# 9 Meritor SQL Interview Questions (Updated 2024)

Updated on

June 30, 2024

Data Analysts & Data Scientists at Meritor code up SQL queries as a big part of their job. They use SQL for analyzing vehicle performance data and managing supply chain databases. That's why Meritor typically asks folks interviewing for data jobs SQL interview problems.

Thus, to help prep you for the Meritor SQL interview, we've curated 9 Meritor SQL interview questions in this article.

## 9 Meritor SQL Interview Questions

### SQL Question 1: Identify Top-Spending Customers in Meritor

In Meritor, a leading provider of products for commercial vehicles, customers make regular purchases and some of them are high-value customers because of their high spending pattern. Your task is to write a SQL query to identify the top 10 customers who have spent the most on their total orders in the last 12 months.

Consider the below datasets for the problem,

##### Example Input:
order_idcustomer_idorder_dateproduct_idorder_amount
10112021-08-15 00:00:00A001500
10222021-09-10 00:00:00B002700
10312021-10-12 00:00:00C0031500
10432021-11-18 00:00:00D0042000
10522022-01-05 00:00:00E0052500
##### Example Input:
customer_idcustomer_name
1John
2Jane
3David

In this query, we're joining the and tables on the field. We're limiting the orders to the ones that have been made in the last 12 months. Then, we're grouping the records by customer names and calculating the total amount they spent on their orders. The result is ordered in descending order by and limited to the top 10 to identify the top spenders.

To practice another SQL customer analytics question where you can solve it right in the browser and have your SQL solution instantly graded, try this Walmart Labs SQL Interview Question:

### SQL Question 2: Department vs. Company Salary

Imagine there was a table of Meritor 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.

Try this problem directly within the browser on DataLemur:

The solution is LONG – 30+ lines of SQL. You can find a step-by-step solution here: Department Salaries.

Learn and understand what products Meritor sells and check out their product search page!

### SQL Question 3: Why is database normalization a good idea?

Database normalization has several benefits:

• Reduces Redundancy: Normalization can minimize redundancy by breaking down a larger, general table into smaller, more granular tables. This often reduces the amount of data that needs to be accessed for particular queries, since some duplicated columns can be removed.

• Improves Data Integrity: Normalization can help to ensure the integrity of the data by minimizing the risk of data inconsistencies and errors. By establishing clear relationships between the tables via primary and foreign keys, and enforcing these constraints, you can have more reliable records and relationships stored in your DB.

• Improves Performance: Normalization can improve the performance of the database by reducing the amount of data that needs to be stored and accessed in a single table. This results in faster query times and better overall performance.

### SQL Question 4: Monthly Average Sales Volumes Per Product for Meritor

The management team at Meritor, a global supplier of drivetrain, mobility, braking, and aftermarket solutions for commercial vehicle and industrial markets, wants to understand the monthly average sales volumes per product. This is critical for supply chain management and forecasting.

The data is found in two main datasets: and tables.

The table includes each product's sales transactions, with fields: , , , .

The table contains and .

Write a SQL query to calculate the monthly average of sales volumes for each product, sorted by month and product id.

##### Example Input:
transaction_idproduct_idtransaction_datequantity
11012022-01-1410
21022022-01-2120
31012022-02-0515
41032022-02-1530
51022022-02-2225
##### Example Input:
product_idproduct_name
101Product X
102Product Y
103Product Z

In PostgreSQL, the query would be written like this:

This query first joins the sales and products data on the field. The function truncates the to month. The aggregate function calculates the monthly average quantity per product. Finally, the results are ordered by month and product id.

To practice a related window function SQL problem on DataLemur's free interactive coding environment, try this Amazon SQL question asked in a BI Engineer interview:

### SQL Question 5: What does it mean to perform a self-join?

A self-join is a type of join in which a table is joined to itself. To perform a self-join, you need to specify the table name twice in the clause, and give each instance of the table a different alias. You can then join the two instances of the table using a clause, and use a clause to specify the relationship between the rows.

For example, say you were doing an HR analytics project and needed to analyze how much all Meritor employees in the same department interact with each other. Here's a self-join query you could use to retrieve all pairs of Meritor employees who work in the same department:

This query returns all pairs of Meritor employees who work in the same department, and excludes pairs where the employee's id is the same (since this would represent the same Meritor employee being paired with themselves).

### SQL Question 6: Departmental Sales Performance

Meritor is a provider of innovative products such as truck, trailer, and industrial drivetrain and drive axle systems. They have several departments where they track the sales. Assume that you have been asked to design a database that can help keep track of the sales data for better analysis and decision making.

For this task, consider the following scenario:

Management wants to track performance of each department on a monthly basis. They want to see average monthly sales as well as any sales that are outside the monthly averages for each department.

##### Example Input:
sales_iddept_idsales_dateproduct_idunits
1001106/01/2022 00:00:004000120
1002206/02/2022 00:00:004000225
1002306/03/2022 00:00:004000323
1003106/04/2022 00:00:004000422
1004206/05/2022 00:00:004000124
##### Example Input:
product_idproduct_price
40001500
40002550
40003450
40004600
##### Required Output:
dept_idmonthaverage_salesoutliers
165200null
26null5500
364500null

Build a Postgre SQL query that can return the required output.

This query first calculates the average monthly sales for each department. After that, it looks for sales that are outside (below or above) the average monthly sales for the same department. The final query merges these two to get the required output. The department, month, average sales and sales outliers are returned for each department. Outside sales are considered as sales that are not equal to the average sales for that department during the same month.

### SQL Question 7: Why might you denormalize a database?

Denormalization is the practice of altering a database schema in a way that breaks the normalization rules (1st, 2nd, 3rd normal forms).

A few reasons to denormalize a database:

• Improved performance: Denormalization often reduces the the number of costly join operations that are needed to retrieve data. This is helpful when the database is being used for OLAP (Online Analytical Processing) use cases at Meritor, as joins can be expensive and slow.

• Scalability: Denormalization can be like a turbo boost for your database's scalability! By requiring less data to be read and processed when executing a query, denormalization can help your database handle a ton of queries without breaking a sweat.

• Simplification: One way to simplify the design of a database is by using denormalization to reduce the number of tables and relationships that need to be managed. This can make the database easier to understand and maintain.

Of course, don't over-do the database denormalization magic – too much redundancy can increase data storage costs, and get complicated to manage if there's frequent commands that need to be run.

### SQL Question 8: Average Hours Worked Per Week Per Department

As a Business Intelligence Analyst at Meritor, a leading global supplier of drivetrain, mobility, braking and aftermarket solutions for commercial vehicle and industrial markets, you have been tasked with finding the average hours worked per week by employees from each department. You have access to the employee table, which includes employee IDs, department names, and hours worked each week.

##### Example Input:
employee_iddepartmenthours_worked_per_week
1001Manufacturing40
1002Sales35
1003Research and Development45
1004Manufacturing38
1005Sales33
1006Human Resources40
##### Example Output:
departmentavg_hours_worked_per_week
Manufacturing39
Sales34
Research and Development45
Human Resources40

In this PostgreSQL query, we fetch the department and the average hours worked per week from the employee table, and group the result by the department. The AVG function calculates the average of the values in a group, in this case the average hours worked per week for each department. As a result, we get the average hours worked per week for each department.

To practice a very similar question try this interactive Alibaba Compressed Mean Question which is similar for requiring computation of averages or this Amazon Average Review Ratings Question which is similar for requiring data analysis grouped by a particular category.

### SQL Question 9: Filtering Customer Records Based on Email Addresses

As a data analyst at “Meritor”, you are provided with a customer records database. You suspect that there are duplicate records of customers due to different formats of emails used when registering on different platforms. The email formats in question include:

Write an SQL query that selects all customer records where the email column matches the string 'johnDoe@xyz.com' irrespective of the case and periods (.) in the customer email.

##### Example Input:
customer_idnameemailsignup_datesignup_platform
1012John DoejohnDoe@xyz.com04/08/2021Web
2025John DoeJOHNDOE@xyz.com07/12/2021Android App
3154John Doejohn.doe@xyz.com09/20/2021Web

This query lowercases all email addresses, removes periods, and compares them to our pattern "johndoe@xyz.com" to fetch the wanted entries. It will help Meritor identify customer entries that may not immediately appear to be duplicates due to slight differences in formatting conventions across different platforms.

### Meritor SQL Interview Tips

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 Meritor SQL interview questions, you should also solve the 200+ SQL questions on DataLemur which come from companies like Google, Microsoft and Silicon Valley startups.

Each interview question has multiple hints, step-by-step solutions and most importantly, there is an interactive SQL code editor so you can right in the browser run your SQL query and have it graded.

To prep for the Meritor SQL interview you can also be a great idea to solve SQL questions from other automotive companies like:

In case your SQL query skills are weak, don't worry about jumping right into solving questions – refresh your SQL knowledge with this SQL tutorial for Data Analytics.

This tutorial covers things like math functions in SQL and filtering strings using LIKE – both of these show up frequently in SQL job interviews at Meritor.

### Meritor Data Science Interview Tips

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

In addition to SQL interview questions, the other topics to prepare for the Meritor Data Science Interview include:

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

To prepare for Meritor Data Science interviews read the book Ace the Data Science Interview because it's got: