11 Renesas Electronics SQL Interview Questions (Updated 2024)

At Renesas Electronics, SQL is used across the company for analyzing semiconductor data for quality control purposes and managing the vast inventory database of electronic components. That's why Renesas Electronics almost always evaluates jobseekers on SQL query questions during interviews for Data Analytics, Data Science, and Data Engineering jobs.

As such, to help you prepare for the Renesas Electronics SQL interview, we've curated 11 Renesas Electronics SQL interview questions – can you solve them?

11 Renesas Electronics SQL Interview Questions

SQL Question 1: Identify Renesas Electronics' Top Customers based on Item Purchase Frequency

As a data analyst at Renesas Electronics, you have been given a task to identify the power customers—those who frequently purchase high quantities of electronics. These customers are critical for the company's business growth. Write a PostgreSQL query to find the top 5 customers with maximum purchases in the last 6 months based on transaction data.

Example Input:

Example Input:

Answer:

This query would join and tables on the field. The clause filters the transactions for the last 6 months by subtracting 6 months from the current date and truncating it to month. The sum of is calculated for each user and they are then sorted in descending order to bring the top customers first. The output would be shown only for the top 5 customers with most purchases.

To work on a similar customer analytics SQL question where you can solve it right in the browser and have your SQL query automatically checked, try this Walmart Labs SQL Interview Question:

SQL Question 2: Average Monthly Sale of Each Product

Renesas Electronics, being a premier supplier of advanced semiconductor solutions, tracks all of its sales. Suppose you are provided with a dataset where each row represents a sale of a product. The dataset has five columns: 'sale_id', 'sale_date', 'product_id', 'quantities', and 'total_price'.

Write a SQL query to obtain the average monthly sale (in terms of total price) of each product.

Example Input:

Example Output:

Answer:

In this PostgreSQL query, we first use the function to get the month and year from the 'sale_date' column for each record. Then, the statement groups the records by month, year, and product_id. The function calculates the average total price for each group, giving us the average monthly sale for each product. Finally, the query sorts the result by year, month and product_id.

To practice a similar window function question on DataLemur's free online SQL code editor, solve this Google SQL Interview Question:

SQL Question 3: Can you explain the difference between the and window functions in SQL?

In SQL, both and are used to rank rows within a result set. The key difference between the two functions is how deal with two or more rows having the same value in the ranked column (aka how the break 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 second row, and a rank of 4 to the third row.

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.

SQL Question 4: Database Design for Renesas Electronics Product Tracking

As a Renesas Electronics database manager, you have been tasked with designing a relational database to track production output, including product types and their quantities, and understanding the production trends. The company produces a variety of microcontrollers and semiconductors. The manufacturing process requires tracking the various stages a product goes through from initiation to completion.

The required tables are , , with the following data:

Example Input:

Example Input:

Example Input:

SQL Question:

Write a PostgreSQL query that will join these tables and return a list of products, their current stage, and how long they have been in that stage.

Answer:

This SQL query does the following:

• Joins the , , and tables using the and for references
• Filters the records by using a WHERE clause to include only the records where the stage is NULL, implying the current stage
• Returns the product name, stage name, and the number of days since the product entered the current stage.

SQL Question 5: What is denormalization, and in what situations might it be a useful?

Imagine you've got giant AF jigsaw puzzle with thousands of pieces, and each piece represents a piece of data in your database. You've spent hours organizing the puzzle into neat little piles, with each pile representing a table in your database. This process is called normalization, and it's a great way to make your database efficient, flexible, and easy to maintain.

But what if you want to solve the puzzle faster (aka make your queries run faster?)?

That's where denormalization comes in – Denormalization is like the puzzle-solving equivalent of taking a shortcut!

Instead of putting all the pieces in separate piles, you might decide to clone some of the pieces, and then have that one puzzle piece be put into multiple piles. Clearly, we are breaking the rules of physics, but that's just like de-normalization because it breaks the normal rules of normalization (1st, 2nd, 3rd normal forms).

By adding redundant puzzle pieces, it can be easier to find the pieces you need, but it also means that you have to be extra careful when you're moving pieces around or adding new ones (aka INSERT/UPDATE commands become more complex).

On the plus side, denormalization can improve the performance of your database and make it easier to use. On the downside, it can make your database more prone to errors and inconsistencies, and it can be harder to update and maintain. In short, denormalization can be a helpful tool, but it's important to use it wisely!

SQL Question 6: Filter Customers Based On Specific Requirements

Renesas Electronics, a top-tier semiconductor company with a prominent customer base, wants to filter down their customer records for targeted advertisements. They need to identify all customers living in California, USA, who have purchased at least once in the past 6 months, and whose overall purchase value is more than $100000. Assume we have the following tables:

Example Input:

Example Input:

Write an SQL query that retrieves the customer_id, first_name, and last_name of these customers.

Answer:

This query first aggregates the purchase value for each customer from the table, who have purchased within the past 6 months. Then, it joins this result with the table based on . Finally, it filters out the customers who live in California, USA, and whose total purchase value is more than $100000.

SQL Question 7: What is a SQL constraint?

A UNIQUE constraint ensures that all values in a column are different. This provides uniqueness for the column(s) and helps identify each row uniquely. Unlike primary key, there can be multiple unique constraints defined per table.

SQL Question 8: Calculating Average Quantity of Products Ordered

As an SQL analyst for Renesas Electronics, you are tasked with calculating the average quantity of each type of electronic component ordered from the warehouse daily. This will allow the company to understand how often different components need to be restocked.

Example Input:

Example Output:

Answer:

This SQL query groups the data by date and product_id and calculates the average quantity per day for each product by using the AVG function. It's important to group by both date and product_id to get daily averages for each product.

To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for calculating top items per category or this Alibaba Compressed Mean Question which is similar for calculating average per day.

SQL Question 9: Calculate Click-Through-Rates for Product Ads

Renesas Electronics, a semiconductor manufacturer, uses digital ads to showcase their products and wants to analyze their effectiveness. In this exercise, we'll analyze the click-through rates of their digital ads.

The click-through rate (CTR) is defined as the number of click-throughs divided by the number of views (or impressions).

We are given two tables:

• The table, which records every time an ad is shown to a user.
• The table, which records every time a user clicks on an ad.

Let's define the tables as follows:

Example Input:

Example Input:

Please write a PostgreSQL query to calculate the click-through rate by product for June 2022. The output should include the product_id and its corresponding CTR.

Answer:

With the two tables defined above, we can join them based on the to calculate the total views and clicks for each product. Then we calculate the CTR by dividing the number of clicks by the number of views, and output the result in a descending order by CTR.

This query first joins the and tables on the . It then restricts the data to ads/clicks that occurred in June 2022. The clause groups the data by , and calculates the click-through rate for each product by dividing the count of clicks () by the count of views (). The query then orders the result so that the product with the highest CTR appears first.

To solve a similar SQL problem on DataLemur's free interactive SQL code editor, solve this Facebook SQL Interview question:

SQL Question 10: What are the similarities and differences between correleated and non-correlated sub-queries?

hile a correlated subquery relies on columns in the main query's FROM clause and cannot function independently, a non-correlated subquery operates as a standalone query and its results are integrated into the main query.

An example correlated sub-query:

This correlated subquery retrieves the names and salaries of Renesas Electronics employees who make more than the average salary for their department. The subquery references the department column in the main query's FROM clause (e1.department) and uses it to filter the rows of the subquery's FROM clause (e2.department).

An example non-correlated sub-query:

This non-correlated subquery retrieves the names and salaries of Renesas Electronics employees who make more than the average salary for the Data Science department (which honestly should be very few people since Data Scientists are awesome and deserve to be paid well).The subquery is considered independent of the main query can stand alone. Its output (the average salary for the Data Science department) is then used in the main query to filter the rows of the Renesas Electronics employees table.

SQL Question 11: Find the Total Sales of Each Product per Month

Given a table which records every sale that Renesas Electronics has made, could you write a query that gives us the total number of sales for each product on a monthly basis?

Example Input:

Example Output:

Answer:

The following PostgreSQL query can provide us the desired output:

What this query does is it first extracts the month from the column and includes it as a "month" in the SELECT clause. It then groups the records in the sales table by both month and product_id - ergo, we get a reading of unit sales on a per product per month basis. Additionally, it orders the result set by both the and in ascending order to make digesting the output easier.

Preparing For The Renesas Electronics SQL Interview

Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Renesas Electronics SQL interview is to solve as many practice SQL interview questions as you can! In addition to solving the above Renesas Electronics SQL interview questions, you should also solve the 200+ SQL coding questions which come from companies like FAANG tech companies and tech startups.

Each problem on DataLemur has multiple hints, fully explained answers along with a discussion board to see how others solved it and crucially, there's an interactive SQL code editor so you can easily right in the browser your query and have it graded.

To prep for the Renesas Electronics SQL interview you can also be useful to practice SQL questions from other semiconductor companies like:

• Infineon SQL questions

• NXP Semiconductors SQL questions

• STMicroelectronics SQL questions

• ON Semiconductor SQL questions

But if your SQL coding skills are weak, don't worry about diving straight into solving questions – strengthen your SQL foundations with this SQL tutorial for Data Scientists & Analysts.

This tutorial covers SQL concepts such as handling NULLs in SQL and filtering data with WHERE – both of which show up routinely during Renesas Electronics SQL assessments.

Renesas Electronics Data Science Interview Tips

What Do Renesas Electronics Data Science Interviews Cover?

Besides SQL interview questions, the other types of questions to practice for the Renesas Electronics Data Science Interview are:

• Probability & Stats Questions
• Python Pandas or R Coding Questions
• Open-Ended Data Case Studies
• ML Interview Questions
• Behavioral Based Interview Questions

How To Prepare for Renesas Electronics Data Science Interviews?

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

• 201 Interview Questions from FAANG, tech startups, and Wall Street
• A Crash Course on SQL, Product-Sense & ML
• Great Reviews (900+ reviews, 4.5-star rating)