At Advantest, SQL is used all the damn time for analyzing semiconductor testing data and optimizing equipment performance metrics. Unsurprisingly this is why Advantest almost always evaluates jobseekers on SQL coding questions during interviews for Data Science, Analytics, and & Data Engineering jobs.
As such, to help you ace the Advantest SQL interview, this blog covers 8 Advantest SQL interview questions – how many can you solve?
Advantest is a leading developer of automated test equipment (ATE) for the semiconductor industry. Given the high value of purchases, their key customers are companies who purchase their equipment frequently and in large volumes. They classify a VIP customer as a customer who has placed 5 or more orders within a month and where the total value of orders exceed $1 million.
Given the customer order table and the order details table below, write a SQL query to identify the VIP customers for the month of July 2022.
order_id | customer_id | order_date |
---|---|---|
1 | 123 | 2022-07-01 00:00:00 |
2 | 265 | 2022-07-03 00:00:00 |
3 | 123 | 2022-07-05 00:00:00 |
4 | 978 | 2022-07-07 00:00:00 |
5 | 123 | 2022-07-09 00:00:00 |
order_id | product_id | unit_price | quantity |
---|---|---|---|
1 | 1023 | 250000 | 5 |
2 | 2658 | 150000 | 6 |
3 | 2093 | 300000 | 4 |
4 | 7809 | 200000 | 3 |
5 | 1023 | 250000 | 2 |
In this query, we are joining the table and table on the field. Then we filter by the order_date to select only the orders placed in July 2022. The result is grouped by . The HAVING clause is used to filter the result for customers who have placed 5 or more orders and where the total_order_value exceeds $1 million, thereby identifying the VIP customers.
As part of the Data Analytics team at Advantest, you are given access to the records of monthly test scores obtained by various Advantest testing machines in use within company operations.
Advantest is interested in understanding the historical performance of these machines on a monthly basis.
They would like to know how the average test score computed from all samples, for each machine, changes over the months.
More specifically, your task is to write a SQL query that provides a monthly average of test scores for each machine.
test_id | machine_id | test_date | score |
---|---|---|---|
8215 | 882 | 2020-01-20 | 95 |
6529 | 445 | 2020-02-16 | 88 |
9270 | 882 | 2020-02-17 | 90 |
7386 | 997 | 2020-03-08 | 92 |
5197 | 997 | 2020-03-29 | 91 |
4052 | 445 | 2020-01-25 | 84 |
month | machine | avg_score |
---|---|---|
1 | 882 | 95.00 |
2 | 882 | 90.00 |
2 | 445 | 88.00 |
1 | 445 | 84.00 |
3 | 997 | 91.50 |
In this query, we are partitioning our dataset into groups of machine ID and the month of the test date. For each of these groups, we apply the AVG() window function to compute the average score.
For more window function practice, solve this Uber SQL problem on DataLemur's online SQL code editor:
The and operators are both used to filter data based on certain criteria, but they work in different ways. is used to select values within a range, while is used to select values that match a list.
For instance, if you have a table called that contains the salary of each employee, along with which country they reside in, you could use the operator to find all employees who make between 160k:
To find all employees that reside in France and Germany, you could use the operator:
Advantest is a leading company in automatic test equipment for integrated circuits. They want to optimize their supply chain by designing an inventory database. There are two things they deal with majorly: Components and Finished Products.
Each of the Components and Finished Products has a unique ID, name, and quantity in inventory. For Finished Products, they're assembled from various Components, each has a corresponding quantity used.
Please help Advantest design the Inventory and Supply Chain database.
component_id | component_name | quantity |
---|---|---|
101 | Processor | 500 |
102 | RAM | 300 |
103 | Storage Drive | 350 |
104 | Mother Board | 400 |
105 | Power Supply Unit | 500 |
product_id | product_name | quantity |
---|---|---|
201 | Gaming PC | 95 |
202 | Office PC | 120 |
203 | Home PC | 100 |
product_id | component_id | component_needed |
---|---|---|
201 | 101 | 1 |
201 | 102 | 2 |
201 | 103 | 1 |
201 | 104 | 1 |
201 | 105 | 1 |
202 | 101 | 1 |
202 | 102 | 1 |
202 | 103 | 1 |
202 | 104 | 1 |
202 | 105 | 1 |
203 | 101 | 1 |
203 | 102 | 1 |
203 | 103 | 1 |
203 | 104 | 1 |
Given this problem, we are required to create a database schema that allows us to track the inventory of both components and products, also consider the relationship between components and products.
We suggest the following SQL command for creating these tables:
This design would allow the company to easily manage its inventory and product assembly requirements.
As for a sample question related, we might want to query to get the number of each product that we can assemble with the current inventory of components.
Here is a query to solve that:
This query calculates how many products can be assembled by dividing the quantity of each component by the number needed for the product, then it selects the minimum number which indicates the limiting component for each product.
{#Question-5}
is used to combine the output of multiple statements into one big result!
For a concrete example, say you were a Data Analyst supporting the Sales Analytics team at Advantest, and data on potential sales leads lived in both Salesforce CRM and Hubspot. To write a query to analyze leads created after 2023 started, across both CRMs, you could use in the following way:
"
Working at Advantest, you are required to filter out customer's records based on certain conditions. You need to find out all the customers who bought Advantest's device 'V93000' after '01/01/2019' and are located in 'Japan'. Furthermore, they must have purchased more than 50 units of the device.
customer_id | customer_name | purchase_date | device_name | units_bought | location |
---|---|---|---|---|---|
1 | Company A | 01/02/2019 | V93000 | 30 | Japan |
2 | Company B | 12/12/2019 | V93000 | 65 | Japan |
3 | Company C | 12/11/2018 | V93000 | 55 | USA |
4 | Company D | 05/09/2020 | V95000 | 70 | Japan |
5 | Company E | 24/08/2019 | V93000 | 75 | Japan |
This query first filters out all records where device_name is 'V93000'. It then filters out records where purchase_date is after '01/01/2019', followed by checking if the number of units_bought is greater than 50. Lastly, it filters out all records where the location is 'Japan'. Therefore, the result is a list containing all customers who bought more than 50 units of Advantest's device 'V93000' after '01/01/2019' in Japan. The final result will include customers 'Company B' and 'Company E'.
A UNIQUE constraint ensures that all values in a column are different. It is often used in conjunction with other constraints, such as NOT NULL, to ensure that the data meets certain conditions.
For example, if you had Advantest employee data stored in a database, here's some constraints you'd use:
In the Advantest employee example, the UNIQUE constraint is applied to the "email" field to ensure that each employee has a unique email address. This helps to ensure the integrity of the data in the database and prevents errors that could occur if two employees had the same email address.
As an analyst at Advantest, you are asked to evaluate the company's sales performance on each product. You need to calculate the total sales revenue, the average sales revenue, and the standard deviation of the sales revenue for each product. The standard deviation is calculated by using the SQRT() function. The total sales revenue equals the product of the price and the quantity of the product sold.
Please note: The price is provided in cents, you should convert it to dollars using the SQL arithmetic operator /. Also, the quantity data might have some errors, and there might be negative values. Use the ABS() function to obtain the absolute quantity sold. The price and the absolute quantity should be well-appropriated and then rounded to two decimal places.
Please Write a SQL query to perform the above calculations on the provided and tables.
sales_id | product_id | quantity | sales_date |
---|---|---|---|
001 | 1 | 35 | 2022-08-01 |
002 | 2 | -9 | 2022-08-02 |
003 | 2 | 14 | 2022-09-02 |
004 | 3 | 20 | 2022-09-02 |
005 | 1 | -8 | 2022-09-02 |
product_id | product_name | price_cents |
---|---|---|
1 | Keyboard | 2999 |
2 | Mouse | 1999 |
3 | Monitor | 9999 |
This SQL query first joins the and table on , and for each product in the table, it then calculates total revenue, average revenue and standard deviation of the revenue. The total revenue is the sum of product price times the absolute quantity. The average revenue is the average of product price times absolute quantity. The standard deviation of the revenue is calculated based on the formula of standard deviation of revenues. Note that the ABS() function is used to get the absolute value of the quantity as there might be some data error. Also, the arithmetic operator / is used to convert price from cents to dollars. The resulting revenues are rounded to two decimal places using the ROUND() function.
The 2 most similar questions to the one provided are "Y-on-Y Growth Rate" and "Highest-Grossing Items".
To practice a very similar question try this interactive Wayfair Y-on-Y Growth Rate Question which is similar for requiring to calculate sales metrics or this Amazon Highest-Grossing Items Question which is similar for requiring sales revenue calculations per product.
The best way to prepare for a Advantest SQL interview is to practice, practice, practice. Beyond just solving the earlier Advantest SQL interview questions, you should also solve the 200+ SQL Interview Questions on DataLemur which come from companies like FAANG and tech startups.
Each interview question has multiple hints, detailed solutions and crucially, there is an interactive coding environment so you can instantly run your SQL query and have it checked.
To prep for the Advantest SQL interview you can also be useful to practice SQL questions from other tech companies like:
However, if your SQL coding skills are weak, forget about going right into solving questions – strengthen your SQL foundations with this interactive SQL tutorial.
This tutorial covers topics including removing NULLs and math functions like ROUND()/CEIL() – both of these show up routinely in SQL interviews at Advantest.
In addition to SQL interview questions, the other types of questions covered in the Advantest Data Science Interview are:
The best way to prepare for Advantest Data Science interviews is by reading Ace the Data Science Interview. The book's got: