8 Roku SQL Interview Questions (Updated 2025)

At Roku, SQL is used day-to-day for analyzing user streaming behavior data and maintaining advertising metrics, crucial for personalized content recommendation and monetization strategies. That's why Roku frequently asks SQL questions in interviews for Data Science and Data Engineering positions.

To help you study for the Roku SQL interview, this blog covers 8 Roku SQL interview questions – can you solve them?

8 Roku SQL Interview Questions

SQL Question 1: Calculate Monthly Average Review Scores Per Product

Roku would be interested in tracking the performance of their products based on customer reviews. Let's assume Roku collects user reviews on its products and each review is tied to a product id, review id, user id, and submit date.

Write a SQL query to calculate the average review score (stars) for each product_id on a monthly basis. Your result should be ordered by month (mth) and then product_id.

reviews Example Input:

Answer:

SELECT 
    EXTRACT(MONTH FROM submit_date) AS mth,
    product_id,
    ROUND(AVG(stars)::numeric, 2) as avg_stars
FROM 
    reviews
GROUP BY 
    mth,
    product_id
ORDER BY 
    mth,
    product_id;

This PostgreSQL query applies the AVG function (which calculates the mean) to the stars column in order to find the average review score per product per month. The EXTRACT(MONTH FROM submit_date) AS mth clause is used to group the data by month. The ROUND function is used to round to two decimal places for the sake of neatness.

Please note that your dataset should have the time in 'YYYY-MM-DD' format.

Example Output:

For more window function practice, solve this Uber SQL problem on DataLemur's online SQL coding environment:

SQL Question 2: Design Database for Tracking Video Streaming

Roku, Inc. is a provider of hardware and software for video streaming in the home. Given that Roku offers a large variety of channels, they want to keep track of the following data related to each user:

1. User information (Name, Email)
2. User subscription details (SubscriptionType - Free, Basic, Premium)
3. The channels watched by the user (ChannelID, ChannelName)
4. The date, time, and duration of each watch session

Design a database that can store this information, ensuring it is searchable and can be analyzed for user behavior patterns.

users Sample Input:

channels Sample Input:

watch_sessions Sample Input:

Assuming the database tables are users, channels, and watch_sessions, write a PostgreSQL query to return all users who watched more than 100 minutes of content on a given date.

Answer:

SELECT u.user_id, u.name
FROM users u
INNER JOIN watch_sessions ws on u.user_id = ws.user_id
WHERE ws.watch_date = '2022/10/01'
GROUP BY u.user_id
HAVING SUM(ws.watch_duration) > 100

The query joins the users table and watch_sessions table on user_id, filtered by the watch_date. It groups the result by user_id and filters out users who watched more than 100 minutes based on the aggregate function SUM().

SQL Question 3: What does the EXCEPT operator do?

The EXCEPT operator is used to return all rows from the first SELECT statement that are not returned by the second SELECT statement. Note that EXCEPT is available in PostgreSQL and SQL Server, and it's equivalent operator is called MINUS and is available in MySQL and Oracle.

For a tangible example, suppose you were doing an HR Analytics project for Roku, and had access to Roku's contractors and employees data. Assume that some employees were previously contractors, and vice versa, and thus would show up in both tables. You could use EXCEPT operator to find all employees who never were a contractor using this query:

SELECT first_name, last_name
FROM roku_employees

EXCEPT

SELECT first_name, last_name
FROM roku_contractors

SQL Question 4: Calculate the Average Streaming Hours per Show for Roku Users

For Roku, a company that deals with digital media players and streaming services, a relevant question might be:
"What is the average streaming hours per show by Roku users for the past month?"

Assuming we have two tables users and streaming_history:

users Example Input:

streaming_history Example Input:

Answer:

SELECT show_id, 
       AVG(streaming_hrs) as average_streaming_hrs
FROM streaming_history
WHERE DATE_TRUNC('MONTH', stream_date) = DATE_TRUNC('MONTH', CURRENT_DATE - INTERVAL '1' MONTH)
GROUP BY show_id
ORDER BY average_streaming_hrs DESC;

This query calculates the average streaming hours per show for the past month. It groups the data by show_id, and only considers the records where the stream_date is from the previous month. The resulting averages are sorted in descending order, so you can quickly identify the shows with the highest average streaming hours.

The DATE_TRUNC function is used here to align dates to the start of the month, allowing for a simple comparison between any stream_date and the start of the previous month. The DATE_TRUNC function "truncates" a date (or timestamp) to the specified precision, which in this case is 'MONTH'.

To practice a very similar question try this interactive Facebook App Click-through Rate (CTR) Question which is similar for computing metrics for a specific time period or this New York Times Laptop vs. Mobile Viewership Question which is similar for calculating usage statistics for media consumption.

SQL Question 5: Name the different types of joins in SQL. What does each one do?

In SQL, a join is used to combine rows from different tables based on a shared key or set of keys, resulting in a single merged table of data.

There are four distinct types of JOINs: INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL OUTER JOIN.

(INNER) JOIN: Retrieves records that have matching values in both tables involved in the join.

SELECT *
FROM Table_A
JOIN Table_B;
SELECT *
FROM Table_A
INNER JOIN Table_B;

LEFT (OUTER) JOIN: Retrieves all the records/rows from the left and the matched records/rows from the right table.

SELECT *
FROM Table_A A
LEFT JOIN Table_B B
ON A.col = B.col;

RIGHT (OUTER) JOIN: Retrieves all the records/rows from the right and the matched records/rows from the left table.

SELECT *
FROM Table_A A
RIGHT JOIN Table_B B
ON A.col = B.col;

FULL (OUTER) JOIN: Retrieves all the records where there is a match in either the left or right table.

SELECT *
FROM Table_A A
FULL JOIN Table_B B
ON A.col = B.col;

SQL Question 6: Evaluate Click-Through Conversion Rate for Roku Products

For a company like Roku, it's important to understand how many of the users that clicked on a digital ad for a product end up adding that product to their cart. Calculate the click-through conversion rate defined as the number of users who added a product to their cart after clicking on its ad divided by the total number of users who clicked on that ad.

ad_clicks Example Input:

cart_add Example Input:

Answer:

SELECT a.product_id, 
       (CAST(COUNT(c.user_id) AS float) / COUNT(a.user_id)) AS click_conversion_rate
FROM ad_clicks a
LEFT JOIN cart_add c 
ON a.user_id = c.user_id
AND a.product_id = c.product_id
GROUP BY a.product_id;

This SQL query first performs a LEFT JOIN on the ad_clicks and cart_add tables, joining them based on their user_id and product_id. After the join, for each product, it calculates the click-through conversion rate by dividing the number of users who added the product to their cart by the total number of users who clicked the ad. The conversion rate is converted to float for accurate calculation.

To practice a similar SQL problem on DataLemur's free online SQL code editor, try this SQL interview question asked by Facebook:

SQL Question 7: Can you explain the concept of a constraint in SQL?

SQL constraints are used to specify rules for the data in a table. They can be applied to single or multiple fields in a table when the table is created, or after the table has been created using the ALTER TABLE command.

For example, say you had a database that stores ad campaign data from Roku's Google Analytics account.

Here's what some constraints could look like:

CREATE TABLE ad_campaigns (
    ad_id INTEGER PRIMARY KEY,
    ad_name VARCHAR(255) NOT NULL,
    start_date DATE NOT NULL,
    end_date DATE NOT NULL,
    budget DECIMAL(10,2) NOT NULL CHECK (budget > 0),
    cost_per_click DECIMAL(10,2) NOT NULL CHECK (cost_per_click > 0)
);

SQL Question 8: Filtering Customer Reviews Containing Specific Keywords

Consider that you're a data analyst at Roku. The product team is interested in filtering customer reviews that contain certain keywords in order to better understand the customers' preferences. Particularly, they are looking for reviews that contain the word "remote". Write an SQL query to fetch these reviews.

reviews Example Input:

Answer:

SELECT *
FROM reviews
WHERE review LIKE '%remote%'

This query scans the 'reviews' table and returns all reviews that contain the word "remote". The '%' on either side of 'remote' is a wildcard operator that matches any sequence of characters. Hence, the query will match reviews that contain "remote" anywhere in the review text, regardless of what text may come before or after it.

Preparing For The Roku SQL Interview

The key to acing a Roku SQL interview is to practice, practice, and then practice some more! Beyond just solving the above Roku SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like Microsoft, Google, Amazon, and tech startups.

Each DataLemur SQL question has multiple hints, full answers and crucially, there is an online SQL coding environment so you can instantly run your SQL query and have it graded.

To prep for the Roku SQL interview you can also be helpful to solve SQL questions from other tech companies like:

• Amazon SQL questions

• Apple SQL questions

• Netflix SQL questions

• Disney SQL questions

But if your SQL coding skills are weak, don't worry about going right into solving questions – go learn SQL with this SQL tutorial for Data Scientists & Analysts.

This tutorial covers topics including GROUP BY and finding NULLs – both of which show up often in Roku interviews.

Roku Data Science Interview Tips

What Do Roku Data Science Interviews Cover?

Besides SQL interview questions, the other types of problems covered in the Roku Data Science Interview are:

• Statistics & Probability Questions
• Coding Questions in Python or R
• Product Data Science Interview Questions
• Machine Learning Questions
• Behavioral Interview Questions

How To Prepare for Roku Data Science Interviews?

To prepare for Roku Data Science interviews read the book Ace the Data Science Interview because it's got:

• 201 interview questions taken from FAANG, tech startups, and Wall Street
• a crash course on Python, SQL & ML
• over 900+ 5-star reviews on Amazon