11 Onto Innovation SQL Interview Questions (Updated 2024)

At Onto Innovation, SQL is used day-to-day for analyzing massive datasets for semiconductor process optimization. That's the reason behind why Onto Innovation often tests SQL problems in interviews for Data Science, Data Engineering and Data Analytics jobs.

As such, to help you study for the Onto Innovation SQL interview, we've curated 11 Onto Innovation SQL interview questions – able to solve them?

11 Onto Innovation SQL Interview Questions

SQL Question 1: Identify the Top Spending Customers

Onto Innovation runs a lot of technological researches and survey activities for its customers. Each customer has a unique customer_id and records of their transaction amounts in the company’s database.

We have two tables - , which lists the details of the customers, and , which lists the transactions conducted by those customers. Each transaction has a unique transaction_id, the customer_id of the customer who did the transaction, the date of transaction, and the amount spent by the customer.

The goal is to write a SQL query to find out the top 10 customers who spent the most money in the year 2022. We would like to obtain their customer_id, name, and total amount spent.

Sample data:

Example Input:

Example Input:

Answer:

This query joins the two tables on customer_id, and then groups by customer_id and customer name. It then sums the amount spent by each customer within the year 2022, sorts the customers in descending order of their total amount spent, and finally selects the top 10 spenders.

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

SQL Question 2: Get Monthly Average Quality Score of Products

Onto Innovation is a leader in process control, combining global scale with an expanded portfolio of leading-edge technologies that include: lithography/wafer inspection, surface, composition analysis. They track quality scores of their products in their database.

Each product can have multiple quality scores recorded per month. The quality scores are reviews provided by their quality control engineers and are recorded in a table .

Write a SQL query to analyze the dataset and find the monthly average quality score (round off to 2 decimal places) for each product for the year 2023.

Example Input:

Example Output:

Answer:

This query uses the function to get the month and year from review_date for further grouping. The function is used to calculate the average quality score rounded to 2 decimal places using the function for each product per month in the specified year. The condition for the specified year is set in the clause. The clause groups the data by month, year, and product, and the clause orders the result set by year, month, and product.

For more window function practice, try this Uber SQL problem on DataLemur's online SQL code editor:

SQL Question 3: What's the difference between an inner and a 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 a concrete example, imagine you had two database tables, an advertising campaigns table which had information on Google Ads keywords and how much was bid for each keyword, and a Onto Innovation sales table, which has data on how many products were sold and which Google Ads keyword drove that sale.

: retrieves rows from both tables where there is a match in the shared key or keys. For example, an INNER JOIN between the table and the table could be performed using the keyword column as the shared key. This would retrieve only the rows where the keyword in the table matches the keyword in the table.

: retrieves all rows from both tables, regardless of whether there is a match in the shared key or keys. If there is no match, values will be returned for the columns of the non-matching table.

SQL Question 4: Filter Customer Orders with Specific Criteria

Onto Innovation, a leading provider of process control and process enabling solutions, wants to analyze its customers' order data. Your task as the data analyst is to write a SQL command that filters customers who placed orders in 2022 worth more than $5000 and either originate from the United States (US) or have ordered Semiconductor products.

You have been provided with two tables: and . Structure of and tables is as follows.

Example Input:

Example Input:

Answer:

This query joins the and tables on the field. It then filters for customer orders that were placed in 2022 (on or after '2022-01-01'), are worth more than $5000, and are either from customers in the US or involve 'Semiconductor' products as specified in the question.

SQL Question 5: What does adding 'DISTINCT' to a SQL query do?

The keyword removes duplicates from a query.

Suppose you had a table of Onto Innovation customers, and wanted to figure out which cities the customers lived in, but didn't want duplicate results.

table:

You could write a query like this to filter out the repeated cities:

Your result would be:

SQL Question 6: Average Duration of Machine Usage

In your role at Onto Innovation, a leading provider of process control, metrology, and inspection systems used across the semiconductor and industrial markets, you are tasked with optimizing usage of the machines.

To help achieve this, your first task is to determine the average duration of machinery usage per product process in order to identify possible efficiencies and streamline operations.

The data is structured in a PostgreSQL database similar to the table below:

Example Input:

Answer:

Using PostgreSQL, a possible solution would be:

This query first calculates the duration of each machine's usage in hours by subtracting the from the and converting it to seconds using , then to hours by dividing by 3600. It then calculates the average duration per with the function.

In the end, you get an overview of the average duration of machinery usage for each process, which can be used to identify efficiencies and streamline operations.

To practice a very similar question try this interactive Amazon Server Utilization Time Question which is similar for calculating total machine usage duration or this Tesla Unfinished Parts Question which is similar for analyzing machine usage for production processes.

SQL Question 7: In SQL, are values same the same as zero or a blank space?

A value represents a missing or unknown value. It is different from a zero or a blank space, which are actual values.

It's important to handle values properly in SQL because they can cause unexpected results if not treated correctly. For example, comparisons involving values always result in . Also values are not included in calculations. For example, will ignore values in the column.

SQL Question 8: Obtain Average Sale Price per Year for Each Product

Onto Innovation is a technology company that specializes in providing process control, combining global scale with an expanded portfolio of advanced technologies that include: 3D metrology spanning the chip from nanometer-scale transistors to micron-level die-interconnects; macro defect inspection of wafers and packages; metal interconnect composition; factory analytics; and lithography for advanced semiconductor packaging.

Given the database of their sales records, we want to find out the average sale price per year for each product.

Here is the table with some sample data:

Example Input:

We want to find the average sale price per year for each product.

Example Output:

Answer:

The following PostgreSQL query will solve the problem:

The query extracts the year part from the and groups the rows based on the year and the . It then applies the aggregate function on to compute the average sale price per year for each product. The part is used to round the average prices to 2 decimal places. The clause is then used to sort the results first by year, and then by within each year.

SQL Question 9: Filter Customer Records from Onto Innovation Database

As a data analyst for Onto Innovation, you are asked to filter specific customer records from the company database. The task is to identify all customers whose email domain is '@onto-innovation.com'. You are tasked with this so the company can send out targeted emails to its internal employees. Assume you have a table that has their information.

This is how a sample data looks like:

Example Input:

Write a SQL query in PostgreSQL to fetch customer details who have an email with domain '@onto-innovation.com'.

Answer:

This SQL query uses the keyword to search for all records in the database where the email field matches the pattern '@onto-innovation.com'. The '%' character is a wildcard character that matches any sequence of characters. In this case, it matches any email that ends with '@onto-innovation.com'.

SQL Question 10: Can you explain what SQL constraints are, and why they are useful?

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 Onto Innovation'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 11: Calculate the Power and Modulus of Sensor Readings

Onto Innovation designs and produces sensor systems for industrial applications. Each sensor delivers a power signal, and a temperature reading each time it is inspected.

The company wants to develop a more complex analysis, which calculates the power of each sensor's temperature reading and the modulus of each sensor's power signal and temperature reading (if the power signal is divided by the temperature). This will help them understand whether some sensors are overheating or overperforming.

Your task is to write an SQL query to generate the requested analysis.

table Example Input:

Example Output:

Answer:

This query uses the POWER() function to calculate the power of the temperature reading (the temperature to the power of 2). The MOD() function is used to find the modulus of the division of the power signal by the temperature. If the power signal is not a multiple of the temperature, the MOD() function will return the remainder of the division.

Preparing For The Onto Innovation SQL Interview

The key to acing a Onto Innovation SQL interview is to practice, practice, and then practice some more! Besides solving the earlier Onto Innovation SQL interview questions, you should also solve the 200+ SQL questions on DataLemur which come from companies like Microsoft, Google, Amazon, and tech startups.

Each exercise has multiple hints, detailed solutions and crucially, there is an interactive SQL code editor so you can right online code up your SQL query answer and have it checked.

To prep for the Onto Innovation SQL interview it is also useful to solve interview questions from other tech companies like:

• Applied Materials SQL questions

• KLA SQL questions

• LAM Research SQL questions

• BE Semiconductor SQL questions

However, if your SQL skills are weak, don't worry about going right into solving questions – improve your SQL foundations with this SQL tutorial for Data Analytics.

This tutorial covers SQL topics like filtering on multiple conditions using AND/OR/NOT and joining a table to itself – both of which pop up routinely during SQL job interviews at Onto Innovation.

Onto Innovation Data Science Interview Tips

What Do Onto Innovation Data Science Interviews Cover?

Besides SQL interview questions, the other types of problems to practice for the Onto Innovation Data Science Interview are:

• Statistics & Probability Questions
• Coding Questions in Python or R
• Product-Sense Questions
• Machine Learning Questions
• Behavioral Based Interview Questions

How To Prepare for Onto Innovation Data Science Interviews?

The best way to prepare for Onto Innovation Data Science interviews is by reading Ace the Data Science Interview. The book's got:

• 201 Interview Questions from tech companies like Google & Microsoft
• A Crash Course on SQL, Product-Sense & ML
• Amazing Reviews (900+ reviews, 4.5-star rating)