(Ex-Facebook & Best-Selling Data Science Author)
February 6, 2024
Internally at Micron, SQL crucial for analyzing semiconductor manufacturing data and optimizing production processes. Externally, Micron products help speed up Microsoft SQL Server queries. That's why Micron asks SQL query interview questions during for Data Science, Analytics, and & Data Engineering job interviews.
In case you're preparing for a SQL Interview, here’s 8 Micron SQL interview questions to practice, which are similar to commonly asked questions at Micron – how many can you solve?
Given a table containing information about different Micron products that are sold, where each row represents one sale, write a SQL query to find the average quantity of each product sold per week. Consider the week starting from Monday and ending on Sunday.
The table has the following columns:
The script firstly converts the to weekly dates using . Then it applies the AVG window function partitioned by the and the week of . This calculates the average quantity of each product sold per week. The results are then ordered by the week date and product ID.
Pro Tip: Window functions are a frequent SQL interview topic, so practice all the window function problems on DataLemur
As a data analyst at Micron, you are asked to identify active customers who have made purchase orders within the last six months and whose expenditure exceeds $10,000. For this question, consider the database has two tables and .
Write a SQL query to return the and of these customers.
This SQL script first joins the and tables based on the . It then filters out the orders that took place more than six months ago. Lastly, it groups the remaining lines by and keeps only those customers where the total exceeds $10,000.
A database index is a way to optimize the performance of a database by reducing the amount of data that needs to be searched to retrieve a record.
There are several types of indexes that can be used in a database:
For a concrete example, say you had a table of Micron customer payments with the following columns: payment_id, customer_id, payment_amount, and payment_date.
Here's what a clustered index on the column would look like:
A clustered index on the column would determine the physical order of the records in the table based on the . This means that the records with the earliest values would be stored together physically in the table, followed by records with later payment_date values.
Having a clustered index on the column can speed up queries that filter or sort the data based on the payment_date, as the records are already physically sorted in the table. For example, if you want to retrieve all the payments made in the month of January, the database can use the clustered index to quickly locate and retrieve the desired records without having to search through the entire table.
As a data analyst at Micron Technology, your task is to find the total sales per product for the previous year. For each product, you need to calculate the total sales amount.
Consider the table where each entry represents a completed sale.
This SQL block groups the sales data by and calculates the total sales for each product by multiplying unit price and quantity. The WHERE clause limits the data to the entries from 2021. The results are then ordered by total sales in descending order.
Normalizing a database involves dividing a large table into smaller and more specialized ones, and establishing relationships between them using foreign keys. This reduces repetition, resulting in a database that is more adaptable, scalable, and easy to maintain. Additionally, it helps to preserve the accuracy of the data by reducing the likelihood of inconsistencies and problems.
Suppose that Micron is planning a sale event, during which they are going to apply a discount on some of their products. Each product's price will be reduced by the same percentage value. However, to avoid losses, Micron doesn't want any product's price to drop below a certain threshold. Write a SQL query that will return the maximum discount percentage possible without violating this rule. For extra complexity, round the result to two decimal points.
The price threshold is set to be 20 dollars.
In this SQL query, we first execute the subquery . This gives us the proportion of the minimum product price with respect to the price threshold. Then, we subtract this value from 1. The result is the maximum allowable reduction in price, as a proportion of the original price. We then multiply this number by 100 so it is expressed as a percentage, and finally we use to limit the result to two decimal places. We assign an alias of to this column to provide context for the returned value.
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for involving calculations based on product pricing> or this Wayfair Y-on-Y Growth Rate Question which is similar for calculating changes in pricing over time.
No, in 99% of normal cases a and do NOT produce the same result.
You can think of more as set addition when it combines the results of two tables.
You can think of a more as set multiplication, producing all combos of table 1 with combos of table 2.
Micron's manufacturing mainly deals with semiconductor products which require complex manufacturing processes. It's important to oversee the processing time each product takes due to numerous stages they go through. Write a SQL query which will average out the processing time taken by each product id on a monthly basis. We assume that processing time begins when a product enters production and ends when it is marked for shipment.
The above SQL query groups rows by the month of the and . It then calculates the average difference in days between and , giving the average time each product id spends in production within a specific month. undefined
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Micron SQL interview is to solve as many practice SQL interview questions as you can! Besides solving the above Micron SQL interview questions, you should also solve the 200+ DataLemur SQL Interview Questions which come from companies like Facebook, Google and unicorn tech startups.
Each exercise has hints to guide you, step-by-step solutions and best of all, there's an online SQL coding environment so you can easily right in the browser your SQL query and have it checked.
To prep for the Micron SQL interview you can also be useful to solve SQL problems from other tech companies like:
However, if your SQL skills are weak, don't worry about going right into solving questions – strengthen your SQL foundations with this SQL interview tutorial.
For the Micron Data Science Interview, beyond writing SQL queries, the other types of questions which are covered:
The best way to prepare for Micron Data Science interviews is by reading Ace the Data Science Interview. The book's got: