(Ex-Facebook & Best-Selling Data Science Author)
February 5, 2024
SK Hynix was the FIRST Korean manufacturing company to establish a Data Science division. Their team often used SQL for analyzing manufacturing data to improve semiconductor chip production, which is why SK Hynix LOVES to ask SQL questions in interviews for Data Science, Analytics, and & Data Engineering jobs.
So, in case you're stressed about an upcoming SQL Assessment, we've collected 9 SK Hynix SQL interview questions to practice, which are similar to recently asked questions at SK Hynix – able to answer them all?
SK Hynix, a renowned provider of semiconductor products and solutions, wants to identify their power users. A power user is defined as a customer who has bought a significant amount of products in terms of total price within the last quarter. Your task is to write a SQL query that returns the and total purchase amount for each user, sorted in descending order based on the purchase amount, and limit the result to top 10 customers.
Assume you have access to a table named with the following structure and sample data:
In this table, is single product price and is the number of products a user bought in that particular purchase. So total price of a purchase will be ( * ).
The SQL command will be:
With this query, we first extract the purchases that took place within the last quarter (from July 2022 to end of September 2022). Then, for each user, we calculate the total purchase amount. Finally, we order the result in descending order and limit it to the top 10, which gives us the 10 power users we want to identify. The gives us the total amount of money that the user has spent on purchases in the defined period.
To practice a related customer analytics question on DataLemur's free online SQL coding environment, try this recently asked Microsoft SQL interview question:
Given a dataset that records the sales information of SK Hynix's different product types, please write a SQL query that calculates the average monthly sales and year-over-year sales growth for each product type.
Assume you have the following table:
Define the sales in each month as the total quantity sold of a product times the price of the product.
Calculate the monthly sales for each product type first, and then the annual growth rate. The year-over-year sales growth rate can be calculated with the formula: (Sales in the current month of this year - Sales in the same month of last year)/Sales in the same month of last year.
Please use PostgreSQL syntax for this task.
The window function is used in this query to obtain data from the previous row (in terms of time) when computing the rate of growth. The query first computes a sub-table of monthly sales, and then uses the LAG window function to calculate the YoY growth by each product type. It groups by to ensure that growth rates are calculated separately for each type.
The function is used to get YEAR and MONTH from the timestamp, and the statement handles null values that may arise for the first record of each product type.
p.s. Window functions show up super frequently during SQL interviews, so practice the 27+ window function questions on DataLemur
Views are a lot like virtual tables, where you can take a base table and customize it (such as by hiding some data from non-admin users, or removing some random columns/rows based on business requirements).
Here's the PostgreSQL syntax for creating a view based on data in the table:
The company, SK Hynix, is a major global semiconductor manufacturing company, and it is important for them to efficiently manage their inventory. Create a database design that captures details of the products, their respective sales, and the current inventory.
Assume the following tables exist:
|"RAM Module Type 1"
|"RAM Module Type 2"
|"Microprocessor Type 1"
Your task is to write a query that gives the product details and quantity sold per product for a given date, if the stock left for any product goes below 100.
This query retrieves the product details of those items which have less than 100 items left in the inventory after a day's sales. Change 'YOUR_INPUT_DATE' to the date for which you want to check the inventory status. The date should be a string in the format 'YYYY-MM-DD'. The product details and quantity sold are collected from the products and sales tables respectively before being integrated with the inventory table based on the product_id. The final query checks those entries where the stock remaining after the title's sales is less than 100. undefined
While knowing this answer is beyond the scope of most Data Analyst & Data Science interviews, Data Engineers at SK Hynix should be at least aware of SQL vs. NoSQL databases.
Relational databases and non-relational (NoSQL) databases have some key differences, particularly in terms of how data is stored. Whereas relational databases have tables, made up of rows and columns, NoSQL databases use various data models like:
This added flexibility makes NoSQL databases well-suited for handling non-tabular data or data with a constantly changing format. However, this flexibility comes at the cost of ACID compliance, which is a set of properties (atomic, consistent, isolated, and durable) that ensure the reliability and integrity of data in a database. While most relational databases are ACID-compliant, NoSQL databases may not provide the same level of guarantees. Sure, here's how an interview question on click-through-rates could be formatted.
SK Hynix, a global semiconductor company, is interested in evaluating the effectiveness of its digital advertising campaigns. They want to know the click-through rate (CTR), which is the percentage of users who view a digital advertisement (impressions) and then go on to click the advertisement (clicks). Assume we have two tables, and .
The table keeps records of every time a digital advertisement is viewed by a user:
The table logs every time a user clicks on a digital advertisement:
Write a PostgreSQL query to compute the click-through rate for each advertisement.
Here's how you could write the SQL to solve this:
This query first joins the and tables on , , and makes sure that the is on or after the . It then groups by to get the count of impressions and clicks for each advertisement. Finally, it calculates the CTR by dividing the number of clicks by impressions for each advertisement and multiplying the result by 100 to get the percent. Please note that NULL click_ids from the LEFT JOIN are not counted in the COUNT(c.click_id), thus achieving the desired result.
To practice a similar problem on DataLemur's free online SQL code editor, try this SQL interview question asked by Facebook:
Clustered indexes have a special characteristic in that the order of the rows in the database corresponds to the order of the rows in the index. This is why a table can only have one clustered index, but it can have multiple non-clustered indexes.
The main difference between clustered and non-clustered indexes is that the database tries to maintain the order of the data in the database to match the order of the corresponding keys in the clustered index. This can improve query performance as it provides a linear-access path to the data stored in the database.
As a database administrator for SK Hynix, you are asked to retrieve information about certain customers who have a high order rate in the past month. The specifics are as follows:
Find all customers whose names start with 'A' and have made more than 5 orders in the past month.
This query begins by retrieving all the customers from the customers table whose name begins with 'A'. It then counts the number of orders placed by those customers in the past month. The HAVING clause at the end of the query is used to filter out the customers who have placed more than 5 orders. undefined
SK Hynix, being a leading manufacturer of memory modules, needs to monitor its production data regularly. As an SQL developer, your task is to provide a report on the most produced memory module for each month. Use the to identify the products.
Structure of table is given below:
The output should contain the month (), the with maximum production for each month, and the count of the units produced ().
The output table structure should look like below:
You can achieve this by using the function to group by month, and the function to select the top product for each month.
This query returns the with the highest production for each month (identified by ), along with the month and the count of units produced. undefined
The key to acing a SK Hynix SQL interview is to practice, practice, and then practice some more! Beyond just solving the earlier SK Hynix SQL interview questions, you should also solve the 200+ SQL questions from real Data Science & Analytics interviews which come from companies like Netflix, Airbnb, and Amazon.
Each SQL question has hints to guide you, step-by-step solutions and best of all, there is an online SQL code editor so you can right online code up your SQL query answer and have it executed.
To prep for the SK Hynix SQL interview it is also helpful to solve SQL problems from other tech companies like:
In case your SQL query skills are weak, forget about jumping right into solving questions – go learn SQL with this DataLemur SQL tutorial.
For the SK Hynix Data Science Interview, in addition to SQL query questions, the other types of questions which are covered:
To prepare for SK Hynix Data Science interviews read the book Ace the Data Science Interview because it's got: