10 Keyence SQL Interview Questions (Updated 2024)

At Keyence, SQL is used often for analyzing complex manufacturing data, creating data-driven solutions for automation products. They also have products that can take manufacturing data and connect to the RDBMS of your choice. Unsurprisingly this is why Keyence frequently asks SQL coding questions in interviews for Data Analytics, Data Science, and Data Engineering jobs.

In case you're preparing for a SQL Assessment, we've curated 10 Keyence SQL interview questions to practice, which are similar to commonly asked questions at Keyence – can you solve them?

10 Keyence SQL Interview Questions

SQL Question 1: Identify Keyence's Power Users

Given a database of Keyence's orders and users, write a SQL query to identify power users as defined by the company. Power users are the users who have made large purchases (over $1,000) for more than 10 times in the past 6 months.

Example Input:

Example Input:

Answer:

This SQL PostgreSQL query joins the users table with a subquery of the orders table. The subquery finds all users who made an order of over $1,000 more than 10 times in the past 6 months. The outer query then selects these users' emails from the users table.

To practice a super-customer analysis question on DataLemur's free interactive coding environment, try this Microsoft Azure Cloud SQL Interview Question:

SQL Question 2: Calculate the Monthly Average Review Star Rating

Keyence, a leading supplier of sensors, measuring systems, laser markers, microscopes, and machine vision systems worldwide, often receives customer reviews on its various products. The manager wants to assess the average monthly review star rating for each product. The range of stars can be from 1 (worst) to 5 (best).

Here is the review table that records product reviews given by users on a specific date.

Table:

We want to construct another table which shows the average star rating for each product per month.

Answer:

This query first extracts the month from the using the function and then groups the reviews by month and . The average star rating per month, per product is then calculated using the function. The resulting table is ordered by and .

p.s. Window functions show up super often during SQL interviews, so practice the 27+ window function questions on DataLemur

SQL Question 3: What sets a cross join apart from a natural join?

A cross join is a JOIN operation in SQL that creates a new table by pairing each row from the first table with every row from the second table. It is also referred to as a cartesian join. In contrast, a natural join combines rows from two or more tables based on their common columns, forming a new table. Natural joins are called "natural" because they rely on the natural relationship between the common columns in the joined tables.

Here's an example of a cross join:

Here's a natural join example using two tables, Keyence employees and Keyence managers:

This natural join returns all rows from Keyence employees where there is no matching row in managers based on the column.

One significant difference between cross joins and natural joins is that the former do not require common columns between the tables being joined, while the latter do. Another distinction is that cross joins can generate very large tables if the input tables have a large number of rows, while natural joins only produce a table with the number of rows equal to the number of matching rows in the input tables.

SQL Question 4: Product Sales Analysis

As a data scientist at Keyence, you're tasked with analyzing sales data for each product. Your objective is to provide an overview of the total sales, total quantity sold and average price per product for each year.

Keyence's sales data is stored across two tables -- and . Here is the structure and some sample data for both tables:

Example Input:

Example Input:

Your task is to write a PostgreSQL query that returns the product_id, year of sale, total sale, total quantity, and average price per product for that year.

Answer:

This query first joins the and tables using their common column. It then groups the data by (extracted from ) and . The total sales, total quantity, and average price per product per year are calculated using the aggregate functions and . The results are then ordered by and in a descending order. undefined

SQL Question 5: How does the RANK() window function differ from DENSE_RANK()?

While both and are used to rank rows, the key difference is in how they deal with ties.

RANK(): When there's a tie, leaves a gap in the ranking. For example, if three rows are tied for 2nd place, the RANK() function will assign a rank of 2 to the first of these rows, a rank of 3 to the 2nd row in the tie, and a rank of 4 to the the 3rd tie.

DENSE_RANK(): For ties, does not leave a gap in the ranking. Instead, it assigns the same rank to all tied rows, and then makes the next row 1 bigger. Confusing, I know, but here's an example to make it more clear: if three rows are tied for 3rd place, the function will assign a rank of 3 to all three rows, and then assign a rank of 4 to the next row.

Suppose we had data on how many deals different salespeople at Keyence closed, and wanted to rank the salespeople.

The result of this query would be:

As you can see, the function assigns a rank of 1 to the first row (Akash), a rank of 2 to the second row (Brittany), and so on. In contrast, the , gives both Akash and Brittany a rank of 1, since they are tied, then moves on to rank 2 for Carlos.

SQL Question 6: Average Sales of Products by Category

As an analyst at Keyence, a manufacturer of automation sensors, barcode readers and laser markers, among others, you were asked to find the average sales of all products per category for the last quarter.

We have two tables - 'products' and 'sales'. The 'products' table has all the product details including 'product_id', 'product_name', and 'product_category'. The 'sales' table contain all the sales transactions including 'transaction_date', 'product_id', and 'sale_price'.

Example Input:

Example Input:

Example Output:

Answer:

This query joins the 'sales' and 'products' tables on 'product_id' and then it filters the sales transactions to only include the last quarter. It then uses the GROUP BY clause to group the results by 'product_category' and the AVG function to calculate the average 'sale_price' of the products in each category.

To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for analyzing product sales by category or this Wayfair Y-on-Y Growth Rate Question which is similar for making sales comparisons over time.

SQL Question 7: Can you explain the meaning of database denormalization?

Denormalization is the practice of altering a database schema in a way that breaks the normalization rules (1NF, 2NF, 3NF, etc.).

This is typically done to improve the performance of the database when it is being used for online analytics processing (OLAP), rather than online transaction processing (OLTP) use cases.

Denormalization can improve query performance by reducing the number of expensive joins required to retrieve data, but it comes with it's own drawbacks too. By adding redundant columns, you incur more data storage costs, and there's need for more complex update and delete operations in order to maintain data integrity across all the duplicated data. Thus, it's important to carefully consider the trade-offs involved before implementing denormalization.

SQL Question 8: Calculating Click-Through Rate (CTR)

Keyence is considering the optimization of its digital marketing campaign. The team has recorded every time a user viewed an ad and clicked on it. The data is stored in two tables:

• The table records all instances where the ad was viewed.
• The table records all instances when a user clicked the ad.

Calculate the overall click-through rate (CTR) per product. Use the following tables to create your query.

Example Input:

Example Input:

Answer:

You can use the clause to match the and fields from these two tables. can be used in both tables to count unique users. Finally, divide the number of distinct users who clicked by the number of distinct users who viewed to get the overall CTR.

This answer calculates the CTR by dividing the number of unique users who clicked the ad by the number of unique users who viewed the ad. This number is then grouped by the product id to give a clear indication of the CTR per product.

To practice a similar problem about calculating rates, solve this TikTok SQL Interview Question on DataLemur's interactive coding environment:

SQL Question 9: Find the product with the maximum total sales in every quarter

As a Data Analyst at Keyence, a supplier of automation sensors, industrial cameras and measurement systems, you are tasked to identify which product has the maximum total sales in every quarter for the previous year. You have access to the table that includes the columns , , and .

Example Input:

Answer:

This query first creates a sub-query that groups sales by quarter and product, calculating the total sales for each grouping. Then, for each quarter, it identifies the product_id and the amount of sales it had if it had the maximum sales for that quarter. The result is a list of each quarter from the year 2021, the product that had the maximum total sales, and the amount of those sales. undefined

SQL Question 10: How do foreign and primary keys differ?

To clarify the distinction between a primary key and a foreign key, let's examine employee data from Keyence's HR database:

:

+------------+------------+------------+------------+ | employee_id| first_name | last_name | manager_id | +------------+------------+------------+------------+ | 1 | Aubrey | Graham | 3 | | 2 | Marshal | Mathers | 3 | | 3 | Dwayne | Carter | 4 | | 4 | Shawn | Carter | | +------------+------------+------------+------------+

In this table, serves as the primary key. It uniquely identifies each employee and cannot be null.

functions as a foreign key, linking to the of the employee's manager. This establishes a relationship between Keyence employees and their managers, allowing for easy querying to find an employee's manager or see which employees report to a specific manager.

The table may also have multiple foreign keys that reference primary keys in other tables. For example, and foreign keys could be used to connect each employee to their respective department and location.

Keyence SQL Interview Tips

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. Beyond just solving the above Keyence SQL interview questions, you should also solve the 200+ DataLemur SQL Interview Questions which come from companies like FAANG tech companies and tech startups.

Each interview question has multiple hints, step-by-step solutions and most importantly, there is an online SQL coding environment so you can easily right in the browser your query and have it executed.

To prep for the Keyence SQL interview you can also be wise to practice SQL problems from other tech companies like:

• UIPath SQL questions

• Omron SQL questions

• Schneider Electric SQL questions

• Rockwell Automation SQL questions

In case your SQL foundations are weak, forget about jumping right into solving questions – refresh your SQL knowledge with this free SQL for Data Analytics course.

This tutorial covers topics including math functions in SQL and 4 types of JOINS – both of which pop up routinely during SQL job interviews at Keyence.

Keyence Data Science Interview Tips

What Do Keyence Data Science Interviews Cover?

For the Keyence Data Science Interview, besides SQL questions, the other types of questions which are covered:

• Statistics and Probability Questions
• Python or R Coding Questions
• Product Analytics Questions
• Machine Learning and Predictive Modeling Questions
• Behavioral Based Interview Questions

How To Prepare for Keyence Data Science Interviews?

The best way to prepare for Keyence Data Science interviews is by reading Ace the Data Science Interview. The book's got:

• 201 Interview Questions from Facebook, Google & startups
• A Refresher on Python, SQL & ML
• Great Reviews (900+ reviews, 4.5-star rating)