8 Monolithic Power Systems SQL Interview Questions (Updated 2025)

At Monolithic Power Systems, SQL is used day-to-day for analyzing electronics production data for efficiency improvements, and managing large datasets for predictive modeling of manufacturing equipment failures. Because of this, Monolithic Power Systems almost always asks SQL coding questions during interviews for Data Science, Analytics, and & Data Engineering jobs.

To help you ace the Monolithic Power Systems SQL interview, this blog covers 8 Monolithic Power Systems SQL interview questions – can you solve them?

8 Monolithic Power Systems SQL Interview Questions

SQL Question 1: Identify Top Paying Customers in Monolithic Power Systems

Monolithic Power Systems wants to identify the 'power users' who have made the most purchases of high-cost items from the business. Define a 'power user' as a customer who has made purchases amounting to over $1000 in total.

Here is your sample data:

customers Example Input:

transactions Example Input:

We need to write a SQL query to identify the 'power users' based on the total purchase amount.

Answer:

Here is a PostgreSQL query that will get us the desired answer:

SELECT c.customer_name, SUM(t.amount) AS total_spent
FROM customers c
JOIN transactions t ON c.customer_id = t.customer_id
GROUP BY c.customer_name
HAVING SUM(t.amount) > 1000;

This query joins the customers and transactions tables on the customer_id field to combine customer and transactional data. It then groups the results by customer_name and calculates the total amount spent for each customer using the SUM() function. The HAVING clause is used to filter out customers who have spent less than $1000.

To solve a related customer analytics question on DataLemur's free interactive coding environment, try this Microsoft Azure Cloud SQL Interview Question:

SQL Question 2: Analyzing Sales Performance

As a data analyst at Monolithic Power Systems, you have access to a database of sales data. Your manager has asked you to write a SQL query to calculate the total sales and average monthly sales of each product over a certain period, as well as the percentage change in sales compared to the previous month.

Here is an example of how the sales data may be structured:

sales_data Example Input:

Example Output:

Answer:

SELECT
    product_id AS product,
    EXTRACT(MONTH FROM sale_date) AS mth,
    SUM(units_sold * price_per_unit) AS total_sales,
    AVG(SUM(units_sold * price_per_unit)) OVER (PARTITION BY product_id ORDER BY EXTRACT(MONTH FROM sale_date)) AS avg_monthly_sales,
    (SUM(units_sold * price_per_unit) - LAG(SUM(units_sold * price_per_unit)) OVER (PARTITION BY product_id ORDER BY EXTRACT(MONTH FROM sale_date))) / LAG(SUM(units_sold * price_per_unit)) OVER (PARTITION BY product_id ORDER BY EXTRACT(MONTH FROM sale_date)) as mth_over_month_change
FROM
    sales_data
GROUP BY
    product_id, EXTRACT(MONTH FROM sale_date)
ORDER BY
    product_id, EXTRACT(MONTH FROM sale_date);

In this query, window functions have been used to calculate the average monthly sales and the month-over-month change in sales. Each row in the output provides the total sales, average monthly sales, and percentage change in sales for a specific product in a specific month.

Pro Tip: Window functions are a popular SQL interview topic, so practice all the window function problems on DataLemur

SQL Question 3: How does HAVING and WHERE differ?

The HAVING clause serves as a filter for the groups created by the GROUP BY clause, similar to how the WHERE clause filters rows. However, HAVING is applied to groups rather than individual rows.

For example, say you were a data analyst at Monolithic Power Systems trying to understand how sales differed by region:

SELECT region, SUM(sales)
FROM monolithic_power_systems_sales
WHERE date > '2023-01-01'
GROUP BY region
HAVING SUM(sales) > 400000;

This query retrieves the total sales for all products in each region, and uses the WHERE clause to only sales made after January 1, 2023. The rows are then grouped by region and the HAVING clause filters the groups to include only those with total sales greater than $400,000.

SQL Question 4: Filter Customer Orders

In Monolithic Power Systems (MPS), one of the key aspects of interest is orders placed by customers. As an SQL developer, you are tasked to filter down orders in the last three months (from the current date) for customers from California who have purchased items worth more than $4000.

The condition for filtering are:

• The Order date should be from the last three months.
• The Customer's state should be 'California'
• The Purchase amount should be greater than $4000

Here are some tables with relevant data:

orders Example Input:

customers Example Input:

Answer:

SELECT o.order_id, o.order_date, o.amount, c.name
FROM orders AS o
JOIN customers AS c
ON o.customer_id=c.customer_id
WHERE c.state='California'
AND o.amount>4000
AND o.order_date>= (CURRENT_DATE - INTERVAL '3 months');

The above SQL query joins the 'orders' and 'customers' tables based on the 'customer_id'. It then filters out the orders based on the specified conditions (state being 'California', order amount being more than $4000, and the order date within the last three months).

SQL Question 5: In SQL, what's the primary difference between the 'BETWEEN' and 'IN' operators?

The BETWEEN operator is used to select rows that fall within a certain range of values, while the IN operator is used to select rows that match values in a specified list.

For example, suppose you are a data analyst at Monolithic Power Systems and have a table of advertising campaign data. To find campaigns with a spend between $1k and$5k, you could use BETWEEN:

SELECT * 
FROM  monolithic_power_systems_ad_campaigns
WHERE spend BETWEEN 1000 AND 5000;

To find advertising campaigns that were video and image based (as opposed to text or billboard ads), you could use the IN operator:

SELECT * 
FROM monolithic_power_systems_ad_campaigns
WHERE media_type IN ("video", "image");

SQL Question 6: Find the Highest Selling Product

Monolithic Power Systems specializes in integrated circuit solutions. They have a Products table that stores information about each product, such as the product ID and name. They also have a Sales table with information about individual sales, including the product ID of the sold item, the sale price, and the date of the sale.

Write a SQL query to analyze the total revenue of each product by combining these two tables. The goal is to find out which product has the highest total revenue.

Products Example Input:

Sales Example Input:

Answer:

SELECT p.product_name, SUM(s.sale_price) AS total_revenue
FROM Products p
JOIN Sales s ON p.product_id = s.product_id
GROUP BY p.product_name
ORDER BY total_revenue DESC
LIMIT 1;

This query first performs a JOIN operation between the Products and Sales tables on the product_id field. It then groups the results based on product_name, calculates the total sales revenue for each product using the SUM function, and selects the product name along with its total revenue. Finally, the results are sorted in descending order of revenue, and only the product with the highest total revenue is returned by limiting the output to the first row.

Because joins come up frequently during SQL interviews, try an interactive SQL join question from Spotify:

SQL Question 7: What's the difference between a one-to-one vs. a one-to-many relationship between two entities? Give examples.

In database schema design, a one-to-one relationship between two entities is where each entity is associated with only one instance of the other entity. For example, the relationship between a US citizen and their social-security number (SSN) is one-to-one, because each citizen can only have one SSN, and each SSN belongs to exactly one person.

On the other hand, a one-to-many relationship is where one entity can be associated with multiple instances of the 2nd entity. For example, each person can be associated with multiple email addresses, but each email address only relates back to one person.

SQL Question 8: Calculate Monthly Average Power Consumption

Monolithic Power Systems is an electronic components provider. One of their key products is equipment used in power management. As an analyst for their software and hardware testing department, your task is to monitor the power consumption of their equipment and provide a monthly average report.

To do this, you'll need to extract the data from their performance test results database. Every time a product undergoes a performance test, the details, including the date, the product ID, and the power consumption in watts, are recorded in the testing_reports table.

Here is a recent sample of data from that table:

testing_reports Example Input:

Based on the table above, write a SQL query that calculates the average power consumption per month for each product.

Answer:

In PostgreSQL, the SQL to achieve this is:

SELECT 
    EXTRACT(MONTH FROM test_date) AS mth,
    product_id,
    AVG(wattage) AS avg_wattage 
FROM 
    testing_reports 
GROUP BY 
    EXTRACT(MONTH FROM test_date), product_id
ORDER BY 
    mth, product_id;

This SQL query first extracts the month from the test_date field with the EXTRACT function. Then, it groups the data by the extracted month and product_id before calculating the average wattage per group. It is important to note that the average is calculated after the grouping. Finally, the ORDER BY clause arranges the output by month and product ID.

Preparing For The Monolithic Power Systems SQL Interview

The key to acing a Monolithic Power Systems SQL interview is to practice, practice, and then practice some more! Besides solving the earlier Monolithic Power Systems SQL interview questions, you should also solve the 200+ SQL exercises on DataLemur which come from companies like Netflix, Airbnb, and Amazon.

Each exercise has multiple hints, step-by-step solutions and best of all, there is an interactive coding environment so you can instantly run your SQL query answer and have it checked.

To prep for the Monolithic Power Systems SQL interview it is also a great idea to practice SQL problems from other semiconductor companies like:

• Texas Instruments SQL questions

• NXP Semiconductors SQL questions

• Infineon SQL questions

• STMicroelectronics SQL questions

But if your SQL coding skills are weak, don't worry about jumping right into solving questions – go learn SQL with this SQL tutorial for Data Analytics.

This tutorial covers SQL topics like Subqueries and cleaning text data – both of which pop up often during Monolithic Power Systems SQL interviews.

Monolithic Power Systems Data Science Interview Tips

What Do Monolithic Power Systems Data Science Interviews Cover?

Beyond writing SQL queries, the other types of problems to practice for the Monolithic Power Systems Data Science Interview are:

• Statistics and AB Testing Questions
• Python or R Coding Questions
• Open-Ended Data Case Studies
• Machine Learning and Predictive Modeling Questions
• Behavioral Interview Questions

How To Prepare for Monolithic Power Systems Data Science Interviews?

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

• 201 interview questions sourced from companies like Google, Tesla, & Goldman Sachs
• a refresher on SQL, AB Testing & ML
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