10 Masco SQL Interview Questions (Updated 2024)
Masco write SQL queries for analyzing sales data by identifying seasonal spikes in demand for specific home improvement products, tracking sales by region. It is also used for optimizing inventory levels across multiple retail locations by ensuring the right products are stocked at the right stores, the reason why Masco includes SQL questions during interviews for Data Analyst, Data Science, and BI jobs.
As such, to help prep you for the Masco SQL interview, we've collected 10 Masco SQL interview questions – can you solve them?
10 Masco SQL Interview Questions
SQL Question 1: Identify the Most Active Customers
The company "Masco" wants to identify their most active customers based on their purchase frequency and amount spent within a specified time period. The company considers a customer to be a "power user" when they make more than 20 purchases and spend more than $10,000 within any given month.
Below are the sample tables needed for this analysis.
Example Input:
| customer_id | full_name | |
|---|---|---|
| 123 | John Doe | john.doe@example.com |
| 265 | Jane Smith | jane.smith@example.com |
| 362 | Jim Brown | jim.brown@example.com |
Example Input:
| order_id | customer_id | order_date | total_amount |
|---|---|---|---|
| 2632 | 123 | 06/08/2022 00:00:00 | 1000 |
| 3938 | 265 | 06/10/2022 00:00:00 | 500 |
| 9156 | 123 | 06/18/2022 00:00:00 | 3000 |
| 2854 | 123 | 06/20/2022 00:00:00 | 2000 |
| 5692 | 362 | 06/22/2022 00:00:00 | 120 |
| 3385 | 123 | 06/23/2022 00:00:00 | 1500 |
| 8745 | 265 | 06/24/2022 00:00:00 | 1200 |
| 7694 | 123 | 07/01/2022 00:00:00 | 1300 |
Answer:
The query above identifies the most active customers based on Masco's definition of a power user: a customer making more than 20 purchases and spending more than $10,000 within any given month. It groups the orders by customer and truncates the order_date to the nearest month to analyze monthly activity. To focus only on power users, it includes a WHERE clause that filters based on the count of orders and total amount spent per customer.
To practice a similar VIP customer analysis question on DataLemur's free online SQL code editor, try this Microsoft SQL Interview problem:
SQL Question 2: Department vs. Company Salary
You're given a table of Masco employee and department salary data. Write a SQL query to find departments where the departments average salary is higher than the company's average salary.
Code your solution to this problem and run your code right in DataLemur's online SQL environment:
The solution is LONG – 30+ lines of SQL. You can find a step-by-step solution with hints here: Department Salaries.
SQL Question 3: What are some similarities and differences between unique and non-unique indexes?
Unique indexes help ensure that there are no duplicate key values in a table, maintaining data integrity. They enforce uniqueness whenever keys are added or changed within the index.
To define a unique index in PostgreSQL, you can use the following syntax:
To define a non-unique index in PostgreSQL, you can use the following syntax:
Non-unique indexes on the other hand, are used to improve query performance by maintaining a sorted order of frequently used data values, but they do not enforce constraints on the associated table.
SQL Question 4: Analyzing Product Reviews Over Time
Given a table "reviews", a common interview question may be to write a SQL query that ranks all distinct products based on their average star-rating per month, with the product having the highest average score ranking first. The "reviews" table contains product reviews by users along with the date the review was submitted. Your query should use window functions to achieve the desired result.
Example Input:
| review_id | user_id | submit_date | product_id | stars |
|---|---|---|---|---|
| 6171 | 123 | 2022-06-08 | 50001 | 4 |
| 7802 | 265 | 2022-06-10 | 69852 | 4 |
| 5293 | 362 | 2022-06-18 | 50001 | 3 |
| 6352 | 192 | 2022-07-26 | 69852 | 3 |
| 4517 | 981 | 2022-07-05 | 69852 | 2 |
Expected Output:
| mth | product | ranking | avg_stars |
|---|---|---|---|
| 6 | 69852 | 1 | 4 |
| 6 | 50001 | 2 | 3.5 |
| 7 | 69852 | 1 | 2.5 |
Answer:
The query needed to solve this would take into account the RANK() window function, as well as functions to extract the month from the date and calculate averages:
This query will group all reviews by product and month, calculate the average score for each product per month, and then assign a rank to each product within its respective month based on the average score. The final result is ordered by month and ranking so that the products with the highest average rating per month appear first.
Pro Tip: Window functions are a popular SQL interview topic, so practice all the window function problems on DataLemur
SQL Question 5: Could you explain the differences between an inner and full outer join?
An inner join only includes rows from both tables that satisfy the join condition, whereas a full outer join includes all rows from both tables, regardless of whether they match the join condition or not.
For example, suppose you had a table of Masco orders and Masco customers.
INNER JOIN: When there is a match in the shared key or keys, rows from both tables are retrieved. In this example, an between the Orders and Customers tables would retrieve rows where the in the Orders table matches the in the Customers table.
FULL OUTER JOIN: A 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: Product Revenue Analysis for Masco Corporation
Masco Corporation is a leading manufacturer of home improvement and building products. They deal in a wide array of products, including paints, cabinetry, plumbing parts, etc., which are sold through different retail dealers. Assume, for simplicity, that all products have the same price across all dealers.
Given the following tables:
- Products (product_id, product_name, product_price)
- Dealers (dealer_id, dealer_name, dealer_location)
- Sales (sale_id, product_id, dealer_id, sale_date, quantity)
Write a SQL query to find out the top three products in terms of total revenue generated in the last one year. The output should have product_id, product_name and total revenue.
Example Input:
| product_id | product_name | product_price |
|---|---|---|
| 101 | Behr Premium Paint | $30 |
| 102 | Delta Faucet | $100 |
| 103 | Merillat Cabinet | $200 |
Example Input:
| dealer_id | dealer_name | dealer_location |
|---|---|---|
| 1 | Home Depot | Chicago |
| 2 | Lowe's | New York |
| 3 | Menard's | Los Angeles |
Example Input:
| sale_id | product_id | dealer_id | sale_date | quantity |
|---|---|---|---|---|
| 5001 | 101 | 2 | 07/15/2021 | 15 |
| 5002 | 102 | 3 | 07/20/2021 | 8 |
| 5003 | 103 | 1 | 08/01/2021 | 4 |
| 5004 | 101 | 1 | 08/10/2021 | 20 |
| 5005 | 102 | 2 | 08/15/2021 | 12 |
Answer:
In this query, we join the Products and Sales tables on product_id to get all relevant information in one place. We filter the sales in the last 1 year using the WHERE clause. The GROUP BY clause is used to group all the sales of each product together. The total revenue for each product is calculated as the sum of the product of quantity and product_price for all sales of that product. Finally, the query orders the result in descending order by total_revenue and limits the output to include only the top 3 products.
SQL Question 7: What are the similarities and differences between correleated and non-correlated sub-queries?
A correlated sub-query is one that relies on the outer query and cannot be run independently. It uses the outer query to filter or transform data by referencing a column from it, while the outer query uses the results of the inner query. On the other hand, a non-correlated sub-query is not linked to the outer query and can be run on its own. It does not reference any columns from the outer query and is used to retrieve additional data for the outer query.
An example correlated sub-query:
This correlated subquery retrieves the names and salaries of Masco employees who make more than the average salary for their department. The subquery references the department column in the main query's FROM clause (e1.department) and uses it to filter the rows of the subquery's FROM clause (e2.department).
An example non-correlated sub-query:
This non-correlated subquery retrieves the names and salaries of Masco employees who make more than the average salary for the Data Analytics department (which honestly should be very few people since Data Analysts are awesome).
Anways, the subquery is independent of the main query and can be considered a standalone query. Its output (the average salary for the Data Analytics department) is then used in the main query to filter the rows of the table.
SQL Question 8: Calculate the average salary by department for Masco
Given a company like Masco which contains several departments, it may be useful to know the average salary per department to determine if there are any disparities. This can help in financial planning and budgeting, as well as in making decisions about hiring and salary adjustments.
Write a SQL query to calculate the average salary by department.
Sample table:
| employee_id | first_name | last_name | department_id | salary |
|---|---|---|---|---|
| 1 | John | Doe | 100 | 60000 |
| 2 | Jane | Smith | 200 | 80000 |
| 3 | Mary | Johnson | 300 | 70000 |
| 4 | James | Brown | 100 | 65000 |
| 5 | Linda | Davis | 200 | 90000 |
Sample table:
| department_id | department_name |
|---|---|
| 100 | Sales |
| 200 | Marketing |
| 300 | IT |
Answer:
This SQL query starts by joining the table with the table on the column. This results in a new table that includes both employee salary and department information. Then, it groups this combined table by and calculates the average salary within each group.
To practice a very similar question try this interactive Amazon Average Review Ratings Question which is similar for calculating averages or this Alibaba Compressed Mean Question which is similar for working with company data.
SQL Question 9: Calculate the Click-Through-Rate (CTR) for Masco's products
Given the and tables, find out the click-through rate for each product. The click-through rate (CTR) is calculated as the number of users who added a product to their cart divided by the number of users who clicked on the product, expressed as a percentage.
Example Input:
| click_id | user_id | click_date | product_id |
|---|---|---|---|
| 101 | 67 | 06/08/2022 00:00:00 | 9001 |
| 102 | 89 | 06/10/2022 00:00:00 | 9002 |
| 103 | 67 | 06/18/2022 00:00:00 | 9001 |
| 104 | 93 | 07/26/2022 00:00:00 | 9002 |
| 105 | 88 | 07/05/2022 00:00:00 | 9001 |
Example Input:
| add_to_cart_id | user_id | add_to_cart_date | product_id |
|---|---|---|---|
| 501 | 67 | 06/08/2022 00:00:00 | 9001 |
| 502 | 89 | 06/10/2022 00:00:00 | 9002 |
| 503 | 67 | 06/19/2022 00:00:00 | 9001 |
| 504 | 93 | 07/27/2022 00:00:00 | 9002 |
Answer:
The above PostgreSQL query calculates the click-through rate (CTR) for each product. It does so by joining the and tables on and , then calculates the ratio of unique users who added a product to their carts to the unique users who clicked on the product. This ratio is then multiplied by 100 to convert it to a percentage. The results are grouped by .
To practice a similar SQL interview question on DataLemur's free online SQL coding environment, solve this SQL interview question asked by Facebook:
SQL Question 10: Would a UNION ALL and a FULL OUTER JOIN produce the same result?
No, in almost all cases, and for all practical purposes, and do NOT produce the same result.
While both are similar, in that they combine two tables, you can think of joins as increasing the width of the resulting table (you'll have more columns in the result set for a left/inner/right join), whereas a union is used to combine rows which increases the height of the result set but keeps the column count the same.
Preparing For The Masco SQL Interview
The key to acing a Masco SQL interview is to practice, practice, and then practice some more! Beyond just solving the earlier Masco SQL interview questions, you should also solve the 200+ DataLemur interview questions which come from companies like Amazon, JP Morgan, and consumer good companies like Masco.
Each SQL question has multiple hints, full answers and best of all, there's an online SQL coding environment so you can right online code up your SQL query and have it executed.
To prep for the Masco SQL interview you can also be wise to practice SQL questions from other consumer good companies like:
- Newell Brands SQL questions
- Mohawk Industries SQL questions
- Spectrum Brands SQL questions
- Stanley Black & Decker SQL questions
Dive into the world of AI and machine learning with Macso's expert insights and stay ahead of the curve!
In case your SQL coding skills are weak, forget about going right into solving questions – refresh your SQL knowledge with this DataLemur SQL Tutorial.
This tutorial covers SQL topics like creating summary stats with GROUP BY and WHERE vs. HAVING – both of these show up often during SQL job interviews at Masco.
Masco Data Science Interview Tips
What Do Masco Data Science Interviews Cover?
In addition to SQL query questions, the other question categories to prepare for the Masco Data Science Interview include:
- Probability & Stats Questions
- Python or R Programming Questions
- Product Metrics Interview Questions
- ML Interview Questions
- Behavioral Interview Questions focussed on Masco culture and values
How To Prepare for Masco Data Science Interviews?
To prepare for Masco Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions sourced from FAANG tech companies
- a crash course covering SQL, AB Testing & ML
- over 1000+ 5-star reviews on Amazon
Also focus on the behavioral interview – prepare for it with this guide on acing behavioral interviews.