8 Microchip Technology SQL Interview Questions (Updated 2024)
At Microchip Technology, SQL is used across the company for analyzing semiconductor performance data and managing databases associated with microchip production automation. Unsurprisingly this is why Microchip Technology asks SQL coding questions during interviews for Data Analyst, Data Science, and BI jobs.
As such, to help you prepare for the Microchip Technology SQL interview, this blog covers 8 Microchip Technology SQL interview questions – how many can you solve?
8 Microchip Technology SQL Interview Questions
SQL Question 1: Microchip Sales Analytics
Assuming Microchip Technology Inc. tracks sales of their microchips and wants to know which microchip model is most popular among their customers for each month throughout a given year. Here is an appropriate SQL window function question for that scenario:
"Given a table, write a SQL query to find the most frequently sold microchip in each month of the year 2022. The sales table includes columns for , , and ."
Example Input:
| sale_id | customer_id | sale_date | microchip_id |
|---|---|---|---|
| 1234 | 1001 | 01/03/2022 | 69852 |
| 1235 | 1002 | 01/04/2022 | 69852 |
| 1236 | 1003 | 02/02/2022 | 50001 |
| 1237 | 1004 | 02/05/2022 | 69852 |
| 1238 | 1005 | 03/01/2022 | 50001 |
Expected Output:
| month | most_sold_microchip_id |
|---|---|
| 1 | 69852 |
| 2 | 69852 |
| 3 | 50001 |
Answer:
This query uses a window function to partition the sales data by month, then ranks each partition by the count of sales for each microchip. The subquery groups the data by and to get the total sales for each microchip each day, then the window function assigns a rank to these groups based on the count of sales, with the highest count having a rank of 1. The outer query selects the results from the subquery where the rank equals 1, i.e., the microchip with the most sales for each month.
To solve a similar window function question on DataLemur's free online SQL code editor, try this Google SQL Interview Question:
SQL Question 2: Filtering Customers Based on Order Status and Location
Assume you work as a data analyst in Microchip Technology, and you have been asked to extract customer data based on some conditions. Your manager wants to filter customer data where their last order status is 'Delivered', they belong to the 'Gold' membership, and they are located either in 'California' or 'New York'. The manager wants this data to target promotions particularly to these loyal customers.
You have two database tables, and with the following structure:
Example Input:
| customer_id | name | member_status | location |
|---|---|---|---|
| 101 | John Doe | Gold | California |
| 102 | Mary Johnson | Silver | New York |
| 103 | James Smith | Gold | Texas |
| 104 | Patricia Brown | Gold | California |
| 105 | Robert Davis | Bronze | California |
Example Input:
| order_id | customer_id | status | order_date |
|---|---|---|---|
| 1001 | 101 | Delivered | 01/01/2022 |
| 1002 | 102 | Cancelled | 01/01/2022 |
| 1003 | 103 | Delivered | 01/01/2022 |
| 1004 | 101 | In Transit | 02/01/2022 |
| 1005 | 104 | Delivered | 02/01/2022 |
Answer:
This query works by firstly filtering the 'orders' table according to the boolean condition of the latest ('Delivered') order for each customer. This subquery result is then joined with the 'customers' table to allow for further filtering according to the membership and location conditions. The final output is the details of customers who match all of the conditions.
SQL Question 3: When would you use denormalization?
Database denormalization is when you add redundancy to a database, and break typical normalization rules (specified by 1st, 2nd, 3rd normal forms). There's a few reasons to denormalize a database:
-
Improved performance: Denormalization often reduces the the number of costly join operations that are needed to retrieve data. This is helpful when the database is being used for OLAP (Online Analytical Processing) use cases, as joins can be expensive and slow.
-
Simplification: Denormalization can also be used to simplify the design of a database by reducing the number of tables and relationships that need to be managed. This can make it easier to understand and maintain the database.
-
Ease of use: Denormalization can also make it easier for users to work with a database by providing them with a more intuitive and straightforward data model.
While denormalization can be a useful tool for improving performance and scalability, it's important to keep in mind that it can make update and delete operations more complex. This is because denormalization can create data duplicates, which can make it harder to maintain data integrity. So, to avoid any headaches, it's a good idea to start with a well-normalized database design and then consider denormalization only if it's absolutely necessary for your specific performance and scalability needs. In other words, denormalization can be a bit of a wild card, so it's best to handle it with care!
SQL Question 4: Analyzing Click-Through Rates for Digital Ads Campaigns
Assume you are a data analyst at Microchip Technology. The company has decided to focus on their digital marketing efforts to boost their product sales. They have started a digital ad campaign to promote a new microcontroller across various platforms. Your task is to calculate the click-through rates on the ads on these platforms and determine which platform has the highest click-through rate.
Below are the tables with some sample data:
Example Input:
| ad_id | platform | view_date | user_id |
|---|---|---|---|
| 1001 | 06/01/2021 | 345 | |
| 1002 | 06/02/2021 | 567 | |
| 1003 | 06/03/2021 | 789 | |
| 1004 | 06/04/2021 | 234 | |
| 1005 | 06/05/2021 | 567 |
Example Input:
| ad_id | click_date | user_id |
|---|---|---|
| 1001 | 06/01/2021 | 345 |
| 1002 | 06/02/2021 | 567 |
| 1004 | 06/05/2021 | 234 |
Here's the PostgreSQL query to calculate the click through rates:
Answer:
This query first creates two views: one for the number of clicks per ad and another for the number of views per ad per platform. Then it joins on ad_id and calculates the click-through rate by dividing the number of clicks by the number of views and multiplying the result by 100 to get the rate in percentage form. The result is a list of platforms with their associated total clicks, views and click-through rates, sorted by click through rate in descending order.
To solve a similar problem about calculating rates, solve this TikTok SQL question on DataLemur's online SQL coding environment:
SQL Question 5: How can you select records without duplicates from a table?
The clause in SQL allows you to select records that are unique, eliminating duplicates.
For example, if you had a table of Microchip Technology employees, and wanted to see what are all the unique job titles that currently worked at the company, you could write the following query:
SQL Question 6: Find the total sales of each type of microchip
As an SQL programmer for the company "Microchip Technology", you have access to two databases – one labelled , which documented every sale of a microchip, including the date of sale, the type of chip (e.g. 'Microchip A'), the chip ID, and the customer ID. The second database named details the information of each customer (their ID, their name, and their location).
You were asked to provide a report that shows the total sales of each type of microchip broken down by customer location. The time period given is between January 1, 2021 and December 31, 2021.
Example Input:
| sale_date | chip_type | chip_id | customer_id |
|---|---|---|---|
| 01/08/2021 | Microchip A | 1572 | 123 |
| 03/04/2021 | Microchip B | 4862 | 265 |
| 07/28/2021 | Microchip A | 2534 | 362 |
| 12/29/2021 | Microchip C | 3762 | 192 |
| 11/16/2021 | Microchip A | 8431 | 981 |
Example Input:
| customer_id | customer_name | location |
|---|---|---|
| 123 | Daniel Smith | USA |
| 265 | Alice Johnson | UK |
| 362 | Charlie Brown | USA |
| 192 | Bob Williams | UK |
| 981 | Ella Davis | USA |
Example Output:
| chip_type | location | total_sales |
|---|---|---|
| Microchip A | USA | 2 |
| Microchip B | UK | 1 |
| Microchip C | UK | 1 |
| Microchip A | UK | 0 |
| Microchip B | USA | 0 |
| Microchip C | USA | 1 |
Answer:
This SQL command first joins the two databases on the shared variable . It looks only at sales made between January 1, 2021, and December 31, 2021. Then, it counts the total number of each chip sold, broken down by the location of the purchasers.
Because joins come up so often during SQL interviews, try this interactive Snapchat JOIN SQL interview question:
SQL Question 7: What are the similarities and differences between a clustered index and non-clustered index?
Clustered and non-clustered indexes are both used to improve query performance, but they differ in how they are implemented.
A clustered index determines the physical order of the data rows in a table, while a non-clustered index does not. As a result, a table can have only one clustered index, but it can have multiple non-clustered indexes.
In terms of query efficiency & performance, a clustered index is generally faster for SELECT queries (reads) but updates (writes) to a clustered index are slower, as they require the data rows to be physically rearranged.
SQL Question 8: Calculating Volume of Semiconductor Sales
Microchip Technology wants to analyze the volume of their semiconductor sales in the past year. Each semiconductor is a cuboid in shape and its dimensions (length, breadth and height) are stored in their database.
Ignoring the units, write an SQL query to calculate the total volume of semiconductors sold by each sales representative in the last financial year. Round this volume to a full number and order the result by representative's id in ascending order.
For simplicity, imagine we have only two tables:
- - storing information about each sale
- - storing information about each semiconductor
Example Input:
| sales_id | representative_id | semiconductor_id | sale_date |
|---|---|---|---|
| 1 | 1 | 1 | 2021-07-15 |
| 2 | 1 | 2 | 2021-08-10 |
| 3 | 2 | 1 | 2021-06-05 |
| 4 | 3 | 3 | 2021-04-22 |
| 5 | 1 | 1 | 2021-11-11 |
Example Input:
| semiconductor_id | length | breadth | height |
|---|---|---|---|
| 1 | 2 | 2 | 3 |
| 2 | 3 | 4 | 5 |
| 3 | 2 | 2 | 5 |
Answer:
This query calculates the product of length, breadth, and height (which gives the volume of the semiconductor) for each sale. The ROUND function is used to round off the total volume to the nearest whole number. The sum of such volumes sold by each representative is calculated and grouped by representative_id. The final result is sorted by representative_id in ascending order.
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for data aggregation by categories or this Amazon Maximize Prime Item Inventory Question which is similar for calculation involving product dimensions.
Preparing For The Microchip Technology SQL Interview
The best way to prepare for a SQL interview, besides making sure you have strong SQL fundamentals, is to practice a ton of real SQL questions that were asked in recent job interviews.
Besides solving the above Microchip Technology SQL interview questions, you should also solve the 200+ SQL coding questions which come from companies like Amazon, Microsoft, Meta, and smaller tech companies.
Each interview question has hints to guide you, full answers and best of all, there's an interactive SQL code editor so you can easily right in the browser your SQL query and have it graded.
To prep for the Microchip Technology SQL interview it is also useful to solve SQL problems from other semiconductor companies like:
In case your SQL skills are weak, forget about jumping right into solving questions – improve your SQL foundations with this SQL tutorial for Data Scientists & Analysts.
This tutorial covers topics including SUM/AVG window functions and filtering groups with HAVING – both of these pop up frequently in Microchip Technology SQL assessments.
Microchip Technology Data Science Interview Tips
What Do Microchip Technology Data Science Interviews Cover?
In addition to SQL interview questions, the other topics to prepare for the Microchip Technology Data Science Interview are:
- Probability & Statistics Questions
- Python or R Programming Questions
- Product Data Science Interview Questions
- ML Interview Questions
- Behavioral Based Interview Questions
How To Prepare for Microchip Technology Data Science Interviews?
The best way to prepare for Microchip Technology Data Science interviews is by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from tech companies like Google & Microsoft
- A Refresher on Python, SQL & ML
- Great Reviews (900+ reviews, 4.5-star rating)