8 Wolfspeed SQL Interview Questions (Updated 2024)
At Wolfspeed, SQL is often used for analyzing semiconductor performance metrics and for getting insights from datasets related to power and RF solutions. For this reason Wolfspeed almost always asks SQL questions during interviews for Data Science and Data Engineering positions.
So, to help you ace the Wolfspeed SQL interview, we've curated 8 Wolfspeed SQL interview questions – how many can you solve?
8 Wolfspeed SQL Interview Questions
SQL Question 1: Analyzing Product Performance over Time
At Wolfspeed, a global leader in wide bandgap semiconductor technology, we rely on consistent feedback to improve our products. You are given access to a customer reviews dataset containing the following information: the review ID, the user ID, the date the review was submitted, the product ID, and the number of stars awarded by the reviewer.
Given this dataset, write a SQL query that calculates the average star rating for each product on a monthly basis. For this problem, assume that the column is of the datatype.
Example Input:
| review_id | user_id | submit_date | product_id | stars |
|---|---|---|---|---|
| 6171 | 123 | 2020-06-08 00:00:00 | 50001 | 4 |
| 7802 | 265 | 2020-06-10 00:00:00 | 69852 | 4 |
| 5293 | 362 | 2020-06-18 00:00:00 | 50001 | 3 |
| 6352 | 192 | 2020-07-26 00:00:00 | 69852 | 3 |
| 4517 | 981 | 2020-07-05 00:00:00 | 69852 | 2 |
Answer:
In this solution, we use a combination of the SQL and functions to group the data by month and product ID, and then calculate the monthly average star rating for each product. The results are ordered by month in ascending order and by average star rating in descending order so that products with higher ratings appear first for each month.
We also use to get the month part from the timestamp and to round the average rating to two decimal places.
Expected Output:
| mth | product | avg_stars |
|---|---|---|
| 6 | 50001 | 3.50 |
| 6 | 69852 | 4.00 |
| 7 | 69852 | 2.50 |
To practice another window function question on DataLemur's free interactive SQL code editor, solve this Google SQL Interview Question:
SQL Question 2: Product Performance Analysis
As a Data Analyst working at Wolfspeed, a semiconductor company specialized in silicon carbide and GaN on SiC technologies, you are given two tables:
- table that contains information about the products including their ID, name and type.
- table that contains information about sales transactions including the sale ID, product ID, region, number of units sold and total revenue.
Your task is to come up with a SQL query that shows the total revenue and number of units sold per product type for each region in the year 2022.
Example Input:
| product_id | product_name | product_type |
|---|---|---|
| 50001 | SiC MOSFET | Power |
| 69852 | GaN on SiC HEMT | RF |
| 85423 | SiC Diode | Power |
Example Input:
| sale_id | product_id | region | units_sold | revenue |
|---|---|---|---|---|
| 6171 | 50001 | North America | 50 | 5000 |
| 7802 | 69852 | Asia | 30 | 8000 |
| 5293 | 50001 | Europe | 60 | 6000 |
| 6352 | 69852 | North America | 20 | 5000 |
| 4517 | 85423 | Asia | 40 | 4000 |
Answer:
This query joins table and table on the , and then uses the clause to get the data per product type for each region. The function is used to calculate the total number of units sold and total revenue.
SQL Question 3: What are the similarities and differences between correleated and non-correlated sub-queries?
A correlated sub-query is a sub-query that depends on the outer query and cannot be executed separately. It uses the outer query to filter or transform data by referencing a column from it, and the outer query uses the results of the inner query. In contrast, a non-correlated sub-query is independent of the outer query and can be executed on its own. It does not reference any columns from the outer query and is used to retrieve additional data needed by the outer query.
Here is an example of a correlated sub-query:
This query selects the and total sales of all Wolfspeed 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 ().
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.
Performance-wise, correlated sub-queries are generally slower to execute, as they have to be re-evaluated for each row of the outer query, while non-correlated sub-queries are faster, as they only have to be executed once.
SQL Question 4: Average Power Usage by Product Type
Wolfspeed is a company that creates silicon carbide and GaN on Silicon Carbide technologies and materials. Suppose we are in the Quality Assurance department and we are interested in finding out the average power usage by each product type across all batches manufactured in each year. We will base our calculations on two tables, and .
Example Input:
| product_id | product_type |
|---|---|
| 2987 | SiC |
| 3612 | GaN |
| 7295 | SiC |
| 9036 | GaN |
| 5418 | SiC |
Example Input:
| batch_id | product_id | manufacture_year | power_usage |
|---|---|---|---|
| 1 | 2987 | 2020 | 540 |
| 2 | 3612 | 2021 | 690 |
| 3 | 2987 | 2021 | 635 |
| 4 | 5418 | 2020 | 720 |
| 5 | 9036 | 2021 | 520 |
We would like to create a new table that shows the average power usage by each product type for each manufacture year.
Example Output:
| manufacture_year | product_type | avg_power_usage |
|---|---|---|
| 2020 | SiC | 630 |
| 2021 | GaN | 605 |
| 2021 | SiC | 635 |
Answer:
In this query, we first perform an INNER JOIN of the and tables on . Then, we group by the manufacture year and product type. For each group, we compute the average power usage, which is renamed to for clarity. The results are ordered by manufacture year and product type for simplicity and readability.
SQL Question 5: What does it mean to use a UNIQUE constraint in a database?
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 Wolfspeed sales leads data stored in a database, here's some constraints you'd use:
In this example, the UNIQUE constraint is applied to the "email" and "phone" fields to ensure that each Wolfspeed lead has a unique email address and phone number. This helps to ensure the integrity of the data in the database and prevents errors that could occur if two leads had the same email address or phone number.
SQL Question 6: Analyzing Customer Orders and Accounts Receivable
Wolfspeed is concerned about their receivables collection performance. They want a clear understanding of the payments that are still pending. Write a SQL query that will help them join the customer orders table and the accounts receivable table to find out the total pending payment for each customer.
Here's your sample tables:
Input Table:
| order_id | customer_id | order_total | order_date |
|---|---|---|---|
| 1201 | 987 | 2000 | 2022-06-08 |
| 3452 | 315 | 3500 | 2022-06-10 |
| 6752 | 987 | 1500 | 2022-06-30 |
| 5870 | 315 | 2500 | 2022-07-25 |
| 6642 | 654 | 3000 | 2022-07-27 |
Input Table:
| ar_id | customer_id | payment_made | billing_date |
|---|---|---|---|
| 5922 | 987 | 1500 | 2022-06-30 |
| 5439 | 315 | 1000 | 2022-07-28 |
| 8253 | 654 | 500 | 2022-06-30 |
| 7124 | 987 | 500 | 2022-07-31 |
| 9234 | 315 | 1000 | 2022-08-01 |
Example Output:
| customer_id | total_pending_payments |
|---|---|
| 987 | 1500 |
| 315 | 4000 |
| 654 | 2500 |
Answer:
You can use the following SQL query:
The query joins and tables on . It calculates the total for each customer and subtracts the total if any. The result is the total pending payment by each customer. If there are no payments made by the customer, the COALESCE function ensures that the sum of returns 0 instead of NULL.
Since join questions come up frequently during SQL interviews, try an interactive SQL join question from Spotify:
SQL Question 7: How do you identify records in one table that are not present in a second table?
To discover records in one table that are not present in another, you can utilize a and filter out any values in the right-side table.
For example, say you had a table of Wolfspeed customers and a 2nd table of all purchases made with Wolfspeed. To find all customers who did not make a purchase, you'd use the following
This query returns all rows from the customers table, along with any matching rows from the purchases table. If there is no matching row in the purchases table, values will be returned for all of the right table's columns. The clause then filters out any rows where the purchases.id column is , leaving only customers who have not made a purchase.
SQL Question 8: Calculate Applied Power and Efficiency Ratio
A Wolfspeed manufacturing plant is testing a batch of newly manufactured semiconductors. The semiconductors are subjected to two different stages of power – initial and final - and the calculations of power applied and the efficiency ratio of the semiconductors are to be found. The applied power is calculated by finding the absolute difference between the initial power and the final power, and the efficiency ratio is calculated by dividing the final power by the initial power (rounding the result to 2 decimal places). Calculate these data using SQL.
Consider the following input and output tables.
Example Input:
| semiconductor_id | initial_power | final_power |
|---|---|---|
| 1 | 20.0 | 25.5 |
| 2 | 18.5 | 22.3 |
| 3 | 26.1 | 21.5 |
| 4 | 20.2 | 23.1 |
| 5 | 23.8 | 20.5 |
Example Output:
| semiconductor_id | applied_power | efficiency_ratio |
|---|---|---|
| 1 | 5.5 | 1.28 |
| 2 | 3.8 | 1.21 |
| 3 | 4.6 | 0.82 |
| 4 | 2.9 | 1.14 |
| 5 | 3.3 | 0.86 |
Answer:
In the above query, we first determine the applied power by calculating the absolute difference between the initial power and final power using ABS(). The efficiency ratio is then calculated by dividing the final power by initial power. The ROUND() function is used to ensure the result is rounded to two decimal places.
To practice a very similar question try this interactive Amazon Server Utilization Time Question which is similar for calculating total values based on data attributes or this Tesla Unfinished Parts Question which is similar for extracting relevant data from a manufacturing process.
Wolfspeed SQL Interview Tips
The best way to prepare for a Wolfspeed SQL interview is to practice, practice, practice.
Beyond just solving the earlier Wolfspeed SQL interview questions, you should also solve the 200+ SQL exercises on DataLemur which come from companies like Facebook, Google and unicorn tech startups.
Each problem on DataLemur has hints to guide you, detailed solutions and crucially, there is an online SQL coding environment so you can right in the browser run your SQL query and have it graded.
To prep for the Wolfspeed SQL interview you can also be wise to practice interview questions from other tech companies like:
However, if your SQL foundations are weak, don't worry about diving straight into solving questions – refresh your SQL knowledge with this free SQL for Data Analytics course.
This tutorial covers SQL concepts such as HAVING and creating summary stats with GROUP BY – both of which show up routinely during SQL job interviews at Wolfspeed.
Wolfspeed Data Science Interview Tips
What Do Wolfspeed Data Science Interviews Cover?
In addition to SQL query questions, the other topics covered in the Wolfspeed Data Science Interview are:
- Statistics and Probability Questions
- Coding Questions in Python or R
- Product-Sense and Business Case Questions
- Machine Learning and Predictive Modeling Questions
- Behavioral Based Interview Questions
How To Prepare for Wolfspeed Data Science Interviews?
The best way to prepare for Wolfspeed Data Science interviews is by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from tech companies like Netflix, Google, & Airbnb
- A Crash Course on Python, SQL & ML
- Great Reviews (900+ reviews, 4.5-star rating)