DataLemur's free interactive coding environment, try this Microsoft Teams Power User SQL Interview Question:
Imagine you're a data analyst at Trimble, a company specializing in software, hardware and services for industries such as agriculture, construction, geospatial and transportation. The Product department wants to improve their products based on customer reviews.
They have a table containing all product reviews by customers. In this table, each row represents a product review by a user.
The table has the following columns: (the ID of the review), , (the date at which the review was submitted), (the ID of the reviewed product) and (the number of stars awarded to the product).
Write a SQL query to determine the average star rating for each product per month.
review_id | user_id | submit_date | product_id | stars |
---|---|---|---|---|
6171 | 123 | 06/08/2022 00:00:00 | 50001 | 4 |
7802 | 265 | 06/10/2022 00:00:00 | 69852 | 4 |
5293 | 362 | 06/18/2022 00:00:00 | 50001 | 3 |
6352 | 192 | 07/26/2022 00:00:00 | 69852 | 3 |
4517 | 981 | 07/05/2022 00:00:00 | 69852 | 2 |
mth | product_id | avg_stars |
---|---|---|
6 | 50001 | 3.50 |
6 | 69852 | 4.00 |
7 | 69852 | 2.50 |
This query firstly extracts the month from the submit_date. It then groups by both product_id and the extracted month to get the average stars for each product per month. We order the result by month and product_id to make it easier to read and analyze. Please note that the AVG function returns the average value of a group, which in this case is the average star rating by users for each product each month.
To solve another window function question on DataLemur's free interactive coding environment, solve this Amazon BI Engineer interview question:
Here's some strategies that can generally speed up a slow SQL query:
While there's an entire industry dedicated to database performance tuning , for Data Analyst and Data Science interviews at Trimble, knowing the above basics should be more than enough to ace the SQL interview! Data Engineers on the other hand should know more about the command and understand a bit more about a query-execution plan.
As a Data Analyst at Trimble, a leading provider of advanced positioning solutions, you are asked to analyze the company's online click-through rates. You have access to a table, which records every time a user clicks on an ad, and a table, which records every time a user makes a purchase after clicking an ad. Calculate the click-through conversion rate for each product category.
click_id | user_id | click_date | product_id | category_id |
---|---|---|---|---|
1012 | 210 | 09/10/2022 00:00:00 | 4001 | 20 |
1013 | 563 | 09/10/2022 00:00:00 | 4002 | 10 |
1014 | 389 | 09/11/2022 00:00:00 | 4003 | 30 |
1015 | 824 | 09/12/2022 00:00:00 | 4001 | 20 |
1016 | 210 | 09/12/2022 00:00:00 | 4002 | 10 |
conversion_id | user_id | conversion_date | product_id |
---|---|---|---|
521 | 210 | 09/11/2022 00:00:00 | 4001 |
522 | 389 | 09/12/2022 00:00:00 | 4003 |
523 | 563 | 09/12/2022 00:00:00 | 4002 |
524 | 824 | 09/13/2022 00:00:00 | 4001 |
category_id | click_through_conversion_rate |
---|---|
10 | 100% |
20 | 50% |
30 | 100% |
Here's an SQL query that would calculate the click-through conversion rate for each product category:
Explanation:
This query calculates the 'click-through conversion rate' for each product category by counting the number of unique users who made a purchase ( table) after clicking an ad ( table). The count is then divided by the total number of unique users who clicked on an ad per category and multiplied by 100 to get the rate in percentage format. The LEFT JOIN ensures that all clicks are included even if they did not result in a conversion.
To solve a related problem on DataLemur's free online SQL coding environment, try this SQL interview question asked by Facebook:
Joins in SQL allow you to combine data from different tables based on a shared key or set of keys.
Four JOIN types are available in SQL. For an example of each one, say you had sales data exported from Trimble's Salesforce CRM stored in a PostgreSQL database, and had access to two tables: and .
Given a database of Trimble's customer data, find all the customers whose starts with 'A' and contains 'gmail'. This query will allow us to segment customers based on naming patterns and different email providers which can be useful for targeting specific marketing campaigns.
customer_id | first_name | last_name | |
---|---|---|---|
101 | Alex | Smith | alexsmith@gmail.com |
102 | Andrew | Johnson | andrewjohnson@yahoo.com |
103 | Alice | Williams | alicewilliams@gmail.com |
104 | Abby | Morgan | abbymorgan@hotmail.com |
105 | Amy | Willis | amywillis@yahoo.com |
106 | Austin | Thompson | austinthompson@gmail.com |
customer_id | first_name | |
---|---|---|
101 | Alex | alexsmith@gmail.com |
103 | Alice | alicewilliams@gmail.com |
106 | Austin | austinthompson@gmail.com |
This query returns all customers whose first names start with the letter 'A' and whose email addresses end with 'gmail.com'. The SQL keyword is used with a pattern containing the '%' wildcard character, which can represent zero, one, or multiple characters in a string. The 'A%' pattern will match any string that starts with 'A', and the '%gmail.com' pattern will match any string that ends with 'gmail.com'.
"In SQL, a join generally retrieves rows from multiple tables and combines them into a single result set. For an example of the difference between a left vs. right join, suppose you had a table of Trimble orders and Trimble customers.
A retrieves all rows from the left table (in this case, the Orders table) and any matching rows from the right table (the Customers table). If there is no match in the right table, NULL values will be returned for the right table's columns.
A combines all rows from the right table (in this case, the Customers table) and any matching rows from the left table (the Orders table). If there is no match in the left table, NULL values will be displayed for the left table's columns.
Please write a SQL query that connects the customer and product tables, and returns the total number of products purchased by each customer in the year 2022.
Use the following tables as sample data:
customer_id | first_name | last_name | |
---|---|---|---|
123 | John | Doe | johndoe@example.com |
456 | Jane | Smith | janesmith@example.com |
789 | Mark | Taylor | marktaylor@example.com |
product_id | product_name | price |
---|---|---|
111 | Tool A | 59.99 |
222 | Tool B | 79.99 |
333 | Tool C | 99.99 |
order_id | customer_id | product_id | purchase_date |
---|---|---|---|
9991 | 123 | 111 | 02/12/2022 |
9992 | 123 | 222 | 03/18/2022 |
9993 | 456 | 111 | 04/22/2022 |
9994 | 789 | 222 | 05/15/2022 |
9995 | 123 | 111 | 06/20/2022 |
This query will join the and tables together on the field. It will then filter results to include only orders made in the year 2022, before grouping by the and counting the number of products each customer purchased within the year.
Because joins come up so often during SQL interviews, try this interactive Snapchat JOIN SQL interview question:
Trimble is interested in analyzing how their products are performing. Specifically, they want to analyze the following metrics:
For simplicity, let's assume we have a products table and a sales table.
product_id | product_name | cost |
---|---|---|
P01 | iPhone | 900 |
P02 | Nintendo | 300 |
P03 | PlayStation | 500 |
P04 | MacBook | 1200 |
P05 | Smart Watch | 350 |
sale_id | product_id | quantity |
---|---|---|
S001 | P01 | 10 |
S002 | P01 | 20 |
S003 | P02 | 18 |
S004 | P03 | 25 |
S005 | P04 | 15 |
S006 | P05 | 30 |
This PostgreSQL query first joins the products table with the sales table on product_id. It then calculates the desired metrics for each product:
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for calculating metrics related to products or this Walmart Histogram of Users and Purchases Question which is similar for calculating product and user quantities.
Constraints are just rules for your DBMS to follow when updating/inserting/deleting data.
Say you had a table of Trimble employees, and their salaries, job titles, and performance review data. Here's some examples of SQL constraints you could implement:
NOT NULL: This constraint could be used to ensure that certain columns in the employee table, such as the employee's first and last name, cannot contain NULL values.
UNIQUE: This constraint could be used to ensure that the employee ID is unique. This would prevent duplicate entries in the employee table.
PRIMARY KEY: This constraint could be used to combine the NOT NULL and UNIQUE constraints to create a primary key for the employee table. The employee ID could serve as the primary key.
FOREIGN KEY: This constraint could be used to establish relationships between the employee table and other tables in the database. For example, you could use a foreign key to link the employee ID to the department ID in a department table to track which department each employee belongs to.
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 salary values 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 employee hire date to the current date if no value is provided when a new employee is added to the database.
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 Trimble SQL interview questions, you should also solve the 200+ SQL questions from real Data Science & Analytics interviews which come from companies like FAANG tech companies and tech startups.
Each DataLemur SQL question has multiple hints, fully explained answers along with a discussion board to see how others solved it and crucially, there is an online SQL code editor so you can easily right in the browser your SQL query and have it executed.
To prep for the Trimble SQL interview you can also be helpful to practice SQL problems from other tech companies like:
But if your SQL query skills are weak, don't worry about jumping right into solving questions – improve your SQL foundations with this DataLemur SQL Tutorial.
This tutorial covers things like RANK vs. DENSE RANK and manipulating date/time data – both of which come up frequently during SQL interviews at Trimble.
Beyond writing SQL queries, the other question categories covered in the Trimble Data Science Interview are:
To prepare for Trimble Data Science interviews read the book Ace the Data Science Interview because it's got: