8 Murata Manufacturing SQL Interview Questions (Updated 2024)
At Murata Manufacturing, SQL is used often for analyzing production data to enhance efficiency and querying sensor data for predictive maintenance of machinery. Unsurprisingly this is why Murata Manufacturing almost always evaluates jobseekers on SQL coding questions in interviews for Data Science, Data Engineering and Data Analytics jobs.
So, to help you practice for the Murata Manufacturing SQL interview, we've collected 8 Murata Manufacturing SQL interview questions – can you solve them?
8 Murata Manufacturing SQL Interview Questions
SQL Question 1: Identify High-Value Customers in Murata Manufacturing
Murata Manufacturing is interested in identifying high-value customers for their business. A high-value customer is defined as one who has placed more than 10 orders, and the total amount of their orders exceeds $25,000 within the last year.
Construct a SQL query to identify these "whale" customers from the Customers and Orders tables provided below.
Sample Input:
| customer_id | first_name | last_name | sign_up_date |
|---|---|---|---|
| 9001 | Peter | Parker | 01/16/2021 |
| 9002 | Bruce | Wayne | 02/20/2021 |
| 9003 | Clark | Kent | 03/05/2021 |
| 9004 | Tony | Stark | 07/04/2021 |
Sample Input:
| order_id | customer_id | order_date | amount |
|---|---|---|---|
| 5101 | 9001 | 04/08/2021 | $4,000 |
| 5102 | 9002 | 04/15/2021 | $1,500 |
| 5293 | 9003 | 02/03/2022 | $30,000 |
| 6352 | 9002 | 11/20/2021 | $10,000 |
| 4518 | 9001 | 07/05/2022 | $25,000 |
Answer:
In this query, we are joining the and tables on . We filter out any orders that are more than a year old. We then group the results by and and , and calculate the count of orders and the total amount per customer. The clause is used to filter out customers who have placed more than 10 orders and whose total order amount exceeds $25,000 in the past year.
To work on another SQL customer analytics question where you can code right in the browser and have your SQL code instantly graded, try this Walmart Labs SQL Interview Question:
SQL Question 2: Analysis of Production Quality Results
Assuming you work at Murata Manufacturing, an international electronic components enterprise, one of your responsibilities could be the analysis of production quality results. Murata conducts complete quality checks after different stages of its production process. The test results are recorded in a dataset containing the component id, component type, test stage, test date and test result (Passed/Failed).
You are asked to write a SQL query that calculates the running total of Failed results per component type, per month, and orders the results by month.
Example Input:
| component_id | component_type | test_stage | test_date | test_result |
|---|---|---|---|---|
| A101 | Resistor | After Assembly | 2022-01-02 | Passed |
| B201 | Capacitor | Mid-Production | 2022-01-05 | Failed |
| A102 | Resistor | Final Inspection | 2022-01-06 | Failed |
| C301 | Inductor | After Assembly | 2022-02-02 | Passed |
| B202 | Capacitor | Final Inspection | 2022-02-04 | Failed |
| B203 | Capacitor | Final Inspection | 2022-02-05 | Failed |
Example Output:
| month | component_type | cumulative_fails |
|---|---|---|
| 2022-01 | Capacitor | 1 |
| 2022-01 | Resistor | 1 |
| 2022-02 | Capacitor | 3 |
Answer:
This query creates a CTE that counts the failures per month and per component type. The main query then calculates a running total of failures using a window function, partitioned by component type and ordered by month. The result provides a cumulative count of test failures for each component type over the months. This allows insight into the components that are giving the most issues in manufacturing, aiding in quality control measures.
To solve a related window function SQL problem on DataLemur's free online SQL coding environment, solve this Google SQL Interview Question:
SQL Question 3: Can you explain what SQL constraints are, and why they are useful?
Constraints are just rules your DBMS has to follow when updating/inserting/deleting data.
Say you had a table of Murata Manufacturing products and a table of Murata Manufacturing customers. Here's some example SQL constraints you'd use:
NOT NULL: This constraint could be used to ensure that certain columns in the product and customer tables, such as the product name and customer email address, cannot contain NULL values.
UNIQUE: This constraint could be used to ensure that the product IDs and customer IDs are unique. This would prevent duplicate entries in the respective tables.
PRIMARY KEY: This constraint could be used to combine the NOT NULL and UNIQUE constraints to create a primary key for each table. The product ID or customer ID could serve as the primary key.
FOREIGN KEY: This constraint could be used to establish relationships between the Murata Manufacturing product and customer tables. For example, you could use a foreign key to link the customer ID in the customer table to the customer ID in the product table to track which products each customer has purchased.
CHECK: This constraint could be used to ensure that certain data meets specific conditions. For example, you could use a CHECK constraint to ensure that Murata Manufacturing product prices are always positive numbers.
DEFAULT: This constraint could be used to specify default values for certain columns. For example, you could use a DEFAULT constraint to set the customer registration date to the current date if no value is provided when a new customer is added to the database.
SQL Question 4: Inventory Management at Murata Manufacturing
Murata Manufacturing wishes to analyze and optimize their inventory management. They have two main tables, and , to track their products in-house and when they are sold.
The table records the products that are currently in-house, and the table records when products are sold.
Example Input:
| product_id | product_name | quantity | inbound_date |
|---|---|---|---|
| 1 | capacitor | 1000 | 2022-04-01 |
| 2 | inductor | 500 | 2022-05-02 |
| 3 | resistor | 2000 | 2022-06-03 |
| 4 | transistor | 500 | 2022-07-01 |
| 5 | diode | 1000 | 2022-08-01 |
Example Input:
| order_id | product_id | quantity | sales_date |
|---|---|---|---|
| 1 | 1 | 200 | 2022-05-01 |
| 2 | 2 | 100 | 2022-05-20 |
| 3 | 3 | 500 | 2022-06-15 |
| 4 | 4 | 200 | 2022-08-02 |
| 5 | 1 | 300 | 2022-04-15 |
Murata Manufacturing is interested in understanding the turnover rate of their inventory per product and to identify which products are selling faster or slower.
Required:
- Write a PostgreSQL query to calculate the number of days from the inbound date (when the product arrived in the warehouse) to the sales date (when the product was sold), per product name.
- Include only those rows from the table where the sale happened after the product was received ( > ).
Answer:
This query performs a join on and tables on . The WHERE condition ensures that we only consider sales that happened after the product was received. The AVG function together with EXTRACT calculates the average number of days it takes for a product to get sold once it's in the warehouse, grouping the result by product name.
SQL Question 5: Can you explain what a cross-join is and the purpose of using them?
A cross-join, also known as a cartesian join, is a JOIN that produces the cross-product of two tables. In a cross-join, each row from the first table is matched with every row from the second table, resulting in a new table with a row for each possible combination of rows from the two input tables.
Suppose you were building a Neural Network ML model, that tried to score the probability of a customer buying a Murata Manufacturing product. Before you started working in Python and Tensorflow, you might want to do some Exploratory Data Analysis (EDA) in SQL, and generate all pairs of customers and Murata Manufacturing products.
Here's a cross-join query you could use to find all the combos:
Cross-joins are great for generating all possible combinations, but they can also create really big tables if you're not careful. For example, if you had 10,000 potential customers, and Murata Manufacturing had 500 different product SKUs, you'd get 5 million rows as a result!!
SQL Question 6: Employee Search by Substring
As a staff at the human resource department in Murata Manufacturing, you're responsible for maintaining the employee database. You're tasked to find all employees whose first names start with a specific letter combination. Please write a SQL query to find all employees whose first names start with 'Ji'.
Example Input:
| employee_id | first_name | department | hire_date | salary |
|---|---|---|---|---|
| 1 | James | Sales | 01/10/2016 | 7000 |
| 2 | Jill | Engineering | 03/15/2018 | 8000 |
| 3 | Jim | Marketing | 11/24/2019 | 6500 |
| 4 | Jackie | Sales | 07/30/2017 | 7200 |
| 5 | Jin | HR | 03/08/2020 | 5000 |
| 6 | Mika | Marketing | 10/05/2018 | 6000 |
| 7 | Mark | Engineering | 11/15/2016 | 8500 |
Example Output:
| employee_id | first_name | department | hire_date | salary |
|---|---|---|---|---|
| 5 | Jin | HR | 03/08/2020 | 5000 |
Answer:
In PostgreSQL, you would use the keyword to filter the names:
This query works by selecting all fields () from the table where the column starts with the string 'Ji'. The '%' symbol is a wildcard in SQL that matches any number of characters, so 'Ji%' will match any string that starts with 'Ji'.
SQL Question 7: What are some ways you can identify duplicates in a table?
One way to find duplicate records in a table is by using , and then seeing which groups have more than one occurence:
Another way is by using the operator:
SQL Question 8: Calculate Discounted Price and Profit
Murata Manufacturing has a range of products for which the selling price changes dynamically based on certain factors. The company also has different vendors who are selling these products at various discounted rates.
The task is to write a PostgreSQL query that calculates the effective selling price for each product after applying the maximum discount provided by any vendor. Also, compute the profit by subtracting the manufacturing cost from the effective selling price. Use ROUND() function to round off the profit to 2 decimal places.
Example Input:
| product_id | product_name | manufacturing_cost | selling_price |
|---|---|---|---|
| 101 | LED Light | 150 | 200 |
| 102 | Ceramic Capacitor | 200 | 300 |
| 103 | Resistor Kit | 400 | 500 |
Example Input:
| vendor_id | product_id | discount_percentage |
|---|---|---|
| 1 | 101 | 10 |
| 2 | 101 | 15 |
| 3 | 101 | 20 |
| 1 | 102 | 5 |
| 2 | 102 | 25 |
| 3 | 102 | 20 |
| 1 | 103 | 30 |
| 2 | 103 | 40 |
Example Output:
| product_id | product_name | effective_selling_price | profit |
|---|---|---|---|
| 101 | LED Light | 160.00 | 10.00 |
| 102 | Ceramic Capacitor | 225.00 | 25.00 |
| 103 | Resistor Kit | 300.00 | -100.00 |
Answer:
This SQL query first joins the products and vendors table on product_id. It then calculates the maximum discount for each product and applies it to the selling price in the SELECT clause. The calculated effective selling price is then used to compute the profit, which is the effective selling price minus the manufacturing cost. The ROUND function rounds the profit to 2 decimal places.
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for calculating highest product revenues or this CVS Health Pharmacy Analytics (Part 1) Question which is similar for identifying most profitable products.
Preparing For The Murata Manufacturing 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.
In addition to solving the above Murata Manufacturing SQL interview questions, you should also solve the 200+ SQL Interview Questions on DataLemur which come from companies like FAANG and tech startups.
Each DataLemur SQL question has hints to guide you, fully explained answers along with a discussion board to see how others solved it and crucially, there is an interactive SQL code editor so you can right in the browser run your query and have it graded.
To prep for the Murata Manufacturing SQL interview you can also be useful to solve SQL questions from other tech companies like:
However, if your SQL query skills are weak, forget about diving straight into solving questions – go learn SQL with this SQL tutorial for Data Scientists & Analysts.
This tutorial covers topics including sorting results with ORDER BY and functions like SUM()/COUNT()/AVG() – both of these show up frequently in SQL job interviews at Murata Manufacturing.
Murata Manufacturing Data Science Interview Tips
What Do Murata Manufacturing Data Science Interviews Cover?
Beyond writing SQL queries, the other types of questions tested in the Murata Manufacturing Data Science Interview are:
- Statistics and AB Testing Questions
- Python or R Programming Questions
- Business Sense and Product-Sense Questions
- Machine Learning Questions
- Behavioral Interview Questions
How To Prepare for Murata Manufacturing Data Science Interviews?
To prepare for Murata Manufacturing Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions taken from companies like Microsoft, Google & Amazon
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