8 Hisense SQL Interview Questions (Updated 2025)

At Hisense Visual Technology, SQL is crucial for analyzing customer behavior for optimal visual display settings and processing product quality data to ensure high-quality television production. Unsurprisingly this is why Hisense almost always evaluates jobseekers on SQL problems during interviews for Data Science and Data Engineering positions.

Thus, to help you study for the Hisense SQL interview, we've curated 8 Hisense Visual Technology SQL interview questions – able to solve them?

8 Hisense SQL Interview Questions

SQL Question 1: Calculate Monthly Average Stars for Each Product

As a data analyst at Hisense, you would be required to conduct reviews analysis on their product line. Your task here is to write a SQL query to calculate the monthly average star rating for each product based on customer reviews. Please note that the submit_date column in the review table represents the dates when reviews were submitted.

reviews Example Input:

products Example Input:

Answer:

Here is a SQL query for calculating the monthly average stars for each product:

SELECT
    DATE_TRUNC('month', submit_date) AS mth,
    product_id as product,
    AVG(stars) as avg_stars
FROM
    reviews
GROUP BY
    mth,
    product
ORDER BY 
    mth, 
    avg_stars DESC;

Another approach to this problem with the use of window function would be:

SELECT 
    EXTRACT(MONTH FROM submit_date) as month,
    product_id,
    AVG(stars) OVER (PARTITION BY product_id, EXTRACT(MONTH FROM submit_date)) as avg_stars
FROM 
    reviews;

These queries will give you the following output:

Example Output:

These queries calculate the average rating by product and month. The GROUP BY clause groups the results by month and product. The AVG function then calculates the mean of the stars for each group. The PARTITION BY in the window function makes it possible to compute the average for each partition, which in this case is per product_id and per month of reviews.

To solve another window function question on DataLemur's free online SQL code editor, solve this Amazon SQL Interview Question:

Sure, here's an example of an SQL interview question on click-through-rate for the company Hisense.

SQL Question 2: Calculate Click-through Conversion Rate

Hisense is a company that sells a variety of digital products online. They are interested in understanding the click-through conversion rates of their products, from viewing a product to adding a product to the cart. The aim is to measure the effectiveness of their product placement on the website.

We have two tables, product_views and cart_additions. The product_views table records every time a user views a product, and the cart_additions table records every time a user adds a product to their cart.

Calculate the click-through conversion rate for each product (calculated as the number of cart additions divided by the number of product views).

Provide data and solution for PostgreSQL.

product_views Example Input:

cart_additions Example Input:

Answer:

SELECT pv.product_id, 
       (COUNT(ca.addition_id)* 1.0 / COUNT(pv.view_id)) AS click_through_rate
FROM product_views pv
LEFT JOIN cart_additions ca
ON pv.user_id = ca.user_id AND pv.product_id = ca.product_id
GROUP BY pv.product_id;

This PostgreSQL query calculates the click-through conversion rate by counting the number of cart additions for each product and dividing it by the number of product views. The LEFT JOIN makes sure that all products that were viewed are included even if they were not added to the cart.

To practice a similar SQL interview question on DataLemur's free interactive coding environment, solve this Meta SQL interview question:

SQL Question 3: Can you explain the distinction between a left and right join?

Both left and right joins in SQL allow you to combine data from different tables based on a shared key or set of keys. For a concrete example of the difference between these two join types, say you had sales data exported from Hisense's Salesforce CRM stored in a PostgreSQL database, and had access to two tables: sales and hisense_customers.

LEFT JOIN: retrieves all rows from the left table (in this case, the sales table) and any matching rows from the right table (the hisense_customers table). If there is no match in the right table, NULL values will be returned for the right table's columns.

• RIGHT JOIN: retrieves all rows from the right table (in this case, the customers table) and any matching rows from the left table (the sales table). If there is no match in the left table, NULL values will be returned for the left table's columns.

SQL Question 4: Calculate Average Ratings for Hisense Products

Given a table containing details about product reviews submitted by various users for different Hisense products, write a SQL query that calculates the average rating for each product for each month.

product_reviews Example Input:

Example Output:

Answer:

SELECT
    EXTRACT(MONTH FROM submission_date) AS month,
    product_id,
    AVG(stars) AS average_rating
FROM 
    product_reviews
GROUP BY
    month, product_id
ORDER BY
    month, product_id;

This SQL query first extracts the month from the submission_date column using the EXTRACT(MONTH FROM date_column) function. It then calculates the average rating for each product (indicated by the product_id column) for each month. The GROUP BY clause is used to group the results by both month and product_id, so an average rating is provided for each product for each month. Finally, the ORDER BY clause orders the results first by month and then by product_id for readability.

SQL Question 5: In database design, what do foreign keys do?

A foreign key is a column or group of columns in a table that refers to the primary key in another table. The foreign key constraint helps maintain referential integrity between the two tables.

For example, let's look at the Hisense sales database:

hisense_sales:

+------------+------------+------------+------------+ | order_id | product_id | customer_id| quantity | +------------+------------+------------+------------+ | 1 | 222 | 1 | 2 | | 2 | 333 | 1 | 1 | | 3 | 444 | 2 | 3 | | 4 | 555 | 3 | 1 | +------------+------------+------------+------------+

In this table, product_id and customer_id could both be foreign keys. They reference the primary keys of other tables, such as a Products table and a Customers table, respectively. This establishes a relationship between the hisense_sales table and the other tables, such that each row in the sales database corresponds to a specific product and a specific customer.

"

SQL Question 6: Fetching Records with Specific Pattern

As part of Hisense's marketing team, you are tasked with creating an email campaign specifically for customers who have previously expressed interest in TVs. Using the customer records database, write an SQL query that will select all customers whose interest contains the word "TV".

customer_records Example Input:

Example Output:

Answer:

SELECT *
FROM customer_records
WHERE interests LIKE '%TV%';

This PostgreSQL query uses the LIKE operator, which allows to perform pattern matching using wildcards. The % character is a wildcard that matches any sequence of characters. So '%TV%' matches any string that contains 'TV' anywhere in it. The query selects all fields (*) from the customer_records table for which the interests field contains the string 'TV'. Hence, it gives us the customers who have 'TV' as one of their interests.

SQL Question 7: What does UNION do in a SQL query?

UNION is used to combine the output of multiple SELECT statements into one big result!

For a concrete example, say you were a Data Analyst supporting the Sales Analytics team at Hisense, and data on potential sales leads lived in both Salesforce CRM and Hubspot. To write a query to analyze leads created after 2023 started, across both CRMs, you could use UNION in the following way:

SELECT email, job_title, company_id
FROM hisense_sfdc_leads
WHERE created_at > '2023-01-01';

UNION

SELECT email, job_title, company_id
FROM hisense_hubspot_leads
WHERE created_at > '2023-01-01'

"

SQL Question 8: Average Monthly Sales of Hisense Products

As a data analyst at Hisense, your job is to analyze sales data and generate monthly sales reports. You are asked to write an SQL query to find out the average number of units sold per product on a monthly basis in the year 2022.

Assume that you have the following sales sample data:

sales Example Input:

Answer:

SELECT 
    TO_CHAR(sold_date, 'yyyy-mm') AS month, 
    product_id, 
    AVG(units_sold) as avg_units_sold
FROM 
    sales
WHERE 
    EXTRACT(YEAR FROM sold_date) = 2022
GROUP BY
    month,
    product_id;

This SQL command extracts the year and month from the sold_date by using the TO_CHAR function. It then groups the data by both month and product_id columns. Within each group, it calculates the average number of units sold using AVG aggregate function. The WHERE clause limits the data to the year 2022.

Example Output:

Please note that your output might vary based on the input data.

Preparing For The Hisense 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. Besides solving the above Hisense SQL interview questions, you should also solve the 200+ SQL exercises on DataLemur which come from companies like Google, Microsoft and Silicon Valley startups.

Each problem on DataLemur has hints to guide you, detailed solutions and best of all, there is an interactive SQL code editor so you can right in the browser run your query and have it checked.

To prep for the Hisense SQL interview it is also wise to practice SQL problems from other tech companies like:

• Samsung SQL questions

• Sony SQL questions

• LG electronics SQL questions

• panasonic SQL questions

However, if your SQL coding skills are weak, don't worry about jumping right into solving questions – go learn SQL with this interactive SQL tutorial.

This tutorial covers SQL concepts such as math functions like ROUND()/CEIL() and filtering data with WHERE – both of which come up often in Hisense SQL interviews.

Hisense Visual Technology Data Science Interview Tips

What Do Hisense Data Science Interviews Cover?

In addition to SQL query questions, the other types of problems covered in the Hisense Data Science Interview are:

• Probability & Statistics Questions
• Coding Questions in Python or R
• Product-Sense and Business Case Questions
• ML Modelling Questions
• Behavioral Interview Questions

How To Prepare for Hisense Data Science Interviews?

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

• 201 Interview Questions from Microsoft, Amazon & startups
• A Refresher covering SQL, Product-Sense & ML
• Amazing Reviews (900+ 5-star reviews on Amazon)