At Axcelis Technologies, SQL is used across the company for analyzing manufacturing data for efficiency improvements and pulling customer insights for targeted marketing strategies. That's why Axcelis Technologies often tests SQL coding questions during interviews for Data Science and Data Engineering positions.
To help you prepare for the Axcelis Technologies SQL interview, we'll cover 10 Axcelis Technologies SQL interview questions – can you answer each one?
Sure, here's an example of how you could structure this type of question:
Given a database of Axcelis Technologies's orders, a leading semiconductor manufacturing company, write a SQL query to yield the account_ids of the "whale" users. Define "whale" users as those who regularly (at least twice a month) purchase high-value equipment (anything over $10,000).
Consider two tables, and :
Example Input:
account_id | full_name | registered_date | |
---|---|---|---|
9874 | John Doe | john.doe@axcelistech.com | 2018-05-10 |
8673 | Jane Smith | jane.smith@axcelistech.com | 2019-07-25 |
5632 | Michael Wong | michael.wong@axcelistech.com | 2017-01-30 |
2345 | Sarah Connor | sarah.connor@axcelistech.com | 2020-08-14 |
Example Input:
order_id | account_id | product_name | price | purchase_date |
---|---|---|---|---|
1234 | 9874 | Semiconductor Machine Z201 | $15,000 | 2022-07-07 |
1536 | 5632 | Equipment A350 | $8,500 | 2022-07-13 |
1756 | 9874 | Semiconductor Machine X202 | $11,500 | 2022-07-20 |
2345 | 2345 | Chip Fabricator Y100 | $12,000 | 2022-07-25 |
2536 | 8673 | Equipment B250 | $9,500 | 2022-07-26 |
2736 | 2345 | Chip Fabricator Y101 | $13,500 | 2022-07-30 |
You may use a query similar to the following one:
This query uses PostgreSQL's EXTRACT function to get the month from each purchase_date. It then groups by account_id and this extracted month, filtering by the stipulation that there must be at least 2 entries in each group (indicating that the user has purchased high-value equipment more than once in a given month). The result is then joined with the accounts table to get the full account details of these "whale" users.
To practice a related super-user data analysis question on DataLemur's free interactive coding environment, try this Microsoft Teams Power User SQL Interview Question:
Axcelis Technologies, a semiconductor manufacturing company, wants to analyze its production process times. They're mainly interested in calculating the average production time per machine, both overall and monthly, and they would like to know how many days it took for each product to move from the first to the last process stage in the production sequence.
The dataset provided by the company contains three tables:
machine_id | machine_name |
---|---|
111 | Machine A |
222 | Machine B |
333 | Machine C |
product_id | product_name |
---|---|
10 | Product X |
20 | Product Y |
30 | Product Z |
production_id | machine_id | product_id | stage | stage_completion_date |
---|---|---|---|---|
1 | 111 | 10 | 1 | 2022-01-01 |
2 | 111 | 10 | 2 | 2022-01-05 |
3 | 111 | 10 | 3 | 2022-01-07 |
4 | 222 | 20 | 1 | 2022-01-02 |
5 | 222 | 20 | 2 | 2022-01-05 |
6 | 222 | 20 | 3 | 2022-01-09 |
7 | 333 | 10 | 1 | 2022-02-01 |
8 | 333 | 10 | 2 | 2022-02-03 |
9 | 333 | 10 | 3 | 2022-02-07 |
Write a PostgreSQL query to calculate the number of days it took for each product to move from stage 1 to the last stage (assuming not all products have the same number of stages), grouped by machine and month.
This PostgreSQL query calculates the average production time per machine per month. It joins the and tables with the table twice: the first join is with the first stage for each product, and the second join is with the last stage of each product. The difference between the two stage completion dates gives the number of days for each product's production. The outer query then groups by machine name and month to calculate the average number of production days.
For more window function practice, solve this Uber SQL Interview Question on DataLemur's online SQL coding environment:
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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 Axcelis Technologies employees, here's an example of how to use the CHECK constraint in a CREATE TABLE statement:
For a company like Axcelis Technologies that manufactures ion implantation systems, precise tracking of inventory and sales data is essential.
Given two tables and , the table has columns , , , and the table has , , , .
The goal is to find the revenue of each product sold in the year 2022.
Please see sample and input tables below, with corresponding output:
product_id | product_name | in_stock_quantity | price_per_unit |
---|---|---|---|
1001 | Ion Implanter A | 50 | 20000 |
1002 | Ion Implanter B | 30 | 25000 |
1003 | Ion Implanter C | 20 | 30000 |
sales_id | product_id | sales_date | sold_quantity |
---|---|---|---|
1 | 1001 | 01/05/2022 | 5 |
2 | 1002 | 02/15/2022 | 3 |
3 | 1001 | 03/10/2022 | 7 |
4 | 1003 | 04/22/2022 | 2 |
product_id | product_name | 2022_revenue |
---|---|---|
1001 | Ion Implanter A | 240000 |
1002 | Ion Implanter B | 75000 |
1003 | Ion Implanter C | 60000 |
The PostgreSQL query would be as follows:
In the above query, we first join the and tables using . We then use the clause to filter the sales data for the year 2022. In the statement, we calculate the total revenue for each product by multiplying the with the and summing this for each product. The clause is used to group the results by and .
A NULL value represents a missing or unknown value. It is different from a zero or a blank space, which are actual values. It is important to handle NULL values properly in SQL because they can cause unexpected results if not treated correctly.
Axcelis Technologies is a semiconductor manufacturer that has a significant customer base. The company runs a customer database storing important customer information and transaction history. The management wants to filter down the customer records based on multiple conditions to study their purchase behavior. They are interested specifically in the customers who have placed at least 3 orders in the last 5 years and whose total purchase price is more than $50000.
Assuming, the and tables have the following schema:
customer_id | name | registration_date |
---|---|---|
6171 | John Doe | 06/08/2017 |
7802 | Jane Doe | 02/10/2018 |
5293 | Emily Smith | 03/18/2019 |
6352 | James Brown | 11/26/2016 |
4517 | Robert Johnson | 07/05/2021 |
order_id | customer_id | order_date | total_price |
---|---|---|---|
1 | 6171 | 06/08/2022 | $32000 |
2 | 7802 | 06/10/2022 | $15000 |
3 | 5293 | 06/18/2022 | $20000 |
4 | 6171 | 07/26/2022 | $25000 |
5 | 7802 | 07/05/2022 | $40000 |
6 | 6171 | 08/26/2022 | $10000 |
7 | 5293 | 10/10/2022 | $30000 |
We need to write a PostgreSQL query to list the eligible customers.
In this answer, we use a subquery to filter the records in the table based on the number of orders and total purchase price of each customer in the last five years. We then join this with the table to get the corresponding customer details.
In SQL, both and are used to rank rows within a result set. The key difference between the two functions is how deal with two or more rows having the same value in the ranked column (aka how the break ties).
RANK(): When there's a tie, leaves a gap in the ranking. For example, if three rows are tied for 2nd place, the RANK() function will assign a rank of 2 to the first of these rows, a rank of 3 to the second row, and a rank of 4 to the third row.
DENSE_RANK(): For ties, does not leave a gap in the ranking. Instead, it assigns the same rank to all tied rows, and then makes the next row 1 bigger. Confusing, I know, but here's an example to make it more clear: if three rows are tied for 3rd place, the function will assign a rank of 3 to all three rows, and then assign a rank of 4 to the next row.
Axcelis Technologies, a semiconductor company, maintains a list of their equipment and their maintenance history. Each record in the maintenance logs includes when the maintenance issue was reported and when it was resolved. Given this data, can you write a SQL query that calculates the average time it takes to resolve a maintenance issue for each type of equipment?
equipment_id | equipment_type |
---|---|
1001 | Ion Implanter |
1002 | EUV Lithography System |
1003 | Asher |
log_id | equipment_id | reported_date | resolved_date |
---|---|---|---|
2001 | 1001 | 07/01/2021 08:00:00 | 07/02/2021 09:00:00 |
2002 | 1001 | 07/05/2021 11:00:00 | 07/07/2021 14:00:00 |
2003 | 1002 | 07/10/2021 07:00:00 | 07/11/2021 11:00:00 |
2004 | 1002 | 07/15/2021 09:00:00 | 07/16/2021 12:00:00 |
2005 | 1003 | 07/20/2021 13:00:00 | 07/22/2021 16:00:00 |
This query joins the equipment and maintenance logs tables on the common field and calculates the average duration between the reported and resolved times using PostgreSQL's EXTRACT function to convert the interval to hours. It then groups the results by the equipment type.
To practice a very similar question try this interactive Amazon Server Utilization Time Question which is similar for calculating total time based metrics or this Tesla Unfinished Parts Question which is similar for monitoring process completion times.
Axcelis Technologies, Inc. offers an array of manufacturing equipment used in the fabrication of semiconductor chips. Assume you're given a table which lists all the products sold each day with the quantity sold. Your task is to find the average quantity sold for each product by month.
sale_id | sold_date | product_id | units_sold |
---|---|---|---|
1 | 06/08/2022 | 1001 | 50 |
2 | 06/10/2022 | 1002 | 30 |
3 | 06/18/2022 | 1001 | 60 |
4 | 07/26/2022 | 1002 | 35 |
5 | 07/05/2022 | 1001 | 45 |
mth | product | avg_units_sold |
---|---|---|
6 | 1001 | 55.00 |
6 | 1002 | 30.00 |
7 | 1001 | 45.00 |
7 | 1002 | 35.00 |
Here, we're using to get the month from the sold_date field. We then group by both month and product_id, and get the average of units sold.
In the resulting table, we get each unique month and product_id pair (i.e., for each product for each month), and the average units sold for each of these pairs.
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. On the other hand, 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.
Here is an example of a non-correlated sub-query:
The sub-query in this case is non-correlated, as it does not reference any columns from the outer query.
Here is an example of a correlated sub-query:
This query selects the and total sales of all Axcelis Technologies customers in the sales table whose are greater than the average of their own customer group. The sub-query in this case is correlated with the outer query, as it references the column from the outer query ().
Non-correlated sub-queries are typically faster, as they only have to be executed once, wheareas correlated sub-queries are slower since they have to be re-evaluated for each row of the outer query.
The key to acing a Axcelis Technologies SQL interview is to practice, practice, and then practice some more! Beyond just solving the earlier Axcelis Technologies SQL interview questions, you should also solve the 200+ SQL questions from real Data Science & Analytics interviews which come from companies like Microsoft, Google, Amazon, and tech startups.
Each interview question has hints to guide you, detailed solutions and crucially, there's an online SQL code editor so you can easily right in the browser your query and have it checked.
To prep for the Axcelis Technologies SQL interview it is also useful to solve SQL questions from other tech companies like:
But if your SQL foundations are weak, don't worry about going right into solving questions – improve your SQL foundations with this DataLemur SQL Tutorial.
This tutorial covers SQL concepts such as LEFT vs. RIGHT JOIN and manipulating date/time data – both of which come up often during Axcelis Technologies SQL interviews.
Beyond writing SQL queries, the other types of problems to practice for the Axcelis Technologies Data Science Interview are:
To prepare for Axcelis Technologies Data Science interviews read the book Ace the Data Science Interview because it's got: