# 11 Sony SQL Interview Questions (Updated 2024)

Updated on

February 7, 2024

At Sony, SQL is used often for analyzing customer behavior data for gaming patterns and for managing and manipulating databases of films and music. Because of this, Sony frequently asks SQL coding questions during interviews for Data Science and Data Engineering positions.

In case you're trying to prepare for the SQL Interview, here’s 11 Sony SQL interview questions to practice, which are similar to recently asked questions at Sony – can you solve them?

## 11 Sony SQL Interview Questions

### SQL Question 1: Identify Most Active Users

Sony is interested in identifying its most active users, also referred to as "power users", "VIP users", or "whale users". The company defines these users as those who purchase the most frequently. Write an SQL query to identify the top 10 users with the highest number of purchases in the last one year.

Based on the tables and .

##### Example Input:
125user12019-01-01
452user22018-12-15
763user32020-02-20
984user42019-11-30
235user52018-06-18
##### Example Input:
purchase_iduser_idpurchase_dateproduct_idamount
10011252021-08-171052999
10024522021-08-171062999
10037632021-08-181074999
10049842022-08-191083999
10052352021-08-191091999

In above query, we first join table with the table on . We filter the data for the year 2022 using the clause. Then, we group the result by username and count the number of purchases for each user. Finally, we order the result in descending order by purchase frequency and limit the output to top 10 users.

To practice a related customer analytics question on DataLemur's free interactive coding environment, try this Microsoft SQL Interview problem:

### SQL Question 2: Calculate Average Monthly Rating for Sony Products

As a data analyst at Sony, you are tasked with analyzing the wide array of products they sell. Write a single SQL query that returns the product by product ID and month with its average stars rating.

##### Example Input:
review_iduser_idsubmit_dateproduct_idstars
617112306/08/2022 00:00:00500014
780226506/10/2022 00:00:00698524
529336206/18/2022 00:00:00500013
635219207/26/2022 00:00:00698523
451798107/05/2022 00:00:00698522

This query groups the review data by month and product_id, and uses the AVG() function to compute the average rating per product per month. The results are then ordered in ascending order of the month, and in descending order of the average rating. Thus, you can easily see which products perform best in terms of user ratings each month.

To solve another window function question on DataLemur's free online SQL coding environment, solve this Google SQL Interview Question:

### SQL Question 3: Could you explain what a self-join is and provide examples of when it might be used?

A self-join is a type of JOIN where a table is joined to itself. To execute a self-join, you must include the table name twice in the FROM clause and assign a different alias to each instance. You can then join the two copies of the table using a JOIN clause, and use a WHERE clause to specify the relationship between the rows.

For example, say you had website visitor data for Sony, exported from the company's Google Analytics account. In support of the web-dev team, you had to analyze pairs of pages for UX or navigational issues. As part of that analysis, you wanted to wanted to generate all pairs of URLs, but needed to avoid pairs where both the URLs were the same since that's not a valid pair.

The self-join query would like the following:

This query returns the url of each page () along with the url of the page that referred to it (). The self-join is performed using the field, which specifies the id of the page that referred the visitor to the current page, and avoids any pages that referred themself (aka data anomalies).

### SQL Question 4: Filter Sony Customers Data Based On Purchase History And Location

Sony, a major electronics company, wants to understand its customer base better for targeted advertising. They are interested in finding all customers who have purchased at least 2 different kinds of electronics (TV, Playstation, etc.) between January 2021 and December 2021, and are located in California.

Given a table consisting of rows representing the purchase records of customers, and a table providing the customer's location, can you write a SQL query to obtain the list of these customers?

Please take into account the following tables structure for the and tables:

##### Example Input:
purchase_idcustomer_idproduct_typepurchase_date
164321'TV''2021-07-01'
264322'TV''2021-05-12'
364321'Playstation''2021-06-25'
564324'TV''2021-09-15'
664324'Playstation''2021-08-27'
##### Example Input:
customer_idlocation
64321'California'
64322'New York'
64323'California'
64324'California'

The SQL query first joins the table to the table on the field. It then filters out records where the product_type is either 'Playstation' or 'TV', the purchase_date is between '2021-01-01' and '2021-12-31', and the customer location is 'California'. The statement groups records by , and the clause filters out groups having less than 2 unique , representing customers who have purchased at least 2 different types of products.

### SQL Question 5: Can you explain the concept of a constraint in SQL?

Constraints are used to specify the rules concerning data in the table. It can be applied for single or multiple fields in an SQL table during the creation of the table or after creating using the ALTER TABLE command. The constraints are:

Say you were storing sales analytyics data from Sony's CRM inside a database. Here's some example constraints you could use:

• PRIMARY KEY constraint: You might use a PRIMARY KEY constraint to ensure that each record in the database has a unique identifier. For example, you could use the "opportunity_id" field as the primary key in the "opportunities" table.

• FOREIGN KEY constraint: You might use a FOREIGN KEY constraint to link the data in one table to the data in another table. For example, you could use a foreign key field in the "opportunities" table to reference the "account_id" field in the "accounts" table.

• NOT NULL constraint: You might use a NOT NULL constraint to ensure that a field cannot contain a NULL value. For example, you could use a NOT NULL constraint on the "opportunity_name" field in the "opportunities" table to ensure that each opportunity has a name.

• UNIQUE constraint: You might use a UNIQUE constraint to ensure that the data in a field is unique across the entire table. For example, you could use a UNIQUE constraint on the "email" field in the "contacts" table to ensure that each contact has a unique email address.

• CHECK constraint: You might use a CHECK constraint to ensure that the data in a field meets certain conditions. For example, you could use a CHECK constraint to ensure that the "deal_probability" field in the "opportunities" table is a value between 0 and 100.

• DEFAULT constraint: You might use a DEFAULT constraint to specify a default value for a field. For example, you could use a DEFAULT constraint on the "stage" field in the "opportunities" table to set the default value to "prospecting"

### SQL Question 6: Calculate the Average Selling Price of Sony's Different Product Categories

As a data analyst at Sony, one of your tasks is to help the sales and marketing team understand the average selling price of each category of Sony products. Using the Sony product sales dataset, can you write a SQL query that calculates the average selling price of each product category?

##### Example Input:
sale_idsale_dateproduct_idproduct_categorysale_price
1012021/01/01001TVs500
1022021/01/02002Cameras200
1032021/01/05003Phones800
1042021/01/07004TVs600
1052021/01/11005Cameras300
##### Example Output:
product_categoryavg_sale_price
TVs550.00
Cameras250.00
Phones800.00

In PostgreSQL, you would use the AVG function to calculate the average selling price:

The query initializes by selecting the and columns from the table. It then calculates the average for each using the AVG function. Lastly, the query groups the data by , so the output reveals the average selling price for each distinct category of Sony products.

To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for calculating category-wise metrics or this Wayfair Y-on-Y Growth Rate Question which is similar for analysis of sales data.

### SQL Question 7: What is the purpose of a primary key in a database?

A primary key is a special type of column or set of columns in a database table that is used to identify each row in the table. It helps to ensure that all the rows are distinct and prevents null values. Additionally, primary keys play a crucial role in maintaining the accuracy of the data by making sure that there are no duplicate rows. They are also used to establish relationships between tables.

To create a primary key in a SQL database, you can use the constraint. For example, in the table , the column is the primary key.

### SQL Question 8: Calculate the Click-Through Conversion Rate for Sony

Sony, a multi-national company that sells a huge range of electronic products, is running a digital marketing campaign. They want to understand the click-through conversion rate of their digital ads that drive users from viewing a product to adding a product to the cart.

You are given two tables 'Clicks' and 'Carts', which show those users who clicked to view a product and those users who then added the product to their cart.

##### Example Input:
click_iduser_idclick_timeproduct_id
10167806/08/2022 11:00:0090301
10235606/10/2022 14:00:0090306
10378906/18/2022 16:00:0090301
10467807/26/2022 15:00:0090306
##### Example Input:
cart_iduser_idcart_timeproduct_id
20135606/10/2022 14:05:0090306
20278906/18/2022 16:10:0090301

This SQL block helps Sony calculate the conversion rate of their digital ads for the month of June 2022. It considers only these users who clicked to view a product and those users who then added the product to their shopping carts within June. The conversion rate is then calculated as the percentage of users who, after clicking on a product, added it to their cart.

To solve a similar problem about calculating rates, solve this SQL interview question from TikTok on DataLemur's online SQL coding environment:

### SQL Question 9: What is the average sale of each product per month?

Consider a case where Sony wants to know the average total sales achieved for each of their products on a monthly basis. You are given a table named with the following schema:

• (integer): the id of the sale.
• (integer): the id of the product.
• (timestamp): the date of sale.
• (integer): the number of product units sold.
• (decimal): the price per unit of the product.

Given the above, write a SQL query that will return each product's id and its average total sales per month. The total sale for each sale_id is calculated as *.

##### Example Input:
sale_idproduct_idsale_datequantityprice_per_unit
121432106/08/2022 00:00:0010100
932162706/12/2022 00:00:005200
789132106/22/2022 00:00:0012100
678298107/02/2022 00:00:006150
173462707/25/2022 00:00:008200
##### Example Output:
monthproduct_idavg_sales
63211100.00
66271000.00
7981900.00
76271600.00

What does this query do?

This query first extracts the month from the and then groups the sales by this month and . For each created group, it calculates the average of total sales for each product. The total sale for each sale entry is calculated as the product of and . The function then calculates the average total sale of each product per month.

### SQL Question 10: What's a correlated sub-query? How does it differ from a non-correlated sub-query?

A correlated sub-query is one that relies on the outer query and cannot be run independently. It uses the outer query to filter or transform data by referencing a column from it, while the outer query uses the results of the inner query. On the other hand, a non-correlated sub-query is not linked to the outer query and can be run on its own. It does not reference any columns from the outer query and is used to retrieve additional data for the outer query.

Here is an example of a correlated sub-query:

This query selects the and total sales of all Sony customers in the sales table whose are greater than the average of their own customer group. The sub-query in this case is correlated with the outer query, as it references the column from the outer query ().

Here is an example of a non-correlated sub-query:

The sub-query in this case is non-correlated, as it does not reference any columns from the outer query.

Correlated sub-queries are slower to execute, as they have to be re-evaluated for each row of the outer query, while non-correlated sub-queries are faster, as they only have to be executed once.

### SQL Question 11: Calculating Weighted Rating for Sony Products

As a Sony data analyst, you are responsible for checking Sony product reviews and identifying top-rated products. You notice the star ratings are biased by the quantity of reviews a product receives. To solve this issue, you decide to implement a rating calculation method based on the IMDb Weighted Rating formula. Applying this formula requires using SQL math functions and arithmetic operators.

The IMDb weighted-rating formula is:

Here,

• R = average for the movie (mean) = average Star rating.
• v = the number of votes for the movie = total reviews for a product.
• m = minimum votes required to be listed = minimum reviews required for the product to be considered. (Let's assume it's 50 for our scenario.)
• C = the mean vote across the whole report = overall average Star rating.

Using the below dataset, find the top three Sony products with the highest weighted ratings for the month of June 2022:

##### Example Input:
review_iduser_idsubmit_dateproduct_idstars
617112306/08/2022 00:00:00500014
780226506/10/2022 00:00:00698524
529336206/18/2022 00:00:00500013
635219207/26/2022 00:00:00698523
451798107/05/2022 00:00:00698522

Consider the following PostgreSQL query:

The query calculates the weighted rating for each product for the month of June 2022. The clause defines two subqueries: , which calculates the average rating and count of reviews for each product, and , which calculates the overall average rating for all products. The final query calculates the weighted rating and orders the products by this rating, outputting the top three products.

To practice a very similar question try this interactive Amazon Average Review Ratings Question which is similar for calculating average rating or this Amazon Highest-Grossing Items Question which is similar for identifying top products.

### Preparing For The Sony 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 earlier Sony SQL interview questions, you should also solve the 200+ SQL Interview Questions on DataLemur which come from companies like FAANG and tech startups.

Each SQL question has multiple hints, full answers and crucially, there's an online SQL coding environment so you can instantly run your SQL query answer and have it checked.

To prep for the Sony SQL interview it is also a great idea to solve SQL problems from other tech companies like:

In case your SQL skills are weak, don't worry about going right into solving questions – improve your SQL foundations with this interactive SQL tutorial.

This tutorial covers things like aggregate window functions and GROUP BY – both of these come up frequently in Sony SQL interviews.

### Sony Data Science Interview Tips

#### What Do Sony Data Science Interviews Cover?

For the Sony Data Science Interview, beyond writing SQL queries, the other types of questions which are covered:

• Probability & Stats Questions
• Python or R Programming Questions
• Product Analytics Questions
• Machine Learning Questions
• Behavioral Interview Questions

#### How To Prepare for Sony Data Science Interviews?

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

• 201 Interview Questions from Google, Microsoft & tech startups
• A Crash Course on Stats, ML, & Data Case Studies
• Amazing Reviews (900+ reviews, 4.5-star rating)