8 Panasonic SQL Interview Questions (Updated 2024)
At Panasonic, SQL is used across the company for data analysis to optimize product functionality and supply chain efficiency, and for managing and querying huge datasets to gain consumer behavior insights. Unsurprisingly this is why Panasonic almost always evaluates jobseekers on SQL questions in interviews for Data Science, Data Engineering and Data Analytics jobs.
As such, to help you practice for the Panasonic SQL interview, we've curated 8 Panasonic SQL interview questions – able to answer them all?
8 Panasonic SQL Interview Questions
SQL Question 1: Calculate Average Review Rating per Month for Each Product
As a Data Analyst at Panasonic, you are tasked with reviewing the customer feedback data. Write a SQL query to calculate the average product rating, by month, for each product. Use the "reviews" table which contains columns for: review_id (integer; unique ID for each review), user_id (integer; ID of the user that submitted the review), submit_date (date; the date the review was submitted), product_id (integer; ID of the product the review is for), and stars (integer; the star rating the user gave the product).
The output should include the following columns: mth (integer; month), product_id (integer; ID of the product), and avg_stars (float; average star rating for the product in that month).
Example Input:
| review_id | user_id | submit_date | product_id | stars |
|---|---|---|---|---|
| 6171 | 123 | 2022-06-08 | 50001 | 4 |
| 7802 | 265 | 2022-06-10 | 69852 | 4 |
| 5293 | 362 | 2022-06-18 | 50001 | 3 |
| 6352 | 192 | 2022-07-26 | 69852 | 3 |
| 4517 | 981 | 2022-07-05 | 69852 | 2 |
Expected Output:
| mth | product | avg_stars |
|---|---|---|
| 6 | 50001 | 3.50 |
| 6 | 69852 | 4.00 |
| 7 | 69852 | 2.50 |
Answer:
This query groups the reviews by month and product_id, then calculates the average star rating for each group. It uses the PostgreSQL function to get the month from the and the function to compute average rating. As a result, we obtain a table showing how the average rating of each product changes per month.
Pro Tip: Window functions are a popular SQL interview topic, so practice all the window function problems on DataLemur
SQL Question 2: Calculate the Average Sales of Each Panasonic Product Category
As an analyt at Panasonic, you are tasked to analyze sales data. Given the sales records of various Panasonic product categories, how can you calculate the average sales of each product category? Assume the cost recorded represents the selling price times the quantity sold.
Example Input:
| sales_id | product_type | selling_date | quantity_sold | cost |
|---|---|---|---|---|
| 101 | TV | 01/03/2022 | 70 | 70000 |
| 102 | Mobile | 02/16/2022 | 80 | 80000 |
| 103 | TV | 05/21/2022 | 100 | 100000 |
| 104 | Air Conditioner | 07/30/2022 | 50 | 50000 |
| 105 | Mobile | 12/25/2022 | 100 | 100000 |
Example Output:
| product_type | avg_sales |
|---|---|
| TV | 85000 |
| Mobile | 90000 |
| Air Conditioner | 50000 |
Answer:
This SQL query calculates the average sales for each Panasonic product category. Here, AVG() is an aggregate function that calculates the average value of 'cost' for each group specified by the GROUP BY clause (in this case, 'product_type'). Thus, the output table contains the average sales for each product category in Panasonic sales records.
To practice a very similar question try this interactive Wayfair Y-on-Y Growth Rate Question which is similar for calculating average and aggregation or this Amazon Highest-Grossing Items Question which is similar for handling sales related data.
SQL Question 3: What is the purpose of the UNIQUE constraint?
A UNIQUE constraint ensures that all values in a column are different. It is often used in conjunction with other constraints, such as NOT NULL, to ensure that the data meets certain conditions.
For example, if you had Panasonic sales leads data stored in a database, here's some constraints you'd use:
In this example, the UNIQUE constraint is applied to the "email" and "phone" fields to ensure that each Panasonic lead has a unique email address and phone number. This helps to ensure the integrity of the data in the database and prevents errors that could occur if two leads had the same email address or phone number.
SQL Question 4: Filter Customer Records for Specific Product
Panasonic is a multinational electronics corporation that sells various products like televisions, cameras, and home appliances. As a data analyst at Panasonic, you are asked to filter the customer records for a specific product from the customer purchase history. The task is to write a SQL query that selects all the customers who have purchased a particular product, say 'TV', more than once.
Use the customer and purchase history tables as reference. The customer table has columns customer_id, first_name, last_name, and the purchase history table has columns id, customer_id and product name.
Example Input:
| customer_id | first_name | last_name |
|---|---|---|
| 1 | Joe | Bedward |
| 2 | Marc | Stalin |
| 3 | Jorge | Meyer |
| 4 | Justin | Thyme |
| 5 | Zara | Apollo |
Example Input:
| purchase_id | customer_id | product |
|---|---|---|
| 101 | 1 | TV |
| 102 | 2 | Camera |
| 103 | 1 | TV |
| 104 | 2 | Microwave |
| 105 | 3 | TV |
| 106 | 5 | TV |
Answer:
Here is the PostgreSQL query that will return all customers who have purchased a 'TV' more than once.
This query first finds the customer id's in purchase history who have a count greater than 1 for the product 'TV' using a subquery in WHERE clause . It then selects first name, last name from the customer table for the id's returned by the subquery. This shows us the customers that have purchased 'TV' more than once.
SQL Question 5: How does and differ?
The clause works similarly to the clause, but it is used to filter the groups of rows created by the clause rather than the rows of the table themselves.
For example, say you were analyzing Panasonic sales data:
This query retrieves the total sales for all products in each region, and uses the clause to only sales made after January 1, 2023. The rows are then grouped by region and the clause filters the groups to include only those with total sales greater than $500k.
SQL Question 6: Calculate Average Power Consumption and Round-off to Nearest Integer
Panasonic has multiple product lines, each with its own power consumption during operation. For this exercise, consider that Panasonic maintains a database table named with data about each product's ID, name, and power consumption. Another database table captures information about which product was sold on which date and in what quantity.
Given the two tables, your task is to calculate the average power consumption (in watts) per product sold for the current month (assuming the current month is July 2022), rounded off to the nearest integer.
Example Input:
| product_id | product_name | power_consumption_watts |
|---|---|---|
| 50001 | "Microwave Oven" | 1500 |
| 69852 | "Air Conditioner" | 2500 |
| 10045 | "LED Television" | 210 |
Example Input:
| sale_id | sale_date | product_id | quantity |
|---|---|---|---|
| 6171 | 06/20/2022 | 50001 | 3 |
| 7802 | 06/25/2022 | 69852 | 2 |
| 5293 | 07/12/2022 | 50001 | 4 |
| 6352 | 07/14/2022 | 69852 | 1 |
| 4517 | 07/17/2022 | 50001 | 2 |
Example Output:
| month | product | avg_power_consumption |
|---|---|---|
| 7 | "Microwave Oven" | 1500 |
| 7 | "Air Conditioner" | 2500 |
Answer:
In this solution, we are joining the and tables on . We are then restricting the data to July 2022 sales records. The power consumptions are averaged out (using AVG) and then rounded off (using the ROUND function) for each sold product. We group by and because we want the average for each product for July 2022.
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for calculating top products based on sales data or this Wayfair Y-on-Y Growth Rate Question which is similar for calculating growth rate based on sales.
SQL Question 7: What are the three different normal forms?
The three levels of database normalization (also known as normal forms) are:
First Normal Form (1NF):
- Each column should contain the same type of data (no mixing strings vs. integers)
- Each column in a table contains a single value (no lists or containers of data)
- Each row in the table is unique
Second Normal Form (2NF)
- It's in 1st Normal Form
- All non-key attributes are dependent on the primary key
Said another way, to achieve 2NF, besides following the 1NF criteria, the columns should also be dependent only on that table's primary key.
Third normal form (3NF) if it meets the following criteria:
- It's in 2nd Normal Form
- There are no transitive dependencies in the table.
A transitive dependency means values in one column are derived from data in another column. For example, it wouldn't make sense to keep a column called ""vehicle's age"" and ""vehicle's manufacture date" (because the vehicle's age can be derived from the date it was manufactured).
Although there is a 4NF and 5NF, you probably don't need to know it for the Panasonic interview.
SQL Question 8: Average Sales Per Month of Panasonic Products
As a Data Analyst at Panasonic, you are tasked with analyzing the sales data for each product category on a monthly basis. You are expected to determine the average quantity of products sold per month for each product category.
Provided below are the sales transactions for Panasonic products. The table consists of five columns: , , , and .
Example Input:
| sales_id | product_id | product_category | quantity | date_sold |
|---|---|---|---|---|
| 4567 | 1001 | TV | 2 | 2022-07-01 |
| 6789 | 1002 | TV | 3 | 2022-07-15 |
| 2345 | 2001 | Air Conditioner | 1 | 2022-07-21 |
| 8234 | 3001 | Kitchen Appliance | 5 | 2022-08-01 |
| 7645 | 3002 | Kitchen Appliance | 4 | 2022-08-12 |
You are expected to write a SQL query to create a new table that consists of three columns: , , and . The is the average quantity of products sold per month for each product category.
Create an output table based on the provided table.
Example Output:
| year | Month | product_category | avg_quantity |
|---|---|---|---|
| 2022 | 7 | TV | 2.5 |
| 2022 | 7 | Air Conditioner | 1 |
| 2022 | 8 | Kitchen Appliance | 4.5 |
Answer:
To solve this problem, we can use the function in PostgreSQL to get the month and year from the column. Then, we use the function to calculate the average quantity and group by the extracted month and year, and the product category.
This query combines the , , and functions to calculate the average quantity sold per product category per month. It displays the year, month, product category, and the calculated average quantity in the result.
How To Prepare for the Panasonic SQL Interview
The key to acing a Panasonic SQL interview is to practice, practice, and then practice some more!
In addition to solving the above Panasonic SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like Netflix, Google, and Amazon.
Each interview 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 online SQL coding environment so you can instantly run your SQL query answer and have it executed.
To prep for the Panasonic SQL interview it is also helpful to practice SQL problems from other tech companies like:
But if your SQL skills are weak, don't worry about going right into solving questions – go learn SQL with this DataLemur SQL Tutorial.
This tutorial covers SQL topics like joining a table to itself and sorting data with ORDER BY – both of which show up often in Panasonic SQL assessments.
Panasonic Data Science Interview Tips
What Do Panasonic Data Science Interviews Cover?
Beyond writing SQL queries, the other question categories tested in the Panasonic Data Science Interview are:
- Statistics and Probability Questions
- Coding Questions in Python or R
- Data Case Study Questions
- Machine Learning Questions
- Behavioral & Resume-Based Questions
How To Prepare for Panasonic Data Science Interviews?
The best way to prepare for Panasonic Data Science interviews is by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from FAANG tech companies
- A Crash Course on SQL, Product-Sense & ML
- Amazing Reviews (900+ 5-star reviews on Amazon)