VIZIO employees write SQL queries all the damn time for analyzing customer television viewing data and managing product inventory databases. So, it shouldn't surprise you that VIZIO almost always asks SQL problems during interviews for Data Science, Analytics, and & Data Engineering jobs.
Thus, to help prep you for the VIZIO SQL interview, we've curated 11 VIZIO SQL interview questions can you solve them?
For this question, imagine the following scenario associated with the VIZIO dataset:
You are given a table named 'reviews' which contains customer reviews on various VIZIO products. The data includes a timestamp for when the review was submitted. You are requested to write a SQL query using Window Functions to calculate the average ratings (stars) for each product on a month-to-month basis.
Here's the sample input and output:
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 |
mth | product | avg_stars |
---|---|---|
6 | 50001 | 3.50 |
6 | 69852 | 4.00 |
7 | 69852 | 2.50 |
With PostgreSQL, you can easily extract the month from a date using the EXTRACT function, and then use the AVG() function to find the average ratings within each month.
Here's a sample SQL query for this problem:
The above SQL query will partition the data by 'product_id' and 'month' and computes the average rating for each group. As a result, it will give you the average ratings for each product on a month-to-month basis. Keep in mind that EXTRACT function is being used to extract the month from the 'submit_date' and AVG is used to calculate average in each partition.
Pro Tip: Window functions are a popular SQL interview topic, so practice every window function problem on DataLemur
You're given a table of VIZIO employee and department salary information. Write a SQL query to find departments where the departments average salary is higher than the company's average salary.
Code your solution to this interview question and run your code right in DataLemur's online SQL environment:
The answer is LONG – 30+ lines of SQL. You can find a step-by-step solution with hints here: Department Salaries.
The function can take in multiple paramaters, and returns the first input paramater that is not null. If all arguments are null, the COALESCE function will return null too.
Suppose you have a table of VIZIO salespeople and the number of deals they closed. If a salesperson didn't close any deals, the data from the 3rd-party CRM system exports a NULL value.
sales_person | closed_deals |
---|---|
Jason Wright | NULL |
Drew Jackson | 3 |
Chris Ho | NULL |
Adam Cohen | 2 |
Samantha Perez | 4 |
To change these NULLs to zeros, you can use the function in the following way:
This would result in the following data:
sales_person | closed_deals |
---|---|
Jason Wright | 0 |
Drew Jackson | 3 |
Chris Ho | 0 |
Adam Cohen | 2 |
Samantha Perez | 4 |
Given that you are a data analyst at VIZIO, the company wants to keep track of the sales of products across different regions. They are particularly interested in finding out how many units of each product are sold each month in each region. Can you design a database to help VIZIO solve this problem and also write a PostgreSQL query to determine the monthly sales for each product in each region?
product_id | product_name | price |
---|---|---|
50001 | VIZIO TV | 500 |
69852 | VIZIO Sound Bar | 200 |
12471 | VIZIO Smartcast | 100 |
region_id | region_name |
---|---|
10 | North America |
20 | Europe |
30 | Asia |
sale_id | product_id | region_id | quantity | sale_date |
---|---|---|---|---|
4251 | 50001 | 10 | 50 | 06/10/2022 |
1897 | 50001 | 20 | 30 | 06/15/2022 |
9836 | 12471 | 30 | 40 | 06/20/2022 |
4205 | 69852 | 10 | 25 | 07/12/2022 |
5698 | 12471 | 30 | 65 | 07/22/2022 |
This query calculates the total units sold for each product in each region for each month. It simply groups by the , and , and sums up the for each group. The result is ordered by and the total units sold in descending order by regions and products.
{#Question-5}
NULLs are NOT the same as zero or blank spaces in SQL. NULLs are used to represent a missing value or the abscence of a value, whereas zero and blank space are legitimate values.
It's important to handle NULLs carefully, because they can mess up your analysis very easily. For example, if you compare a NULL value using the = operator, the result will always be NULL (because just like Drake, nothing be dared compared to NULL). That's why many data analysis in SQL start with removing NULLs using the function.
As an analytics professional working for VIZIO, you want to find all the customers who have both a VIZIO Smart TV and an active subscription to VIZIO's premium service. Furthermore, to better engage with loyal customers, you want to identify those who purchased their TV within the last two years.
customer_id | name | tv_purchase_date | premium_service_start_date |
---|---|---|---|
101 | John Smith | 2019-03-23 | 2021-03-23 |
204 | Jane Doe | 2020-05-05 | 2020-06-05 |
354 | Aaron Johnson | 2021-09-08 | 2022-09-08 |
412 | Emma Brown | 2018-08-15 | NULL |
575 | Michael Choi | 2022-01-15 | 2022-02-15 |
customer_id | name |
---|---|
204 | Jane Doe |
354 | Aaron Johnson |
575 | Michael Choi |
This SQL query filters out the customers from the database who have both a VIZIO Smart TV (purchased within the last two years) and an active subscription to VIZIO's premium service. The clause serves as the condition to filter the data. It first checks if the is within the last two years by subtracting '2 years' from the current date. Then it uses the clause to combine another condition, i.e., the should not be NULL representing an active subscription. The SELECT statement only retrieves the and of the customers meeting these conditions.
A is like a secret code that unlocks the door to another table. It's a field in one table that points to the (the master key) in another table. This helps keep the data in your database organized and tidy, because it won't let you add new rows to the table unless they have the correct secret code (a corresponding entry in the table).
It's also like a special bond between the two tables - if you try to delete the data, the will be like "Whoa, hold on! I still need that information!" and prevent the deletion from happening.
You are a Data Analyst at VIZIO and you have been tasked to analyze the trend of televisions sold by VIZIO. More specifically, your task is to find out the average screen size of VIZIO televisions sold each month.
Consider the given tables, and .
sale_id | date | model_id | customer_id |
---|---|---|---|
101 | 02/01/2021 00:00 | VZ50 | 8475 |
201 | 04/15/2021 00:00 | VZ65 | 4849 |
301 | 05/20/2021 00:00 | VZ60 | 8649 |
401 | 05/22/2021 00:00 | VZ65 | 5647 |
501 | 06/30/2021 00:00 | VZ50 | 4849 |
model_id | screen_size |
---|---|
VZ50 | 50 |
VZ60 | 60 |
VZ65 | 65 |
VZ70 | 70 |
VZ80 | 80 |
Here is SQL query to get the average screen size of the VIZIO television sold each month.
This query first joins the sales and tv_models tables on the column. Then it calculates the average screen size of the VIZIO televisions sold each month. The result is a list of months together with the corresponding average screen size of the televisions sold. The list is ordered by month in ascending order.
To practice a very similar question try this interactive Wayfair Y-on-Y Growth Rate Question which is similar for calculating monthly metrics or this Amazon Average Review Ratings Question which is similar for finding average metrics.
VIZIO may want to analyze the efficiency of promotional campaigns they run for their TV sets. Suppose that they are tracking every single advertisement click and store the related information in a table. Additionally, if a click is followed by a purchase transaction, they store this info in a table. The company would like to calculate the click-through rate (CTR) for each TV model they are marketing for a certain month. The CTR is calculated as the total transactions divided by the total clicks, multiplied by 100 for a certain TV model.
Below are the and tables:
click_id | user_id | click_date | product_id |
---|---|---|---|
101 | 345 | 06/08/2022 00:00:00 | 6001 |
102 | 256 | 06/10/2022 00:00:00 | 6001 |
103 | 325 | 06/18/2022 00:00:00 | 7001 |
104 | 432 | 07/26/2022 00:00:00 | 7001 |
105 | 121 | 07/05/2022 00:00:00 | 7001 |
transaction_id | user_id | transaction_date | product_id |
---|---|---|---|
201 | 345 | 06/08/2022 01:00:00 | 6001 |
202 | 256 | 06/10/2022 01:30:00 | 6001 |
203 | 325 | 07/20/2022 14:00:00 | 7001 |
204 | 432 | 07/26/2022 16:10:00 | 7001 |
Note: We assume that transactions happen after clicks. So, for clarity, a transaction time is always later than the click time correspondingly.
Your task is to write a SQL query to calculate the CTR for each product for the month of June.
Here is one way to approach this using PostgreSQL:
This query first creates two sub-tables: one for the total clicks in June and one for the total transactions in June for each product. It then joins these two tables and calculates the CTR for each product. If there are no transactions for a product, it will return a CTR of 0.
To practice another question about calculating rates, try this SQL interview question from TikTok on DataLemur's interactive SQL code editor:
Database normalization has several benefits:
Reduces Redundancy: Normalization can minimize redundancy by breaking down a larger, general table into smaller, more granular tables. This often reduces the amount of data that needs to be accessed for particular queries, since some duplicated columns can be removed.
Improves Data Integrity: Normalization can help to ensure the integrity of the data by minimizing the risk of data inconsistencies and errors. By establishing clear relationships between the tables via primary and foreign keys, and enforcing these constraints, you can have more reliable records and relationships stored in your DB.
Improves Performance: Normalization can improve the performance of the database by reducing the amount of data that needs to be stored and accessed in a single table. This results in faster query times and better overall performance.
As part of your task at Vizio, your manager asked you to filter the database for all customers whose name contains the word 'Inc.'. The customer records are stored in a table containing their unique ID and name. Write a SQL query to find these records.
customer_id | customer_name |
---|---|
2003 | Vizio Inc. |
5516 | Samsung Electronics |
8619 | Sony Corporation |
7523 | Apple Inc. |
2489 | Google Inc. |
customer_id | customer_name |
---|---|
2003 | Vizio Inc. |
7523 | Apple Inc. |
2489 | Google Inc. |
You can perform this query using the keyword in SQL.
In the provided SQL query, we retrieve the and from the table where the contains the string 'Inc.'. The percent sign (%) is a wildcard character in SQL that matches zero or more unspecified character(s), so it will return all the records where 'Inc.' is found anywhere in the customer name.
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the VIZIO SQL interview is to solve as many practice SQL interview questions as you can! Besides solving the above VIZIO SQL interview questions, you should also solve the 200+ SQL Interview Questions on DataLemur which come from companies like Facebook, Google and unicorn tech startups.
Each interview question has multiple hints, full answers and crucially, there's an interactive SQL code editor so you can instantly run your SQL query and have it executed.
To prep for the VIZIO SQL interview you can also be wise to practice SQL questions from other tech companies like:
In case your SQL coding skills are weak, forget about jumping right into solving questions – improve your SQL foundations with this DataLemur SQL tutorial.
This tutorial covers SQL concepts such as CASE/WHEN/ELSE statements and LEFT vs. RIGHT JOIN – both of which pop up routinely in SQL interviews at VIZIO.
Beyond writing SQL queries, the other types of questions to practice for the VIZIO Data Science Interview are:
I'm sorta biased, but I believe the best way to study for VIZIO Data Science interviews is to read my book Ace the Data Science Interview.
The book has 201 data interview questions sourced from Microsoft, Amazon & startups. It also has a crash course on Python, SQL & ML. And finally it's helped thousands of people land their dream job in data, which is how it's gotten to be an Amazon best-seller with over 1000+ 5-star reviews.