9 ON Semiconductor SQL Interview Questions (Updated 2025)

At ON Semiconductor, SQL is used quite frequently for analyzing semiconductor production data for efficiency improvements and managing databases of electronic component design details. Unsurprisingly this is why ON Semiconductor asks SQL coding questions in interviews for Data Science and Data Engineering positions.

So, to help you practice for the ON Semiconductor SQL interview, we've curated 9 ON Semiconductor SQL interview questions – how many can you solve?

9 ONSemi SQL Interview Questions

SQL Question 1: Calculating Monthly Average Unit Sales

As a data analyst for ON Semiconductor, your manager wants you to calculate the average monthly unit sales volume for each product. The exercise requires you to use the window functions in SQL.

Assume you have a sales table named sales, which has the following structure:

sales Example Input:

Example Output:

Answer:

SELECT 
    EXTRACT(MONTH FROM sale_date) AS mth,
    product_id,
    AVG(units_sold) OVER (PARTITION BY EXTRACT(MONTH FROM sale_date),product_id) AS avg_units_sold
FROM 
    sales
ORDER BY
    mth, product_id;

The above SQL script calculates the average units sold by product id for each month. It first extracts the month from the date, then splits the data into partitions based on the month and product id using the PARTITION BY clause. Then, for each partition, it applies the AVG function to calculate the average units sold. The ORDER BY clause then sorts the final result set by month and product id.

p.s. Window functions show up super often during SQL interviews, so practice the 27+ window function questions on DataLemur

SQL Question 2: Sales Analysis for ON Semiconductor Products

ON Semiconductor is a company operating in the semiconductor industry. They manufacture, among other things, different types of semiconductor components. They need your help to better understand their sales history.

For a given list of products and corresponding sales data, your task is to calculate the total amount of sales per product type per quarter of the year. Consider that the company has three main types of products: transistors, diodes and integrated circuits.

Here's an example of how the company stores their sales data in the sales and products tables:

products Example Input:

sales Example Input:

Answer:

To find the total sales per product type per quarter of the year, we can join the sales table and the products table on product_id, and use the extract function in PostgreSQL to get the quarter of the year from the sales_date. Here's a possible SQL query for this:

SELECT p.product_type,
       EXTRACT(QUARTER FROM s.sales_date) AS quarter,
       SUM(s.quantity) AS total_sales
FROM sales s
JOIN products p ON s.product_id = p.product_id
GROUP BY p.product_type, quarter
ORDER BY quarter, total_sales desc;

This query first joins the sales and products tables on the product_id column. Then it groups the resulting table by product_type and quarter. For each group, it calculates the total sales by summing up quantities from the sales table. The final result is ordered by quarter, and for each quarter, the products are sorted in descending order by sales.

SQL Question 3: How do foreign and primary keys differ?

To explain the difference between a primary key and foreign key, let's start with some data from ON Semiconductor's marketing analytics database which stores data from Google Ads campaigns:

on_semiconductor_ads_data:

+------------+------------+------------+------------+ | ad_id | campaign_id| keyword | click_count| +------------+------------+------------+------------+ | 1 | 100 | ON Semiconductor pricing | 10 | | 2 | 100 | ON Semiconductor reviews | 15 | | 3 | 101 | ON Semiconductor alternatives | 7 | | 4 | 101 | buy ON Semiconductor | 12 | +------------+------------+------------+------------+

In this table, ad_id could be a primary key. It is unique for each row in the table and cannot contain null values.

campaign_id could be a foreign key. It references the campaign_id 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 on_semiconductor_ads_data table could have additional foreign keys for the ad_group_id of the ad group that each ad belongs to, and the account_id of the Google Ads account that the campaigns belong to.

SQL Question 4: Find the Average Scrapping Quantity of Each Production Line

You're given a table called scrap_log, which shows the amount of materials scrapped on each production line in ON Semiconductor each week. Each production line uses different semiconductor materials, and machine calibration and manufacturing processes can result in some material wastage that is tracked.

Find the average amount of materials scrapped in each production line per week.

The scrap_log table is structured as follows:

scrap_log Example Input:

|**log_id**|**date**|**prod_line**|**scrap_amount**|
|:----|:----|:----|:----|
|1|2022-01-01|A|300|
|2|2022-01-01|B|200|
|3|2022-01-08|A|250|
|4|2022-01-08|B|210|
|5|2022-01-15|A|320|
|6|2022-01-15|B|220|

Assuming that this table contains data for more than one week, write a PostgreSQL query that would return the average scrapped quantity per week for every production line. The output should be sorted ascendingly by production lines.

Expected Output:

|**prod_line**|**avg_scrap**|
|:----|:----|
|A|290|
|B|210|

Answer:

Here is a PostgreSQL query that would solve the problem:

SELECT 
   prod_line, 
   AVG(scrap_amount) as avg_scrap
FROM 
   scrap_log
GROUP BY 
   prod_line
ORDER BY 
   prod_line ASC;

This code groups the scrap_log table by the prod_line then averages the scrap_amount for each prod_line group. The ORDER BY clause ensures the result is displayed in ascending order by prod_line.

To practice a very similar question try this interactive Google Odd and Even Measurements Question which is similar for calculating sums based on category or this Amazon Average Review Ratings Question which is similar for calculating average values based on group.

SQL Question 5: What does the FOREIGN KEY constraint do?

A FOREIGN KEY is a field in a table that references the PRIMARY KEY of another table. It creates a link between the two tables and ensures that the data in the FOREIGN KEY field is valid.

Say for example you had sales analytics data from ON Semiconductor's CRM (customer-relationship management) tool.

CREATE TABLE on_semiconductor_accounts (
    account_id INTEGER PRIMARY KEY,
    account_name VARCHAR(255) NOT NULL,
    industry VARCHAR(255) NOT NULL
);

CREATE TABLE opportunities (
    opportunity_id INTEGER PRIMARY KEY,
    opportunity_name VARCHAR(255) NOT NULL,
    account_id INTEGER NOT NULL,
    FOREIGN KEY (account_id) REFERENCES on_semiconductor_accounts(account_id)
);

In this example, the opportunities table has a foreign key field called account_id that references the "account_id" field in the on_semiconductor_accounts table (the primary key). This helps to link the data about each opportunity to the corresponding account information in the accounts table.

This makes sure the insertion of rows in the opportunities table that do not have corresponding entries in the on_semiconductor_accounts table. It also helps to enforce the relationship between the two tables and ensures that data is not deleted from the on_semiconductor_accounts table if there are still references to it in the opportunities table.

SQL Question 6: Calculate the click-through-rate (CTR) for ON Semiconductor products

ON Semiconductor is a company that sells semiconductors for a variety of products. Let's say ON Semiconductor runs ad campaigns on different platforms for their new range of products and wants to analyze the performance in terms of CTR (Click-Through-Rate).

Click-through-rate is the ratio of the number of users who click on an ad to the number of total users who view the ad. In this case, you are asked to calculate monthly CTR for different ad campaigns showing different ON Semiconductor products.

We are to use two tables as follows:

1. A table ad_views to record each time an user views an ad.

2. A table ad_clicks to record each time an user clicks an ad.

Here's a glimpse of the kind of data stored in the ad_views and ad_clicks tables:

ad_views Example Input:

ad_clicks Example Input:

Write a SQL query to calculate the monthly CTR for each campaign.

Answer:

Here is one way you can calculate the monthly CTR for each campaign:

WITH monthly_views AS (
   SELECT DATE_TRUNC('month', view_date) as month, campaign_id, COUNT(*) as num_views
   FROM ad_views
   GROUP BY DATE_TRUNC('month', view_date), campaign_id
),
monthly_clicks AS (
   SELECT DATE_TRUNC('month', click_date) as month, campaign_id, COUNT(*) as num_clicks
   FROM ad_clicks
   GROUP BY DATE_TRUNC('month', click_date), campaign_id
)
SELECT monthly_views.month, 
       monthly_views.campaign_id, 
       COALESCE(monthly_clicks.num_clicks,0) as num_clicks, 
       monthly_views.num_views, 
       (COALESCE(monthly_clicks.num_clicks, 0)::float / monthly_views.num_views) * 100 as ctr
FROM monthly_views 
LEFT JOIN monthly_clicks 
ON monthly_views.campaign_id = monthly_clicks.campaign_id 
AND monthly_views.month = monthly_clicks.month;

The output will be a table that shows, for each month and each ad campaign, the total number of views, total number of clicks, and the calculated CTR (in percentages).

The above query first calculates the monthly number of views and clicks for each campaign in the monthly_views and monthly_clicks temporary tables, respectively. It then LEFT JOINs these two tables on the campaign_id and month fields, and calculates the CTR by diving the number of clicks by the number of views for each row. Note that we use a COALESCE function to replace any NULL value of clicks (where there were views but no clicks) with 0 for the CTR calculation.

To solve another question about calculating rates, try this TikTok SQL Interview Question on DataLemur's interactive coding environment:

SQL Question 7: What's a constraint in SQL, and do you have any examples?

Constraints are used to specify the rules concerning data in the table. It can be applied for single or multiple fields in an SQL table during the creation of the table or after creating using the ALTER TABLE command. The constraints are:

Say you were storing sales analytyics data from ON Semiconductor's CRM inside a database. Here's some example constraints you could use:

• PRIMARY KEY constraint: You might use a PRIMARY KEY constraint to ensure that each record in the database has a unique identifier. For example, you could use the "opportunity_id" field as the primary key in the "opportunities" table.

• FOREIGN KEY constraint: You might use a FOREIGN KEY constraint to link the data in one table to the data in another table. For example, you could use a foreign key field in the "opportunities" table to reference the "account_id" field in the "accounts" table.

• NOT NULL constraint: You might use a NOT NULL constraint to ensure that a field cannot contain a NULL value. For example, you could use a NOT NULL constraint on the "opportunity_name" field in the "opportunities" table to ensure that each opportunity has a name.

• UNIQUE constraint: You might use a UNIQUE constraint to ensure that the data in a field is unique across the entire table. For example, you could use a UNIQUE constraint on the "email" field in the "contacts" table to ensure that each contact has a unique email address.

• CHECK constraint: You might use a CHECK constraint to ensure that the data in a field meets certain conditions. For example, you could use a CHECK constraint to ensure that the "deal_probability" field in the "opportunities" table is a value between 0 and 100.

• DEFAULT constraint: You might use a DEFAULT constraint to specify a default value for a field. For example, you could use a DEFAULT constraint on the "stage" field in the "opportunities" table to set the default value to "prospecting"

SQL Question 8: Find the Maximum Sales per Product Type for Each Quarter

As a data analyst at ON Semiconductor, you are asked to analyze the sales data from the past year. Your task is to create a query that will provide the maximum sales of each product type for each quarter.

product_sales Example Input:

Example Output:

Answer:

To solve this, you could take the following steps in SQL:

1. Extract the year and the quarter from the sales date.
2. Group by the extracted year, quarter, and product type.
3. Use the MAX aggregate function to find the maximum sales for each grouping.

Here is the PostgreSQL query:

SELECT
  EXTRACT(YEAR FROM product_sale_date) as year,
  'Q' || CEIL(EXTRACT(MONTH FROM product_sale_date)/3)::TEXT as quarter,
  product_type,
  MAX(sale_amount) as max_sale_amount
FROM
  product_sales
GROUP BY
  year,
  quarter,
  product_type
ORDER BY
  year ASC,
  quarter ASC;

This query starts by grouping the sales data by the year and quarter of the sale date and the product type. It then extracts the maximum sale amount for each group. The resulting output shows the maximum sale amount for each product type in each quarter of each year. The data is then sorted in ascending order by year and quarter.

SQL Question 9: Filter ON Semiconductor Customer Records

As a Database Analyst at ON Semiconductor, you are tasked with extracting customer records for a new marketing campaign. The company needs records for customers who have a particular interest. To narrow it down, the company wants all customers whose interests contain the word "Semiconductor". Create a SQL query using the SQL keyword LIKE to filter these records from the customer database.

Below is the sample data from customers table:

customers Example Input:

Answer:

You can solve this filtering problem using the following SQL query:

SELECT *
FROM customers
WHERE interests LIKE '%Semiconductor%';

This query will filter and display all data from the customers table where the interests attribute contains the word 'Semiconductor'. The '%' is a wildcard character that can match any sequence of characters.

Please note that PostgreSQL is case-sensitive, so make sure the spelling and the case of the string match with the records in the database.

The output from the query will be:

Preparing For The ON Semiconductor SQL Interview

Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the ON Semiconductor SQL interview is to solve as many practice SQL interview questions as you can! In addition to solving the earlier ON Semiconductor SQL interview questions, you should also solve the 200+ tricky sql questions on DataLemur which come from companies like Facebook, Google, and VC-backed startups.

Each interview question has multiple hints, full answers and most importantly, there's an interactive coding environment so you can easily right in the browser your SQL query and have it executed.

To prep for the ON Semiconductor SQL interview you can also be wise to practice SQL questions from other semiconductor companies like:

• Infineon SQL questions

• STMicroelectronics SQL questions

• Renesas Electronics SQL questions

• NXP Semiconductors SQL questions

However, if your SQL coding skills are weak, forget about jumping right into solving questions – go learn SQL with this interactive SQL tutorial.

This tutorial covers topics including GROUP BY and window functions – both of which show up routinely in SQL job interviews at ON Semiconductor.

ON Semiconductor Data Science Interview Tips

What Do ON Semiconductor Data Science Interviews Cover?

Beyond writing SQL queries, the other topics tested in the ON Semiconductor Data Science Interview are:

• Statistics and Probability Questions
• Python or R Coding Questions
• Product Analytics Questions
• Machine Learning Questions
• Behavioral & Resume-Based Questions

How To Prepare for ON Semiconductor Data Science Interviews?

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

• 201 interview questions taken from Facebook, Google & startups
• a refresher on SQL, Product-Sense & ML
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