10 FormFactor SQL Interview Questions (Updated 2024)
At FormFactor, SQL is used all the damn time for analyzing complex semiconductor testing data, and managing and organizing information within customer and product databases. Unsurprisingly this is why FormFactor often tests SQL questions in interviews for Data Analytics, Data Science, and Data Engineering jobs.
Thus, to help you prepare for the FormFactor SQL interview, here’s 10 FormFactor SQL interview questions – able to solve them?
10 FormFactor SQL Interview Questions
SQL Question 1: Identifying High-Value Customers
FormFactor is a manufacturing company that develops testing and measurement technologies. Among their business key metrics, they've defined a 'power user' as a customer who has purchased at least $10,000 worth of products in a single month.
Write a SQL query that identifies all 'power users' for each month.
Example Data:
| order_id | customer_id | order_date | product_id | product_price |
|---|---|---|---|---|
| 1001 | 301 | 08/01/2022 | 101 | 5000.00 |
| 1002 | 301 | 08/03/2022 | 102 | 3000.00 |
| 1003 | 302 | 08/05/2022 | 101 | 5000.00 |
| 1004 | 301 | 08/10/2022 | 103 | 3000.00 |
| 1005 | 303 | 08/20/2022 | 101 | 5000.00 |
Answer:
To accomplish this task you can group by the and the month of the , then sum the . Finally, you filter out groups whose total purchase is less than $10,000.
Here is a possible PostgreSQL query:
This query groups all orders by customer and month and sums up the total purchase amount for the orders within each group. It then filters out all the groups whose total purchase amount is less than $10,000. The output will be a list of customer IDs and the respective month in which the customer was identified as a 'power user'.
To work on another SQL customer analytics question where you can code right in the browser and have your SQL query automatically checked, try this Walmart Labs SQL Interview Question:
SQL Question 2: Calculate the monthly sales quantity of each product
FormFactor Inc. wants to analyze the monthly sales quantity of their products to understand the product performance over time. You have been given a "sales" table to analyze.
The "sales" table is structured as follows:
Example Input:
| sale_id | product_id | sale_date | quantity |
|---|---|---|---|
| 1 | 1001 | 2021-09-10 | 2 |
| 2 | 1002 | 2021-09-15 | 5 |
| 3 | 1001 | 2021-09-25 | 3 |
| 4 | 1002 | 2021-10-11 | 4 |
| 5 | 1001 | 2021-10-20 | 1 |
Write a SQL query that will return a table that provides the total quantity of each product sold each month.
The expected output should be formatted as follows:
Example Output:
| month | product_id | total_quantity |
|---|---|---|
| 2021-09 | 1001 | 5 |
| 2021-09 | 1002 | 5 |
| 2021-10 | 1001 | 1 |
| 2021-10 | 1002 | 4 |
Answer:
This query first formats the "sale_date" to only include Year-Month and labels it as "month". Then it groups these data by "month" and "product_id". The SUM function is used to calculate the total quantity of each product sold each month. The result is ordered by "month" and by "total_quantity" in descending order so that we can more easily see which products sold the most each month.
p.s. Window functions show up super frequently during SQL interviews, so practice the 27+ window function questions on DataLemur
SQL Question 3: What are some ways you can identify duplicates in a table?
"One creative way is to use the window function ROW_NUMBER() and partition by whichver columns you are loooking for duplicates in. If any row has a row_number() more than 1, then it's a duplicate!
You could also use COUNT(DISTINCT col1) vs. COUNT(*) and see if they return different numbers, indicating the prescence of duplicates!
SQL Question 4: Average Wafer Test Time
As a database analyst at FormFactor, a leading manufacturer of test and measurement equipment, you are tasked with finding the average time taken to test a wafer by each testing machine in the company. Each test on a wafer is logged in your system with a start time and end time.
Example Input:
| test_id | machine_id | wafer_id | start_time | end_time |
|---|---|---|---|---|
| 1 | 101 | 2001 | 2022-01-01 8:00:00 | 2022-01-01 8:10:00 |
| 2 | 101 | 2002 | 2022-01-01 9:00:00 | 2022-01-01 9:15:00 |
| 3 | 101 | 2005 | 2022-01-01 10:00:00 | 2022-01-01 10:05:00 |
| 4 | 102 | 2003 | 2022-01-01 8:20:00 | 2022-01-01 8:40:00 |
| 5 | 102 | 2004 | 2022-01-01 11:00:00 | 2022-01-01 11:10:00 |
Answer:
In this SQL query, we are using the to calculate the difference in minutes between the start_time and end_time for each test. The function is then used to calculate the average of these differences for each machine ().
Example Output:
| machine_id | avg_test_time_minutes |
|---|---|
| 101 | 10 |
| 102 | 15 |
The result shows us each machine's average wafer test time in minutes.
To practice a very similar question try this interactive Amazon Server Utilization Time Question which is similar for calculating total duration using start-time and end-time or this Tesla Unfinished Parts Question which is similar for analyzing process duration tied to specific machines.
SQL Question 5: Could you provide a list of the join types in SQL and explain what each one does?
In SQL, a join retrieves rows from multiple tables and combines them into a single result set.
Four JOIN types can be found in SQL. For an example of each one, suppose you had a table of FormFactor orders and FormFactor customers.
-
INNER JOIN: Rows from both tables are retrieved when there is a match in the shared key or keys. An between the Orders and Customers tables would return only rows where the in the Orders table matches the in the Customers table.
-
LEFT JOIN: A retrieves all rows from the left table (in this case, the Orders table) and any matching rows from the right table (the Customers table). If there is no match in the right table, NULL values will be returned for the right table's columns.
-
RIGHT JOIN: A combines all rows from the right table (in this case, the Customers table) and any matching rows from the left table (the Orders table). If there is no match in the left table, NULL values will be displayed for the left table's columns.
-
FULL OUTER JOIN: A combines all rows from both tables, regardless of whether there is a match in the shared key or keys. If there is no match, NULL values will be displayed for the columns of the non-matching table.
SQL Question 6: Calculation of Average Sales per Month for Our Products
As FormFactor operates in the semiconductor industry, and they are responsible for the production and selling of probe cards used in the testing phases of the manufacturing process. We have a sales records table that lists every sale we made across various months. For business analytics and sales stats, we often need to find an average amount of sales for our individual products on a monthly basis. Given a table , can you write a SQL query to return the average sales for each product per month?
Example Input:
| sales_id | product_id | sales_date | units_sold | price_per_unit |
|---|---|---|---|---|
| 1052 | 301 | 03/08/2022 | 25 | 200 |
| 1589 | 402 | 03/15/2022 | 40 | 150 |
| 9025 | 501 | 04/05/2022 | 30 | 175 |
| 3061 | 402 | 04/28/2022 | 35 | 150 |
| 4712 | 301 | 05/14/2022 | 20 | 200 |
Example Output:
| month | product_id | avg_sales |
|---|---|---|
| 3 | 301 | 5000 |
| 3 | 402 | 6000 |
| 4 | 501 | 5250 |
| 4 | 402 | 5250 |
| 5 | 301 | 4000 |
Answer:
The question asks for the average sales per product per month. The SQL query joins units sold with the price per unit (units_sold*price_per_unit) to get the total sales per record. The function is used to get the month from the sales_date. Finally, the clause groups the records by month and product_id, while the function calculates the average sales for each group.
SQL Question 7: Can you describe the difference between a clustered and a non-clustered index?
Both clustered and non-clustered indexes are used to improve query performance, but they have different characteristics.
A clustered index determines the physical order of the data rows in a table, while a non-clustered index does not.This means that a table can have only one clustered index, but it can have multiple non-clustered indexes.
In terms of query performance, a clustered index is usually faster for searches that return a large number of records, while a non-clustered index is faster for searches that return a small number of records. However, updates to a clustered index are slower, as they require the data rows to be physically rearranged, while updates to a non-clustered index are faster, as they only require the index data structure to be updated.
SQL Question 8: Calculate the Total Revenue per Product
Given the two tables and , write a SQL query to calculate the total revenue per product. Assume that the table records all products bought by customers at FormFactor, and the table has information about each product including its price.
Example Input:
| customer_id | first_name | last_name |
|---|---|---|
| 1 | John | Doe |
| 2 | Jane | Smith |
| 3 | Jerry | Wilson |
| 4 | Tom | Brown |
Example Input:
| sale_id | customer_id | product_id |
|---|---|---|
| 111 | 1 | 101 |
| 222 | 1 | 102 |
| 333 | 2 | 101 |
| 444 | 3 | 103 |
| 555 | 4 | 104 |
Example Input:
| product_id | product_name | price |
|---|---|---|
| 101 | Product1 | 20 |
| 102 | Product2 | 30 |
| 103 | Product3 | 15 |
| 104 | Product4 | 25 |
The output of this query should be the total revenue generated by each product. Revenue for a product is calculated as the total number of sales of that product multiplied by its price.
Example Output:
| product_id | total_revenue |
|---|---|
| 101 | 40 |
| 102 | 30 |
| 103 | 15 |
| 104 | 25 |
Answer:
In this query, we joined the and tables on . We used the function to get the total number of times the product was sold and then we multiplied it by the to get the total revenue for each product. Finally, we grouped the results by to get the total revenue per product.
Because join questions come up so often during SQL interviews, take a stab at this Snapchat Join SQL question:
SQL Question 9: Analyze Sales Data
Let's assume that at FormFactor, we have a table that keeps track of all product sales transactions. Each transaction record includes a , the for the item that was sold, the (Date the item was sold), and (Sale price of the product).
Your task is to calculate the absolute change in the monthly average sale price for each product between the months of March and April. Also, calculate the square root of the absolute change in the percentage of sales between the two months. Round the results to two decimal places.
Example Input:
| transaction_id | sale_date | product_id | sale_price |
|---|---|---|---|
| 100 | 2022-03-01 | 101 | 500 |
| 101 | 2022-03-02 | 101 | 600 |
| 102 | 2022-03-03 | 102 | 100 |
| 103 | 2022-04-01 | 101 | 550 |
| 104 | 2022-04-02 | 101 | 400 |
| 105 | 2022-04-03 | 102 | 110 |
Answer:
Using PostgreSQL, we would use the , , , and basic arithmetic operations as stated below:
In the above query, we first calculate the average sale price and total sales for each product for the month of March using a CTE (Common Table Expression), . We then repeat that for the month of April using another CTE, . Finally, we join these two on to calculate the absolute change in average sale prices and absolute percentage change in total sales between the two months, subsequently obtaining the square root of the percentage and rounding the results.
To practice a very similar question try this interactive Wayfair Y-on-Y Growth Rate Question which is similar for calculating change in sales over time or this Amazon Average Review Ratings Question which is similar for analyzing monthly averages.
SQL Question 10: Can you give some examples of when denormalization might be a good idea?
Database normalization is helpful because it improves the performance, flexibility, and scalability of a database. Some specific benefits of normalization include:
-
Reducing Redundancy: Normalization helps to minimize redundancy by breaking down a larger general table into smaller, more specific tables. This can often reduce the amount of data that needs to be accessed for particular queries.
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Improving Data Integrity: Normalization helps to ensure the integrity of the data by minimizing the risk of data inconsistencies and anomalies. It does this by establishing clear relationships between the tables via primary and foreign keys.
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Increasing Flexibility: Normalization makes it easier to modify the structure of the database, as there is less redundancy, so it allows you to make changes to one table without affecting others. This makes it easier to adapt the database to changing business needs (and change is basically constant at FormFactor!)
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Improving Performance: Normalization can improve the performance of the database by reducing the amount of data that needs to be stored and accessed in a single table. This can result in faster query times and better overall performance.
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Easier Data Modeling: Normalization can make it easier to model the data in a logical and intuitive way, since less random data is commingled together, which improves the overall database design.
FormFactor SQL Interview Tips
The best way to prepare for a FormFactor SQL interview is to practice, practice, practice.
In addition to solving the above FormFactor SQL interview questions, you should also solve the 200+ SQL coding questions which come from companies like Netflix, Google, and Amazon.
Each exercise has hints to guide you, 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 FormFactor SQL interview you can also be useful to solve SQL questions from other tech companies like:
In case your SQL foundations are weak, don't worry about diving straight into solving questions – strengthen your SQL foundations with this free SQL tutorial.
This tutorial covers topics including math functions like CEIL()/FLOOR() and LEAD/LAG – both of which come up routinely during FormFactor SQL assessments.
FormFactor Data Science Interview Tips
What Do FormFactor Data Science Interviews Cover?
Beyond writing SQL queries, the other types of problems covered in the FormFactor Data Science Interview are:
- Probability & Stats Questions
- Coding Questions in Python or R
- Analytics and Product-Metrics Questions
- Machine Learning and Predictive Modeling Questions
- Resume-Based Behavioral Questions
How To Prepare for FormFactor Data Science Interviews?
The best way to prepare for FormFactor Data Science interviews is by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from FAANG tech companies
- A Refresher covering Stats, SQL & ML
- Great Reviews (900+ reviews, 4.5-star rating)