At Marvell, SQL is used across the company for analyzing semiconductor data patterns for improvements in chip design, and managing large datasets for product testing and quality control. That's why Marvell almost always evaluates jobseekers on SQL coding questions during interviews for Data Science, Data Engineering and Data Analytics jobs.
As such, to help you ace the Marvell SQL interview, here’s 11 Marvell SQL interview questions – can you solve them?
As a data analyst at Marvell, a company that sells various tech products, your manager wants to gain insights into how well each product is performing on a monthly basis. You have been tasked with writing a SQL query that, given a table tracking product sales, computing the average number of units sold for each product monthly.
A window function would be helpful in this task to partition data by product then order by month.
sale_id | product_id | sale_date | units_sold |
---|---|---|---|
1050 | 112 | 01/01/2022 | 10 |
1051 | 113 | 01/04/2022 | 15 |
1052 | 112 | 01/15/2022 | 20 |
1053 | 113 | 02/02/2022 | 15 |
1054 | 112 | 02/14/2022 | 25 |
1055 | 114 | 02/25/2022 | 22 |
1056 | 113 | 03/02/2022 | 18 |
1057 | 112 | 03/03/2022 | 12 |
1058 | 114 | 03/22/2022 | 23 |
month | product_id | avg_units_sold |
---|---|---|
1 | 112 | 15.00 |
1 | 113 | 15.00 |
2 | 112 | 25.00 |
2 | 113 | 15.00 |
2 | 114 | 22.00 |
3 | 112 | 12.00 |
3 | 113 | 18.00 |
3 | 114 | 23.00 |
The query utilizes the window function to calculate the average units sold per product for each month. The function is used to segregate the data based on the month of the sale. The statement ensures the average is computed for each product independently, and the clause ensures the data is ordered by month. The result is a list that details the average units sold for each product monthly.
To practice a similar window function question on DataLemur's free interactive SQL code editor, try this Google SQL Interview Question:
Marvell Technology is a semiconductor company that designs, produces, and sells a wide range of products. To understand the company's product sales and inventory for their diverse product range more closely, provide an SQL solution to:
For this problem, assume we have two tables: one for sales and one for inventory:
sale_id | product_id | sale_date | sale_price |
---|---|---|---|
1 | 101 | 01/01/2022 | 200 |
2 | 102 | 01/02/2022 | 300 |
3 | 101 | 01/05/2022 | 210 |
4 | 103 | 02/01/2022 | 150 |
5 | 102 | 03/15/2022 | 290 |
product_id | month | year | status |
---|---|---|---|
101 | 01 | 2022 | In stock |
102 | 01 | 2022 | Out of stock |
103 | 02 | 2022 | In stock |
102 | 02 | 2022 | In stock |
103 | 03 | 2022 | Out of stock |
The above SQL query groups sales data by product and month. It provides us the average selling price and counts the number of each product sold per month. An inner join is performed on sales and inventory to relate the two tables. The maximum of the status is used to show the stock status for the month, assuming 'Out of stock' is recorded as more significant than 'In stock'.
Database denormalization is when you add redundancy to a database, and break typical normalization rules (codified by the 1st, 2nd, 3rd normal forms).
Denormalization is typically used to improve the performance of a database, particularly when the database is being for OLAP (Online Analytical Processing) purposes. By denormalizing a database, you can reduce the number of joins that are required to retrieve data, which can greatly improve the speed of queries since joins are costly and slow. However, denormalization can also introduce some problems, such as increased data redundancy and the need for more complex update and delete operations since data has been duplicated into multiple tables.
In general, denormalization should be used with caution and only after careful consideration of the trade-offs involved. It's typically better to start with a well-normalized database design then denormalize only if your database isn't scaling as well as you want.
At Marvell, we are keen to monitor the performance of our digital marketing efforts. We track the number of times an advertisement was displayed and the number of times the ad was clicked. The Click-Through-Rate (CTR) is a key metric that we calculate as the number of clicks divided by the number of views. Can you write a PostgreSQL query that calculates the CTR for each of our ads?
Here's some sample data:
ad_id | display_date | views | clicks |
---|---|---|---|
101 | 07/10/2022 00:00:00 | 1000 | 30 |
102 | 07/15/2022 00:00:00 | 850 | 45 |
103 | 07/20/2022 00:00:00 | 900 | 48 |
101 | 07/25/2022 00:00:00 | 940 | 35 |
102 | 07/30/2022 00:00:00 | 1000 | 53 |
ad_id | total_views | total_clicks | CTR |
---|---|---|---|
101 | 1940 | 65 | 3.35% |
102 | 1850 | 98 | 5.19% |
103 | 900 | 48 | 5.33% |
Here is a SQL query to get the desired answer:
This SQL query first uses PostgreSQL's aggregate function to get the total number of views and clicks for each ad (grouped by ). It then calculates the Click-Through-Rate (CTR) by dividing the total number of clicks by total views. The result is then multiplied by 100 to get a percentage and rounded to two decimal places for a cleaner output. Finally, '%' is concatenated to the end of the CTR for better readability.
To practice a similar SQL problem on DataLemur's free interactive coding environment, attempt this Facebook SQL Interview question:
In a database, constraints are rules that the DBMS (database management system) follows when performing operations such as inserting, updating, or deleting data.
For example, consider a table of employee records at Marvell. Some constraints that you might want to implement include:
Marvell Technology, Inc. is a multinational company that produces semiconductors and related technology. It sells a wide range of products across different regions. As the manager of sales, you want to track down which product sold the most in each region last year.
order_id | region | product_id | quantity | order_date |
---|---|---|---|---|
4672 | North America | 1024 | 30 | 2021-06-19 |
7869 | Asia | 1025 | 100 | 2021-03-05 |
8392 | Europe | 1024 | 60 | 2021-09-24 |
6421 | North America | 1026 | 50 | 2021-11-02 |
9154 | Asia | 1025 | 120 | 2021-05-01 |
3629 | Europe | 1024 | 20 | 2021-04-20 |
4389 | North America | 1026 | 35 | 2021-07-13 |
region | best_selling_product | total_quantity |
---|---|---|
North America | 1026 | 85 |
Asia | 1025 | 220 |
Europe | 1024 | 80 |
This query works by first creating a subquery that groups all the sales from 2021 by region and product_id and sums up the quantity sold for each. For each region, it then selects the product with the maximum sum of quantity (the best-selling product).
The constraint is used to specify a condition that the data in a column must meet. If a row is inserted or updated and the data in the column doesn't meet the condition specified by the CHECK constraint, the operation will sadly fail.
For example, you might use a constraint to ensure that a column contains only positive numbers, or that a date is within a certain range.
For example, if you had a table of Marvell employees, here's an example of how to use the CHECK constraint in a CREATE TABLE statement:
Marvell would like to improve its customer communication strategy by identifying customers whose first or last name starts with 'Ma'. They have asked you to write a query that retrieves data for all customers whose first or last name begins with 'Ma'. They are specifically interested in the customer's first name, last name, and email address.
customer_id | first_name | last_name | |
---|---|---|---|
001 | John | Doe | john.doe@gmail.com |
002 | Jane | Smith | jane.smith@yahoo.com |
003 | Margaret | Williamson | margaret.williamson@hotmail.com |
004 | Martin | Jackson | martin.jackson@gmail.com |
005 | Emily | Davis | emily.davis@hotmail.com |
006 | Matilda | Brown | matilda.brown@gmail.com |
first_name | last_name | |
---|---|---|
Margaret | Williamson | margaret.williamson@hotmail.com |
Martin | Jackson | martin.jackson@gmail.com |
Matilda | Brown | matilda.brown@gmail.com |
This SQL statement will scan the table and retrieve the , , and of all records where the or starts with 'Ma'. The '%' character is a wildcard in SQL that matches any sequence of characters, thus 'Ma%' will match any string that starts with 'Ma'.
As a data analyst at Marvell, you are asked to analyze the impact of a recent marketing campaign on customers' purchasing behavior. The campaign targeted different segments of customers and you want to find out the top 5 customer segments with the highest difference in average purchase amounts before and after the campaign.
You have two tables - the table which has data on customerId, segment, and other customer details, and the table which has data on orderID, order amount, customerId and order date.
You are supposed to compare averages for two periods; 1 month before the campaign start date (2021-02-01) and 1 month after the campaign start date.
customerId | segment |
---|---|
1 | A |
2 | B |
3 | C |
4 | A |
5 | B |
orderId | orderAmount | customerId | orderDate |
---|---|---|---|
1 | 100.00 | 1 | 2021-01-15 |
2 | 200.00 | 2 | 2021-02-15 |
3 | 300.00 | 3 | 2021-03-15 |
4 | 400.00 | 4 | 2021-04-15 |
5 | 500.00 | 5 | 2021-05-15 |
The result will display the top 5 customer segments with the biggest difference in average order amount before and after the start of the campaign. This will give you an idea on how the campaign has impacted different customer segments.
Because joins come up frequently during SQL interviews, try this interactive Snapchat Join SQL question:
A foreign key is a field in a database table that links to the primary key of another table, establishing a connection between the two tables.
To demonstrate this concept, let's analyze Marvell's marketing analytics database which stores data from Google Ads campaigns:
:
+------------+------------+------------+------------+ | ad_id | campaign_id| keyword | click_count| +------------+------------+------------+------------+ | 1 | 100 | Marvell pricing | 10 | | 2 | 100 | Marvell reviews | 15 | | 3 | 101 | Marvell alternatives | 7 | | 4 | 101 | buy Marvell | 12 | +------------+------------+------------+------------+
is a foreign key. It references the of the Google Ads campaign that each ad belongs to, establishing a relationship between the ads and their campaigns. This foreign key allows you to easily query the table to find out which ads belong to a specific campaign, or to find out which campaigns a specific ad belongs to.
It is also possible for a table to have multiple foreign keys that reference different primary keys in other tables. For example, the table could have additional foreign keys for the of the ad group that each ad belongs to, and the of the Google Ads account that the campaigns belong to.
Marvell technologies has implemented a new bonus system for their employees. Each employee can get a bonus based on their years of service and their existing salary. The years of service is rounded up to the nearest whole year and is then squared to calculate a multiplier. The bonus is given as a percentage of the existing salary, for which the percentage is calculated as the multiplier modulus 10. Write a SQL query that calculates the new employee salary including the bonus.
id | name | join_date | salary |
---|---|---|---|
1 | John | 01/02/2000 | 50000 |
2 | Sara | 19/03/2005 | 60000 |
3 | Jack | 20/05/2020 | 45000 |
4 | Sam | 30/12/2015 | 70000 |
5 | Rita | 15/07/2003 | 85000 |
id | name | old_salary | bonus | new_salary |
---|---|---|---|---|
1 | John | 50000 | 20 | 60000 |
2 | Sara | 60000 | 5 | 63000 |
3 | Jack | 45000 | 0 | 45000 |
4 | Sam | 70000 | 2 | 71400 |
5 | Rita | 85000 | 25 | 106250 |
In the above query, we used to get the number of years since the employee joined and to round this up to the nearest year. We then used combined with to square this value and to get the modulus 10 of this value to calculate the bonus percent. The bonus in absolute value is then added to the old salary to get the new salary.
To practice a very similar question try this interactive JPMorgan Chase Cards Issued Difference Question which is similar for numerical calculations based on existing data or this CVS Health Pharmacy Analytics (Part 1) Question which is similar for calculation based on rankings.
The best way to prepare for a Marvell SQL interview is to practice, practice, practice. In addition to solving the earlier Marvell SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like Google, Facebook, Microsoft and Amazon.
Each SQL question has hints to guide you, detailed solutions and most importantly, there's an interactive SQL code editor so you can instantly run your query and have it graded.
To prep for the Marvell SQL interview you can also be a great idea to solve SQL questions from other tech companies like:
However, if your SQL query skills are weak, forget about jumping right into solving questions – improve your SQL foundations with this free SQL for Data Analytics course.
This tutorial covers things like filtering with LIKE and handling strings – both of which show up routinely in SQL interviews at Marvell.
Beyond writing SQL queries, the other question categories covered in the Marvell Data Science Interview are:
To prepare for Marvell Data Science interviews read the book Ace the Data Science Interview because it's got: