# 10 Trimble SQL Interview Questions (Updated 2024)

Updated on

March 1, 2024

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?

## 10 Trimble SQL Interview Questions

### SQL Question 1: Identify the Top Buyers for Trimble

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.

##### Example Input:
order_iduser_idpurchase_dateproduct_idpurchase_amount
10132101/05/2022 10:00:003000130000
20265701/20/2022 14:00:003000255000
30332102/05/2022 10:00:003000130000
40412302/15/2022 16:00:003000260000
50565703/10/2022 10:00:003000130000
##### Example Output:
user_idnumber_of_whale_months
3212
6571

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:

### SQL Question 2: Average Monthly Ratings for Trimble Products

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.

##### Example Input:
review_iduser_idsubmit_dateproduct_idstars
617112306/08/2022 00:00:00500014
780226506/10/2022 00:00:00698524
529336206/18/2022 00:00:00500013
635219207/26/2022 00:00:00698523
451798107/05/2022 00:00:00698522
##### Example Output:
mthproduct_idavg_stars
6500013.50
6698524.00
7698522.50

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:

### SQL Question 3: How would you speed up a slow SQL query?

Here's some strategies that can generally speed up a slow SQL query:

• Only query the columns you actually need
• Index the columns used in the query
• Use the right kind of JOIN (be explicit about inner vs. outer joins)
• Use the right data types (for example INT vs BIGINT can save you disk space and memory)

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.

### SQL Question 4: Analyzing Click-Through Rates for Trimble

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.

#### Table Example Input:

click_iduser_idclick_dateproduct_idcategory_id
101221009/10/2022 00:00:00400120
101356309/10/2022 00:00:00400210
101438909/11/2022 00:00:00400330
101582409/12/2022 00:00:00400120
101621009/12/2022 00:00:00400210

#### Table Example Input:

conversion_iduser_idconversion_dateproduct_id
52121009/11/2022 00:00:004001
52238909/12/2022 00:00:004003
52356309/12/2022 00:00:004002
52482409/13/2022 00:00:004001

#### Example Output:

category_idclick_through_conversion_rate
10100%
2050%
30100%

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:

### SQL Question 5: Can you describe the different types of joins in SQL?

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 .

• : retrieves rows from both tables where there is a match in the shared key or keys. For example, an INNER JOIN between the Sales table and the Customers table would retrieve only the rows where the customer_id in the Sales table matches the customer_id in the table.
• : retrieves all rows from the left table (in this case, the sales table) and any matching rows from the right table (the table). If there is no match in the right table, NULL values will be returned for the right table's columns.
• : retrieves all rows from the right table (in this case, the customers table) and any matching rows from the left table (the sales table). If there is no match in the left table, NULL values will be returned for the left table's columns.
• : retrieves all rows from both tables, regardless of whether there is a match in the shared key or keys. If there is no match, NULL values will be returned for the columns of the non-matching table.

### SQL Question 6: Finding Specific Customer Patterns

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.

##### Example Input:
customer_idfirst_namelast_nameemail
101AlexSmithalexsmith@gmail.com
102AndrewJohnsonandrewjohnson@yahoo.com
103AliceWilliamsalicewilliams@gmail.com
104AbbyMorganabbymorgan@hotmail.com
105AmyWillisamywillis@yahoo.com
106AustinThompsonaustinthompson@gmail.com

##### Example Output:
customer_idfirst_nameemail
101Alexalexsmith@gmail.com
103Alicealicewilliams@gmail.com
106Austinaustinthompson@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'.

### SQL Question 7: Can you explain the distinction between a left and right join?

"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.

### SQL Question 8: Joining and Analyzing Customer and Product Tables

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:

##### Example Input:
customer_idfirst_namelast_nameemail
123JohnDoejohndoe@example.com
456JaneSmithjanesmith@example.com
789MarkTaylormarktaylor@example.com
##### Example Input:
product_idproduct_nameprice
111Tool A59.99
222Tool B79.99
333Tool C99.99
##### Example Input:
order_idcustomer_idproduct_idpurchase_date
999112311102/12/2022
999212322203/18/2022
999345611104/22/2022
999478922205/15/2022
999512311106/20/2022

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:

### SQL Question 9: Calculate Product Performance Metrics

Trimble is interested in analyzing how their products are performing. Specifically, they want to analyze the following metrics:

• Average cost of each product, rounded to two decimal points.
• Square root of the sum of quantities sold for each product.
• The absolute difference between the highest and lowest cost of each product.
• The power difference (power of 3) between the highest and lowest cost of each product.

For simplicity, let's assume we have a products table and a sales table.

##### Example Input:
product_idproduct_namecost
P01iPhone900
P02Nintendo300
P03PlayStation500
P04MacBook1200
P05Smart Watch350
##### Example Input:
sale_idproduct_idquantity
S001P0110
S002P0120
S003P0218
S004P0325
S005P0415
S006P0530

This PostgreSQL query first joins the products table with the sales table on product_id. It then calculates the desired metrics for each product:

• The function is used to round the average cost to 2 decimal points.
• The function calculates the square root of the sum of quantities sold.
• The function calculates the absolute cost difference between the highest and lowest cost of each product.
• The function calculates the power difference (power of 3) between the highest and lowest cost of 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.

### SQL Question 10: Can you explain the concept of a constraint in SQL?

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.

### How To Prepare for the Trimble 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 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.

### Trimble Data Science Interview Tips

#### What Do Trimble Data Science Interviews Cover?

Beyond writing SQL queries, the other question categories covered in the Trimble Data Science Interview are:

• Statistics and Probability Questions
• Coding Questions in Python or R
• Business Sense and Product-Sense Questions
• Machine Learning Questions
• Resume-Based Behavioral Questions

#### How To Prepare for Trimble Data Science Interviews?

To prepare for Trimble 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 refresher covering Stats, SQL & ML
• over 900+ reviews on Amazon & 4.5-star rating