9 Tokyo Electron SQL Interview Questions - Can You Solve Them?
At Tokyo Electron, SQL is used often for analyzing semiconductor manufacturing data for optimization, and managing quality control data for improved device production. Because of this, Tokyo Electron almost always evaluates jobseekers on SQL query questions during interviews for Data Analytics, Data Science, and Data Engineering jobs.
So, if you're preparing for a SQL Interview, we've collected 9 Tokyo Electron SQL interview questions to practice, which are similar to commonly asked questions at Tokyo Electron – can you solve them?
9 Tokyo Electron SQL Interview Questions
SQL Question 1: Analyzing Semiconductor Production
Tokyo Electron is a global leader in the development and manufacturing of semiconductors. Given a table consisting of records of different semiconductor types produced, their production dates, and count of items produced, write a SQL query to calculate a running monthly total for each semiconductor type.
Assume our table, , looks like this:
Example Input:
| production_id | product_type | production_date | product_count |
|---|---|---|---|
| 1 | A | 2022-03-01 | 150 |
| 2 | A | 2022-03-15 | 200 |
| 3 | B | 2022-03-15 | 100 |
| 4 | A | 2022-04-01 | 150 |
| 5 | B | 2022-04-10 | 200 |
The expected output is a table that shows a running sum for each product type, calculated monthly. For instance, the March running total for product_type A is 350 (which is 150+200), and for product_type B is 100.
Example Output:
| month | product_type | running_total |
|---|---|---|
| 03 | A | 350 |
| 03 | B | 100 |
| 04 | A | 500 |
| 04 | B | 300 |
Answer:
Here is the SQL query that would provide the answer:
The SQL query applies PostgreSQL window functions. The window function provides a sum of products manufactured for each product type. The clause is used to calculate a separate sum for each product type. The clause, in conjunction with the window function, calculates a running total for each subsequent month.
The function is used to format the as a month ('MM').
To practice another window function question on DataLemur's free interactive SQL code editor, try this Amazon SQL Interview Question:
SQL Question 2: Analyzing Semiconductor Production Performance
Tokyo Electron is a leading global manufacturer of semiconductor production equipment. Suppose you are given two tables: one describes the semiconductors produced () and the other describes the production machines ().
Performance of the production machines significantly affects the quality of the semiconductors produced. Your task is to determine the average quality rating for semiconductors produced by each machine.
Example Input:
| semiconductor_id | machine_id | production_date | quality_rating |
|---|---|---|---|
| 111 | 333 | 06/08/2022 | 3.9 |
| 122 | 444 | 06/10/2022 | 4.2 |
| 133 | 555 | 06/15/2022 | 3.8 |
| 144 | 333 | 07/26/2022 | 3.7 |
| 155 | 444 | 07/05/2022 | 4.0 |
Example Input:
| machine_id | manufacturer | model_number |
|---|---|---|
| 333 | Tokyo Electron | AXI930 |
| 444 | Tokyo Electron | FXI800 |
| 555 | Tokyo Electron | EXI500 |
The table includes fields for a unique semiconductor ID, the ID of the machine that produced it, the production date, and the quality rating given to the semiconductor (on a scale of 1.0 to 5.0).
The table includes fields for a unique machine ID, the manufacturer of the machine, and the model number of the machine.
Answer:
To answer this question, you can join the and tables on and calculate the average quality rating for each machine.
Example Output:
| manufacturer | model_number | avg_quality_rating |
|---|---|---|
| Tokyo Electron | AXI930 | 3.8 |
| Tokyo Electron | FXI800 | 4.1 |
| Tokyo Electron | EXI500 | 3.8 |
This SQL query groups by and to give us the average for each machine. undefined
SQL Question 3: In SQL, what's the primary difference between the 'BETWEEN' and 'IN' operators?
The operator is used to select rows that fall within a certain range of values, while the operator is used to select rows that match values in a specified list.
For example, suppose you are a data analyst at Tokyo Electron and have a table of advertising campaign data. To find campaigns with a spend between 5k, you could use BETWEEN:
To find advertising campaigns that were video and image based (as opposed to text or billboard ads), you could use the operator:
SQL Question 4: Calculate the Average Agreement Rate by Noble Gas and Year for Tokyo Electron
Tokyo Electron Ltd. is a leading global supplier of semiconductor and flat panel display (FPD) production equipment. Their machines often use different noble gases such as Helium, Neon, or Argon. For the management of these resources, you are given a table named "usage" which includes the information of the noble gas used, the year, and the agreement rate. The agreement rate is the percentage of time the machines effectively made use of the noble gases supplied to them.
The "usage" table has the following schema:
Example Input:
| usage_id | year | noble_gas | agreement_rate(%) |
|---|---|---|---|
| 1 | 2022 | Helium | 95 |
| 2 | 2022 | Argon | 85 |
| 3 | 2021 | Neon | 89 |
| 4 | 2021 | Helium | 92 |
| 5 | 2022 | Neon | 88 |
The task is to write a SQL query that calculates the average agreement rate per year for each noble gas. The result should be rounded to two decimal places. Filter out any averages that are below 90%.
Use the example input and simulate the desired output:
Example Output:
| year | noble_gas | avg_agreement_rate(%) |
|---|---|---|
| 2022 | Helium | 95.00 |
| 2021 | Helium | 92.00 |
Answer:
This query first groups the table by the and the columns. Afterwards, it calculates the average for each group. The HAVING keyword is then used to filter out the groups where the average agreement rate is below 90%. Finally, the result is rounded to two decimal places for readability. undefined
SQL Question 5: What does database normalization mean?
Database normalization is the process of breaking down a table into smaller and more specific tables and defining relationships between them via foreign keys. This minimizes redundancy, and creates a database that's more flexible, scalable, and easier to maintain. It also helps to ensure the integrity of the data by minimizing the risk of data inconsistencies and anomalies.
SQL Question 6: Finding Customer Contacts in Tokyo
Tokyo Electron is a leading global company in the electronics and semiconductor industry. Suppose you are given a table of customer data. Can you write a SQL query to find the email addresses of all customers whose addresses contain the string "Tokyo"?
Example Input:
| customer_id | first_name | last_name | address | |
|---|---|---|---|---|
| 1 | John | Doe | john.doe@example.com | 123 Tokyo St, Tokyo |
| 2 | Jane | Smith | jane.smith@example.com | 456 Osaka St, Osaka |
| 3 | Jake | Williams | jake.williams@example.com | 789 Kyoto Ave, Kyoto |
| 4 | Jim | Brown | jim.brown@example.com | 321 Nihonbashi, Tokyo |
| 5 | Jill | Johnson | jill.johnson@example.com | 654 Umeda, Osaka |
Answer:
This SQL query uses the LIKE keyword in combination with '%' as a wildcard to search for any customers whose address contains the string "Tokyo". It will return the email addresses of customers John Doe and Jim Brown, as they are the ones with "Tokyo" in their addresses. undefined
SQL Question 7: How does the constraint function, and in what scenarios might it be useful?
The CHECK constraint is used to enforce rules on the data in a specific column. If a row is inserted or updated with data that does not follow the CHECK constraint's rule, the operation will fail.
For example, say you had a marketing analytics database that stores ad campaign data from Tokyo Electron's Google Analytics account.
Here's what some constraints could look like:
The CHECK constraint is used in the above example to make sure that the "budget" and "cost_per_click" fields contain only positive values. This helps to maintain the validity and integrity of the data in the context of ad campaigns.
The CHECK constraint can also be used to enforce other specific conditions on data. For example, you can use it to ensure that the "start_date" for each ad campaign is earlier than the "end_date."
SQL Question 8: Analyze Customer history with Product purchases
Tokyo Electron has a database containing Sales and Customer information. The table includes fields such as , , , and . The table includes , , , and .
Write a SQL query that lists each customer with the total number of unique products they have purchased, the total amount spent, and their personal information. Only include customers who have made at least one purchase.
Example Input:
| sale_id | customer_id | product_id | price | sale_date |
|---|---|---|---|---|
| 1 | 100 | 101 | 200 | 2022-01-10 |
| 2 | 100 | 102 | 300 | 2022-02-20 |
| 3 | 101 | 101 | 200 | 2022-02-25 |
| 4 | 102 | 103 | 400 | 2022-03-10 |
| 5 | 100 | 101 | 200 | 2022-03-15 |
Example Input:
| customer_id | first_name | last_name | |
|---|---|---|---|
| 100 | John | Doe | john.doe@example.com |
| 101 | Jane | Smith | jane.smith@example.com |
| 102 | Bob | Marley | bob.marley@example.com |
Answer:
This query first joins the and tables using the field. Then, for each customer, it counts the number of unique products () and sums the sale prices (). The clause ensures we only include customers who have purchased at least one product.
Since joins come up frequently during SQL interviews, try an interactive Spotify JOIN SQL question:
SQL Question 9: Equipment Usage Statistics
Given a database that stores maintenance scheduled for each production equipment of Tokyo Electron, find out the number of maintenance sessions by month and also the average downtime for each equipment due to maintenance.
Example Input:
| maintenance_id | equipment_id | start_time | end_time |
|---|---|---|---|
| 1001 | E1 | 01/29/2021 09:00:00 | 01/29/2021 12:00:00 |
| 1002 | E2 | 02/15/2021 13:00:00 | 02/15/2021 16:00:00 |
| 1003 | E1 | 03/02/2021 10:00:00 | 03/02/2021 14:00:00 |
| 1004 | E2 | 03/22/2021 11:00:00 | 03/22/2021 14:00:00 |
| 1005 | E1 | 04/05/2021 09:00:00 | 04/05/2021 12:00:00 |
Example Output:
| month | equipment_id | maintenance_count | avg_downtime_hours |
|---|---|---|---|
| 1 | E1 | 1 | 3.00 |
| 2 | E2 | 1 | 3.00 |
| 3 | E1 | 1 | 4.00 |
| 3 | E2 | 1 | 3.00 |
| 4 | E1 | 1 | 3.00 |
Answer:
Explanation:
This query uses the EXTRACT function to isolate the month from the timestamp, and groups by and that derived month. The query counts the number of maintenances for each group to get the monthly maintenance sessions (maintenance_count).
To calculate the average downtime (avg_downtime_hours), it subtracts from which gives an interval, and the EXTRACT function with the EPOCH keyword is used to convert that interval to seconds. The value is then divided by 3600 to convert the seconds to hours. The AVG function is used to calculate the average of these hours. The results are ordered by and to present a clearer view of the information. undefined
How To Prepare for the Tokyo Electron SQL Interview
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Tokyo Electron SQL interview is to solve as many practice SQL interview questions as you can!
Beyond just solving the above Tokyo Electron SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like Microsoft, Google, and Facebook.
Each exercise has multiple hints, step-by-step solutions and crucially, there is an interactive SQL code editor so you can easily right in the browser your SQL query answer and have it executed.
To prep for the Tokyo Electron SQL interview it is also a great idea to solve SQL questions from other tech companies like:
But if your SQL skills are weak, forget about jumping right into solving questions – go learn SQL with this interactive SQL tutorial.
This tutorial covers topics including using LIKE and finding NULLs – both of these come up frequently in Tokyo Electron interviews.
Tokyo Electron Data Science Interview Tips
What Do Tokyo Electron Data Science Interviews Cover?
For the Tokyo Electron Data Science Interview, in addition to SQL query questions, the other types of questions which are covered:
- Probability & Statistics Questions
- Python or R Coding Questions
- Data Case Study Questions
- Machine Learning and Predictive Modeling Questions
- Behavioral & Resume-Based Questions
How To Prepare for Tokyo Electron Data Science Interviews?
To prepare for Tokyo Electron Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions sourced from FAANG & startups
- a crash course covering SQL, Product-Sense & ML
- over 900+ reviews on Amazon & 4.5-star rating