9 Formosa Sumco SQL Interview Questions (Updated 2024)
At Formosa Sumco Technology, SQL is used often for analyzing semiconductor manufacturing data and optimizing yield rates, as well as for predicting silicon wafer demand to streamline inventory management. Unsurprisingly this is why Formosa Sumco often tests SQL questions in interviews for Data Science, Data Engineering and Data Analytics jobs.
Thus, to help you practice for the Formosa Sumco SQL interview, here’s 9 Formosa Sumco Technology SQL interview questions – can you solve them?
9 Formosa Sumco Technology SQL Interview Questions
SQL Question 1: Compute Average Monthly Sales Per Product
For Formosa Sumco, a leading provider of silicon wafers, a useful analysis might involve calculating the average monthly sales of their different product types, by using window functions to group them using a given month. In this example, we will compute the average monthly sales per product, returning a list sorted by product and month.
Considering a dataset called "sales" which contains a record of all the sales made in a specific time frame, the table is structured as follows:
Example Input:
| sale_id | product_id | purchase_date | quantity | price_per_unit |
|---|---|---|---|---|
| 101 | A | 01/01/2021 | 10 | 10.00 |
| 102 | B | 01/02/2021 | 15 | 8.00 |
| 103 | A | 01/15/2021 | 20 | 10.00 |
| 201 | B | 02/01/2021 | 12 | 8.00 |
| 202 | A | 02/10/2021 | 30 | 10.00 |
Your task is to write a SQL query to calculate and display, for each product and each month, the sum of the sales (value) and the average sales (value). The sales value of each sale is obtained by multiplying quantity with price_per_unit.
Answer:
For PostgreSQL, the SQL query to solve the question would be:
This SQL query uses the GROUP BY clause to segment the sales records into groups, one for each product in each month. The EXTRACT function is used to pull the month from the purchase_date column. For each of these groups, the SUM function calculates the total sales and the AVG function calculates the average sales - both as the product of quantity and price_per_unit.
This solution makes use of standard SQL aggregate functions (SUM, AVG) in conjunction with GROUP BY, rather than using window functions, as they are more fit for this purpose and will generally give better performance. However, if additional complexity was needed - for instance, if we needed running totals or access to rows other than the current row - then window functions might be more appropriate.
p.s. Window functions show up super often during SQL interviews, so practice the 27+ window function questions on DataLemur
SQL Question 2: Inventory Analysis of Formosa Sumco
Formosa Sumco is a company specializing in the manufacturing of semiconductors. The firm has multiple factories spread across various geographical locations and maintains an inventory of raw materials and finished products.
The company wants to ensure that the production process continues smoothly and that the final product availability aligns with the demand. Analyze the database to identify the factories where raw materials are running low and which products might be in high demand.
You are provided with two tables, , and .
Table:
| factory_id | location |
|---|---|
| 1 | Taipei |
| 2 | Hsinchu |
| 3 | Taichung |
Table:
| product_id | factory_id | raw_materials | finished_products |
|---|---|---|---|
| 1001 | 1 | 500 | 30 |
| 1002 | 2 | 200 | 25 |
| 1001 | 2 | 300 | 50 |
| 1003 | 3 | 250 | 20 |
| 1002 | 3 | 400 | 35 |
Answer:
This query first calculates the minimum amount of raw materials across all products for each factory, then it counts the total number of each finished product across all factories. This information is used to identify which products are in high demand across all factories. The output of this query will show the minimum raw materials available at each factory and the product with the highest demand in terms of finished products.
SQL Question 3: How does an inner join differ from 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 Formosa Sumco 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 Based on Region and Price
As a data analyst at Formosa Sumco, you are asked to get a list of customers who placed an order in the region with a total price (quantity*unit_price) over $3000.
Assume three tables: , , and .
Example Input:
| customer_id | name | region |
|---|---|---|
| 1 | John Smith | West |
| 2 | Jane Doe | East |
| 3 | Tom Johnson | West |
| 4 | Emily Davis | East |
Example Input:
| order_id | customer_id | order_date |
|---|---|---|
| 1001 | 1 | 06/08/2022 |
| 1002 | 2 | 06/10/2022 |
| 1003 | 3 | 06/18/2022 |
| 1004 | 4 | 07/26/2022 |
Example Input:
| order_id | product_id | quantity | unit_price |
|---|---|---|---|
| 1001 | 50001 | 10 | 300 |
| 1002 | 69852 | 20 | 250 |
| 1003 | 50001 | 30 | 200 |
| 1004 | 69852 | 40 | 50 |
Example Output:
| customer_id | name | region | total_price |
|---|---|---|---|
| 1 | John Smith | West | 3000 |
| 3 | Tom Johnson | West | 6000 |
Answer:
This SQL query first joins the three tables based on their common key, then it filters the result to only include records from the 'West' region and finally, the HAVING clause ensures that only customer with order total (quantity * unit_price) greater than $3000 are returned.
SQL Question 5: In SQL, Are NULL values the same as a zero or blank space?
{#Question-5}
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.
SQL Question 6: Calculate Average Sales
Formosa Sumco is a company that specializes in the production of silicon wafers for semiconductors. For this question, assume they sell their products to different companies. You are given a sales table. Each row records a sale to a company with the price and the sale date. Find the average sales per month for each product.
Example Input:
| sales_id | company_id | sale_date | product_id | sale_price |
|---|---|---|---|---|
| 1011 | 456 | 06/13/2022 | 80001 | 54000 |
| 8901 | 856 | 06/27/2022 | 67052 | 45000 |
| 8210 | 565 | 06/06/2022 | 80001 | 54000 |
| 6450 | 785 | 07/16/2022 | 67052 | 57000 |
| 7730 | 963 | 07/12/2022 | 67052 | 57000 |
Example Output:
| month | product_id | avg_sale_price |
|---|---|---|
| 6 | 80001 | 54000.00 |
| 6 | 67052 | 45000.00 |
| 7 | 67052 | 57000.00 |
Answer:
This query extracts the month from the sale_date, and then it groups by both the month and the product_id. The average sale_price is calculated for each grouping. The results are sorted by month then product_id.
SQL Question 7: What does the function do, and when would you use it?
The function can take in multiple paramaters, and returns the first input paramater that is not null. If all arguments are null, the COALESCE function will return null too.
Suppose you have a table of Formosa Sumco salespeople and the number of deals they closed. If a salesperson didn't close any deals, the data from the 3rd-party CRM system exports a NULL value.
| sales_person | closed_deals |
|---|---|
| Jason Wright | NULL |
| Drew Jackson | 3 |
| Chris Ho | NULL |
| Adam Cohen | 2 |
| Samantha Perez | 4 |
To change these NULLs to zeros, you can use the function in the following way:
This would result in the following data:
| sales_person | closed_deals |
|---|---|
| Jason Wright | 0 |
| Drew Jackson | 3 |
| Chris Ho | 0 |
| Adam Cohen | 2 |
| Samantha Perez | 4 |
SQL Question 8: Calculate a Department's Average Overtime
In the company, Formosa Sumco, each department has multiple employees. Due to the nature of work, all employees could potentially work overtime. The number of overtime hours each employee has each month is documented.
Your task is to write an SQL query to calculate the average overtime worked per month in each department and round the average to two decimal points. Also, rank the departments based on the average overtime in descending order.
Example Input:
| dept_id | dept_name |
|---|---|
| 001 | Manufacturing |
| 002 | Sales |
| 003 | IT |
| 004 | HR |
Example Input:
| emp_id | dept_id | emp_name |
|---|---|---|
| 1001 | 001 | John Doe |
| 1002 | 002 | Jane Doe |
| 1003 | 003 | Jim Brown |
| 1004 | 004 | Jill Smith |
Example Input:
| emp_id | month_year | overtime_hours |
|---|---|---|
| 1001 | 2022-06 | 22 |
| 1002 | 2022-06 | 18 |
| 1003 | 2022-06 | 24 |
| 1004 | 2022-06 | 20 |
| 1001 | 2022-07 | 25 |
| 1002 | 2022-07 | 10 |
| 1003 | 2022-07 | 20 |
| 1004 | 2022-07 | 30 |
Example Output:
| month_year | dept_name | avg_overtime |
|---|---|---|
| 2022-06 | Manufacturing | 22.00 |
| 2022-06 | IT | 24.00 |
| 2022-06 | HR | 20.00 |
| 2022-06 | Sales | 18.00 |
| 2022-07 | HR | 30.00 |
| 2022-07 | Manufacturing | 25.00 |
| 2022-07 | IT | 20.00 |
| 2022-07 | Sales | 10.00 |
Answer:
This query first joins the , and tables on the relevant foreign keys ( and ). It then groups the data by and in order to compute the average overtime for each department for each month. The ROUND function is used to limit the average overtime to two decimal places. The ORDER BY clause is used to ensure that the output is sorted in descending order of average overtime.
The two most similar questions are:
-
"Sending vs. Opening Snaps" from Snapchat: This question deals with calculating time-based averages and rounding the results, similar to your task of calculating average overtime.
-
"Average Review Ratings" from Amazon: This question also involves calculating averages and grouping by a specific attribute, similar to your problem of calculating and grouping average overtime by department.
Now, here is the requested markdown:
To practice a very similar question try this interactive Snapchat Sending vs. Opening Snaps Question which is similar for calculating time-based averages and rounding or this Amazon Average Review Ratings Question which is similar for calculating averaged grouped by specific attribute.
SQL Question 9: Track Production Proficiency
Formosa Sumco, a semiconductor wafer production company, wants to analyze the production proficiency and efficiency of each machine in their premises. They are specifically interested in knowing the total number of wafers produced per machine each month.
We are given the following data:
Example Input:
| log_id | machine_id | operation_time | wafer_id |
|---|---|---|---|
| 1001 | M1 | 01/04/2022 12:00:00 | W100 |
| 1002 | M2 | 01/09/2022 16:00:00 | W101 |
| 1003 | M1 | 02/20/2022 09:30:00 | W102 |
| 1004 | M2 | 02/22/2022 13:00:00 | W103 |
| 1005 | M1 | 03/14/2022 14:30:00 | W104 |
Write an SQL query to return a table showing total wafers produced by each machine every month. The operation_time column records the timestamp when a wafer is finished.
Answer:
This query first uses the EXTRACT function to isolate the month part of the operation_time column. It then groups the data by the month and machine_id, with a count of wafer_id to calculate the total number of wafers produced. Finally, it orders the result by month and machine_id, to make the analysis easier for the decision makers at Formosa Sumco.
How To Prepare for the Formosa Sumco SQL Interview
The key to acing a Formosa Sumco SQL interview is to practice, practice, and then practice some more!
In addition to solving the earlier Formosa Sumco SQL interview questions, you should also solve the 200+ tricky sql questions on DataLemur which come from companies like Google, Microsoft and Silicon Valley startups.
Each problem on DataLemur has hints to guide you, detailed solutions and crucially, there's an online SQL coding environment so you can instantly run your SQL query answer and have it checked.
To prep for the Formosa Sumco SQL interview you can also be a great idea to solve SQL problems from other tech companies like:
In case your SQL query skills are weak, don't worry about jumping right into solving questions – strengthen your SQL foundations with this DataLemur SQL tutorial.
This tutorial covers topics including filtering data with boolean operators and Subqueries – both of which show up often during Formosa Sumco interviews.
Formosa Sumco Technology Data Science Interview Tips
What Do Formosa Sumco Data Science Interviews Cover?
In addition to SQL query questions, the other types of problems tested in the Formosa Sumco Data Science Interview are:
- Statistics & Probability Questions
- Python or R Coding Questions
- Open-Ended Data Case Studies
- ML Modelling Questions
- Behavioral Interview Questions
How To Prepare for Formosa Sumco Data Science Interviews?
To prepare for Formosa Sumco Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions taken from FAANG, tech startups, and Wall Street
- a crash course on SQL, AB Testing & ML
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