9 Light & Wonder SQL Interview Questions (Updated 2024)
At Light & Wonder, SQL is used for managing gaming datasets, helping them to analyze player behavior and preferences. It also allows them to optimize gaming experiences and promotions based on what players enjoy most, enhancing customer satisfaction and engagement, that is the reason why Light & Wonder asks SQL questions in interviews for Data Analytics, Data Science, and Data Engineering jobs.
Thus, to help you practice for the Light & Wonder SQL interview, we'll cover 9 Light & Wonder SQL interview questions in this article.
9 Light & Wonder SQL Interview Questions
SQL Question 1: Calculate Monthly Average Ratings for Each Product
Light & Wonder is a company that sells various products and allows users to review and rate them. The review ratings range from 1 to 5 stars. The company maintains a table that keeps a record of each review submitted by the users. The table has the following columns:
- : Unique ID of the review
- : Unique ID of the user who reviewed
- : Date the review was submitted
- : Unique ID of the product that was reviewed
- : Star rating given by the user
Your task is to write a SQL query to calculate the monthly average star rating for each product based on the reviews submitted.
Below is a snapshot of how your table looks like:
Example Input:
| review_id | user_id | submit_date | product_id | stars |
|---|---|---|---|---|
| 6171 | 123 | 2022-08-06 | 50001 | 4 |
| 7802 | 265 | 2022-10-06 | 69852 | 4 |
| 5293 | 362 | 2022-18-06 | 50001 | 3 |
| 6352 | 192 | 2022-26-07 | 69852 | 3 |
| 4517 | 981 | 2022-05-07 | 69852 | 2 |
Answer:
Before writing the SQL query, make sure that the field is of date or timestamp data type.
Here's a PostgreSQL query that uses window function to compute the monthly average star rating:
Example Output:
| mth | product_id | avg_stars |
|---|---|---|
| 6 | 50001 | 3.5 |
| 6 | 69852 | 4.0 |
| 7 | 69852 | 2.5 |
The query first extracts the month from the field using the function. Then, it calculates the average for each for the extracted months using the function. The clause is used along with to compute the average for each product per month. Finally, the query orders the result by and .
To practice another window function question on DataLemur's free interactive coding environment, solve this Google SQL Interview Question:
Dive into Light & Wonder's newsroom and discover the exciting developments that are shaping the future of technology and innovation! Keeping up with Light & Wonder's updates can help you understand the trends that are driving change in the tech landscape.
SQL Question 2: 2nd Largest Salary
Given a table of Light & Wonder employee salaries, write a SQL query to find the 2nd highest salary among all employees.
Light & Wonder Example Input:
| employee_id | salary |
|---|---|
| 1 | 2500 |
| 2 | 800 |
| 3 | 1000 |
| 4 | 1200 |
Example Output:
| second_highest_salary |
|---|
| 1200 |
You can solve this problem directly within the browser on DataLemur:
Answer:
You can find a detailed solution with hints here: 2nd Highest Salary.
SQL Question 3: Can you describe the difference between a correlated and 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 Light & Wonder 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 4: Filter Customer Records for Light & Wonder
Given the table that includes information on each customer's city and their subscription status at Light & Wonder, write a SQL query to filter records down to only customers who live in either Seattle or San Francisco and have a current, active subscription. The relevant columns are (an integer), (a string), and (a string, where 'Active' means the customer is currently subscribed and 'Inactive' means they are not).
Example Input:
| customer_id | city | subscription_status |
|---|---|---|
| 1 | Seattle | Active |
| 2 | San Francisco | Inactive |
| 3 | New York | Active |
| 4 | Seattle | Inactive |
| 5 | San Francisco | Active |
| 6 | Los Angeles | Active |
| 7 | Seattle | Active |
| 8 | San Francisco | Inactive |
Example Output:
| customer_id | city | subscription_status |
|---|---|---|
| 1 | Seattle | Active |
| 5 | San Francisco | Active |
| 7 | Seattle | Active |
Answer:
This query filters the 'customers' table for records where the customer's city is either Seattle or San Francisco () and the customer's subscription status is 'Active' (). If both of these conditions are true for a record, that record is returned in the output.
SQL Question 5: What does the constraint do?
A is a field in a table that references the of another table. It creates a link between the two tables and ensures that the data in the field is valid.
Say for example you had sales analytics data from Light & Wonder's CRM (customer-relationship management) tool.
The constraint ensures that the data in the field of the table is valid, and prevents the insertion of rows in the table that do not have corresponding entries in the table. It also helps to enforce the relationship between the two tables and can be used to ensure that data is not deleted from the table if there are still references to it in the table.
SQL Question 6: Calculate Average Light Intensity
Light & Wonder is a company that specializes in manufacturing and selling a variety of lighting solutions including smart bulbs which save usage data. Every smart bulb sends a monthly report of its daily average light intensity. As an analyst at Light & Wonder, can you write a SQL query to find the average daily light intensity for each smart bulb for the last month?
Example Input:
| record_id | bulb_id | record_date | daily_light_intensity |
|---|---|---|---|
| 1 | 001 | 2022-11-01 | 300 |
| 2 | 001 | 2022-11-02 | 360 |
| 3 | 001 | 2022-11-03 | 320 |
| 4 | 002 | 2022-11-01 | 250 |
| 5 | 002 | 2022-11-02 | 500 |
| 6 | 002 | 2022-11-03 | 480 |
| 7 | 001 | 2022-12-01 | 250 |
| 8 | 002 | 2022-12-01 | 500 |
Example Output:
| bulb_id | Avg_light_intensity_for_nov |
|---|---|
| 001 | 326.6667 |
| 002 | 410 |
Answer:
This query firstly filters for records from the month of November. It then groups the records by the bulb id and calculates the average daily light intensity for each bulb. The function is used to compute the average light intensity, and the clause divides the data into groups by bulb id. The clause is used to limit the records to November only. With this query we can assess and compare the average daily use / intensity of each smart bulb for a specific time period. This can be beneficial for product improvement and customer usage patterns analysis.
To practice a very similar question try this interactive Google Odd and Even Measurements Question which is similar for calculating with IoT device data or this Amazon Average Review Ratings Question which is similar for averaging metrics over time.
SQL Question 7: Can you explain the concept of a constraint in SQL?
Constraints are just rules your DBMS has to follow when updating/inserting/deleting data.
Say you had a table of Light & Wonder products and a table of Light & Wonder customers. Here's some example SQL constraints you'd use:
NOT NULL: This constraint could be used to ensure that certain columns in the product and customer tables, such as the product name and customer email address, cannot contain NULL values.
UNIQUE: This constraint could be used to ensure that the product IDs and customer IDs are unique. This would prevent duplicate entries in the respective tables.
PRIMARY KEY: This constraint could be used to combine the and constraints to create a primary key for each table. The product ID or customer ID could serve as the primary key.
FOREIGN KEY: This constraint could be used to establish relationships between the Light & Wonder product and customer tables. For example, you could use a to link the customer ID in the customer table to the customer ID in the product table to track which products each customer has purchased.
CHECK: This constraint could be used to ensure that certain data meets specific conditions. For example, you could use a constraint to ensure that Light & Wonder product prices are always positive numbers.
DEFAULT: This constraint could be used to specify default values for certain columns. For example, you could use a constraint to set the customer registration date to the current date if no value is provided when a new customer is added to the database.
SQL Question 8: Calculating Click-through-rates for Light & Wonder
Light & Wonder, a growing digital goods company, is keen on analyzing clickthrough conversion rates from viewing a product to adding a product to the cart. The company wants to better understand which products have the highest conversion rates to improve their marketing efforts.
Given two tables, and , provide the PostgreSQL solution that calculates click-through-rates for each product, i.e., the number of times a product was added to the cart after being viewed by clicking an ad. The result should contain and .
Example Input:
| product_id | user_id | view_time |
|---|---|---|
| 101 | 123 | 2022-07-01 10:00:00 |
| 102 | 256 | 2022-07-02 12:00:00 |
| 101 | 658 | 2022-07-02 15:00:00 |
| 103 | 489 | 2022-07-03 13:00:00 |
| 101 | 954 | 2022-07-03 18:00:00 |
Example Input:
| product_id | user_id | cart_add_time |
|---|---|---|
| 101 | 123 | 2022-07-01 10:20:00 |
| 102 | 256 | 2022-07-02 12:30:00 |
| 103 | 489 | 2022-07-03 13:20:00 |
Answer:
This PostgreSQL solution works by joining the table with the table based on and . It then calculates the as the ratio of the times a product was added to the cart after being viewed, to the total number of times it was viewed. The result is multiplied by 100 to get a percentage. The function is used to restrict the result to two decimal places for easier interpretation.
To practice a similar SQL interview question on DataLemur's free interactive SQL code editor, solve this SQL interview question asked by Facebook:
SQL Question 9: Calculate Total Sales of Each Product Per Month
As an analyst in Light & Wonder, a company that sells a variety of lighting fixtures, it is your task to calculate the total sales of each product per month.
Light & Wonder has table with the following structure:
Example Input:
| sale_id | product_id | sale_date | unit_sold | unit_price |
|---|---|---|---|---|
| 101 | P001 | 2022-08-01 | 10 | 20.00 |
| 102 | P002 | 2022-08-12 | 5 | 30.00 |
| 103 | P001 | 2022-08-20 | 8 | 20.00 |
| 104 | P003 | 2022-09-05 | 12 | 15.00 |
| 105 | P002 | 2022-09-10 | 6 | 30.00 |
You need to write a SQL query that will return a table with each row representing a product and a month, and columns for the product_id, the month of the sales, and the total sales of the product for that month.
Expected Output:
| mth | product | total_sales |
|---|---|---|
| 08 | P001 | 360.00 |
| 08 | P002 | 150.00 |
| 09 | P003 | 180.00 |
| 09 | P002 | 180.00 |
Answer:
This query first extracts the month from the sale_date using the function. It then groups the sales by product and month. Within each group, it calculates the total sales by multiplying the number of units sold by the unit price and then summing up the results. Lastly, it sorts the output by month in ascending order and total sales in descending order.
Light & Wonder SQL Interview Tips
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 earlier Light & Wonder SQL interview questions, you should also solve the 200+ DataLemur SQL Interview Questions which come from companies like Google, Facebook, Microsoft and Amazon.
Each problem on DataLemur has multiple hints, detailed solutions and best of all, there's an interactive SQL code editor so you can right in the browser run your query and have it graded.
To prep for the Light & Wonder SQL interview you can also be wise to solve SQL problems from other hospitality and restaurant companies like:
- Brinker International SQL questions
- Boyd Gaming SQL questions
- Texas Roadhouse SQL questions
- Wynn Resorts SQL questions
But if your SQL foundations are weak, forget about going right into solving questions – go learn SQL with this free SQL for Data Analytics course.
This tutorial covers topics including math functions like ROUND()/CEIL() and handling NULLs in SQL – both of these come up frequently during Light & Wonder SQL interviews.
Light & Wonder Data Science Interview Tips
What Do Light & Wonder Data Science Interviews Cover?
In addition to SQL query questions, the other question categories to prepare for the Light & Wonder Data Science Interview are:
- Experimental Design & AB Testing Questions
- Python or R Coding Questions
- Product Analytics Questions
- Machine Learning Questions
- Behavioral Questions focussed on Light & Wonder culture and values
How To Prepare for Light & Wonder Data Science Interviews?
To prepare for Light & Wonder Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions taken from Facebook, Google, & Amazon
- a refresher covering SQL, AB Testing & ML
- over 1000+ reviews on Amazon & 4.5-star rating
Also focus on the behavioral interview – prepare for that using this Behavioral Interview Guide for Data Scientists.