8 Silicon Motion SQL Interview Questions (Updated 2024)
At Silicon Motion, SQL is used often for analyzing and managing data from embedded storage and graphics products, and optimizing customer-specific firmware algorithms in the semiconductor industry. Unsurprisingly this is why Silicon Motion LOVES to ask SQL questions in interviews for Data Science and Data Engineering positions.
So, to help you prepare for the Silicon Motion SQL interview, we'll cover 8 Silicon Motion SQL interview questions – how many can you solve?
8 Silicon Motion SQL Interview Questions
SQL Question 1: Identify the Most Valuable Customers for Silicon Motion
Silicon Motion is a global leader in developing microcontroller ICs for NAND flash storage devices and specialty RF ICs for mobile devices. They are interested in identifying who their 'power users' or most valuable customers (MVC) are. A MVC is defined as a customer who purchases more than 5 products in a month. Write a SQL query to identify these MVCs.
Example Input:
| purchase_id | customer_id | purchase_date | product_id | quantity |
|---|---|---|---|---|
| 101 | 2001 | 2022-06-01 | 501 | 2 |
| 102 | 3022 | 2022-06-15 | 502 | 1 |
| 103 | 2001 | 2022-06-17 | 502 | 3 |
| 104 | 2001 | 2022-06-22 | 501 | 1 |
| 105 | 3022 | 2022-07-05 | 502 | 6 |
| 106 | 3022 | 2022-07-31 | 501 | 1 |
Example Output:
| month | customer_id | total_quantity |
|---|---|---|
| 6 | 2001 | 6 |
| 7 | 3022 | 7 |
Answer:
This query groups the data first by month and then by the customer. It aggregates the sum of quantities purchased per month per customer. The HAVING clause restricts the aggregation to show only those months' customer IDs where the total quantity purchased exceeds 5. The final result is sorted by month and total quantity in descending order. This would give us the power users for each month.
To practice a related customer analytics question on DataLemur's free online SQL coding environment, try this Microsoft Azure Cloud SQL Interview Question:
SQL Interview Question 2: Monthly Average Rating for Silicon Motion Products
Given a table of customer reviews that contains the ID of the review, the ID of the customer who submitted the review, the date the review was submitted, the ID of the product that was reviewed, and the star rating given (from 1-5), create a SQL query that will return a summary table listing the month, the product ID, and the average star rating for each product on a monthly basis.
Example Input:
| review_id | user_id | submit_date | product_id | stars |
|---|---|---|---|---|
| 1 | 101 | 2022-06-22 | 201 | 4 |
| 2 | 102 | 2022-06-23 | 201 | 5 |
| 3 | 103 | 2022-06-24 | 202 | 3 |
| 4 | 104 | 2022-07-01 | 202 | 2 |
| 5 | 105 | 2022-07-02 | 201 | 3 |
Example Output:
| month | product_id | avg_stars |
|---|---|---|
| June, 2022 | 201 | 4.5 |
| June, 2022 | 202 | 3.0 |
| July, 2022 | 201 | 3.0 |
| July, 2022 | 202 | 2.0 |
Answer:
This query averages the stars per product_id for each month. The function is used to convert the date into a string format that displays only the month and year of the review submission. The function calculates the average of star ratings for each product per month. The clause groups the data by month and product_id. The output is ordered by month and product_id to make it easy to see the average rating progression of each product over time.
Pro Tip: Window functions are a popular SQL interview topic, so practice every window function problem on DataLemur
SQL Question 3: What are the different normal forms (NF)?
Normalization is the process of organizing fields and tables of a database to minimize redundancy and dependency. While there are technically 5 levels (normal forms), the 3 most important normal forms you need to know about for SQL interviews at Silicon Motion are:
- First Normal Form (1NF): This should fix remove a table's duplicate columns. Also, each column should contain only a single value (no lists or containers of data), and finally each row of table should have a unique identifier as well.
- Second Normal Form (2NF): A table is in its second normal form if it meets all requirements of the first normal form and places the subsets of columns in separate tables. The relationships between tables are created using primary/foreign keys.
- Third Normal Form (3NF): The table should be in the second normal form. There should be no dependency on another non-key attribute (meaning a primary key should be the only thing required to identify the row).
SQL Question 4: Find the Average Number of Units Sold Per Product
As a data analyst working for Silicon Motion - a global leader in developing NAND flash controller ICs for solid state storage devices and specialty RF ICs, you have been tasked to find out the average unit sales of each product category.
This will help the company understand the category performance, which can drive strategic decisions in production and marketing.
Given the following sales data:
Example Input:
| sale_id | sale_date | product_category | units_sold |
|---|---|---|---|
| 5273 | 06/08/2022 | NAND flash controller | 150 |
| 6254 | 06/10/2022 | Specialty RF ICs | 95 |
| 3769 | 06/18/2022 | NAND flash controller | 175 |
| 6444 | 07/26/2022 | Specialty RF ICs | 120 |
| 4268 | 07/05/2022 | NAND flash controller | 200 |
Can you write a SQL query that calculates the average number of units sold per product category?
Answer:
The above SQL query selects data from the 'product_category' column and calculates the average of 'units_sold'. The GROUP BY statement groups the rows that have the same value in the 'product_category' column, then the AVG function calculates the average unit sales for each group.
Example Output could look something as below:
| product_category | Average_Unit_Sales |
|---|---|
| NAND flash controller | 175 |
| Specialty RF ICs | 107.5 |
In the output, we have the average units sold per product category. It shows that on average, the 'NAND flash controller' category has higher sales (175 units) than the 'Specialty RF ICs' category (107.5 units). This information can be useful in evaluating the performance of different product categories.
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for requiring category-wise data aggregation or this Wayfair Y-on-Y Growth Rate Question which is similar for focusing on sales data analysis.
SQL Question 5: In SQL, Are NULL values the same as a zero or blank space?
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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: Find The Most Popular Product
You're working as a data analyst in Silicon Motion. The sales team wants to identify the most purchased product in each month in order to prioritize their sales strategy. You have two tables: "Customers" which includes the customer's information and "Purchases" which includes each purchase transaction.
Table:
| customer_id | first_name | last_name | |
|---|---|---|---|
| 1 | John | Doe | johndoe@gmail.com |
| 2 | Jane | Smith | janesmith@gmail.com |
| 3 | Bob | Johnson | bob_johnson@gmail.com |
| 4 | Alice | Williams | alice_williams@gmail.com |
Table:
| purchase_id | customer_id | date | product_id |
|---|---|---|---|
| 100 | 1 | 02/05/2022 | 101 |
| 101 | 2 | 03/10/2022 | 102 |
| 102 | 2 | 04/01/2022 | 101 |
| 103 | 3 | 04/15/2022 | 102 |
| 104 | 1 | 05/20/2022 | 101 |
| 105 | 4 | 06/30/2022 | 103 |
Write a SQL query to identify the most purchased product in each month. If there is a tie, display all tied products.
Answer:
This PostgreSQL query returns the most purchased product id for each month, along with the count of sales. It first groups by month and product_id and counts the number of sales, then identifies the maximum sales for each month. Finally, it joins these results to get the product_id at the maximum sales for each month.
Since join questions come up frequently during SQL interviews, try this Spotify JOIN SQL question:
SQL Question 7: What are the similarities and differences between a clustered index and non-clustered index?
Here is an example of a clustered index on the column of a table of Silicon Motion customer transactions:
This creates a clustered index on the column, which will determine the physical order of the data rows in the table.
Here is an example of a non-clustered index on the column of the same table:
This will create a non-clustered index on the column, which will not affect the physical order of the data rows in the table.
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: Average Sales per Month
Silicon Motion is a global leader in supplying NAND flash controllers for solid state storage devices. With consumer demands increasing, the sales data needs to be accurately analyzed.
Given a table , which includes the sales made for the different product lines of the company for each quarter, calculate the average sales made for every product for each month. Consider each quarter to have exactly 3 months. The table is represented as:
Example Input:
| sales_id | product_line | quarter | year | sales |
|---|---|---|---|---|
| 101 | SMI_Ultra | 1 | 2021 | 30000 |
| 102 | SMI_Hyper | 1 | 2021 | 50000 |
| 103 | SMI_Ultra | 2 | 2021 | 65000 |
| 104 | SMI_Hyper | 2 | 2021 | 80000 |
| 105 | SMI_Hyper | 3 | 2021 | 50000 |
Example Output:
| month | product_line | avg_sales |
|---|---|---|
| 1 | SMI_Ultra | 10000 |
| 1 | SMI_Hyper | 16667 |
| 2 | SMI_Ultra | 21667 |
| 2 | SMI_Hyper | 26667 |
| 3 | SMI_Hyper | 16667 |
The column in the table has a value between 1 to 4 inclusive, which represents the quarter of the year when the sales were done. The column indicates the total sales made for the product line for that quarter of the year.
Write a PostgreSQL query to solve the problem.
Answer:
This query generates the average sales per month for each product line. The generate_series function creates a sequence of integers from 1 to 3, which simulates the 3 months in every quarter. We group by month and product_line to get the average sales for each product line per month. The sales are divided by 3 to derive the monthly average from the quarter sales. The result is sorted to present the output in order of months and product_lines.
Preparing For The Silicon Motion SQL Interview
The best way to prepare for a Silicon Motion SQL interview is to practice, practice, practice.
Beyond just solving the above Silicon Motion SQL interview questions, you should also solve the 200+ SQL coding questions which come from companies like Google, Facebook, Microsoft and Amazon.
Each exercise has multiple hints, full answers and best of all, there's an interactive SQL code editor so you can right online code up your SQL query answer and have it checked.
To prep for the Silicon Motion SQL interview you can also be useful to solve SQL questions from other tech companies like:
But if your SQL query skills are weak, forget about going right into solving questions – strengthen your SQL foundations with this free SQL tutorial.
This tutorial covers topics including sorting data with ORDER BY and SQL joins with practice exercises – both of which come up routinely during SQL job interviews at Silicon Motion.
Silicon Motion Data Science Interview Tips
What Do Silicon Motion Data Science Interviews Cover?
In addition to SQL query questions, the other types of problems covered in the Silicon Motion Data Science Interview are:
- Stats Interview Questions
- Coding Questions in Python or R
- Product-Sense Questions
- ML Interview Questions
- Behavioral & Resume-Based Questions
How To Prepare for Silicon Motion Data Science Interviews?
The best way to prepare for Silicon Motion Data Science interviews is by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from FAANG (FB, Apple, Amazon, Netflix, Google)
- A Crash Course covering SQL, Product-Sense & ML
- Amazing Reviews (900+ reviews, 4.5-star rating)