At Ubisoft, SQL is used all the damn time for analyzing gaming behavioral data for product improvements. Unsurprisingly this is why Ubisoft typically asks SQL problems during interviews for Data Science, Data Engineering and Data Analytics jobs.
So, to help you ace the Ubisoft SQL interview, we've collected 8 Ubisoft SQL interview questions – can you solve them?
Ubisoft wants to identify its "power users" to understand their patterns better and improve its offerings. Define a "power user" as a player who has the highest number of gameplay hours logged. The goal is to write a SQL query that'll return the top 10 users with the most gameplay hours logged during the past year.
log_id | user_id | game_id | start_time | end_time |
---|---|---|---|---|
1 | 4321 | 123 | 2021-09-15 10:00:00 | 2021-09-15 12:00:00 |
2 | 1234 | 123 | 2021-09-15 11:00:00 | 2021-09-15 14:00:00 |
3 | 2345 | 456 | 2021-09-15 12:00:00 | 2021-09-15 14:00:00 |
4 | 4321 | 789 | 2021-09-15 14:00:00 | 2021-09-15 18:00:00 |
5 | 1234 | 123 | 2021-09-16 09:00:00 | 2021-09-16 13:00:00 |
user_id | total_hours |
---|---|
4321 | 6.00 |
1234 | 7.00 |
2345 | 2.00 |
This query calculates the total gameplay hours for each user in the last year by subtracting the start time from the end time of each log. It groups the results by user_id, orders in descending order of total_hours and then provides the top 10 users, thus identifying Ubisoft's power users.
To practice a related customer analytics question on DataLemur's free online SQL code editor, try this Microsoft SQL Interview problem:
Given a dataset that holds user reviews for Ubisoft games, write a SQL query that ranks games by their average monthly ratings starting from the highest. The dataset includes the review_id, user_id, submit_date, product_id, and stars given by each user.
Below are the sample reviews:
review_id | user_id | submit_date | product_id | stars |
---|---|---|---|---|
6171 | 123 | 2019-06-30 15:00:00 | 50001 | 3 |
7802 | 265 | 2019-06-30 14:00:00 | 50001 | 4 |
5293 | 362 | 2019-07-01 10:00:00 | 50001 | 5 |
6352 | 192 | 2019-07-01 10:00:00 | 50002 | 3 |
4517 | 981 | 2019-07-01 11:00:00 | 50002 | 4 |
year_month | product_id | avg_stars | rank |
---|---|---|---|
2019-07 | 50001 | 5.00 | 1 |
2019-07 | 50002 | 3.50 | 2 |
2019-06 | 50001 | 3.50 | 3 |
Here is the required PostgreSQL query:
This query works by using a window function to calculate the average game ratings (stars) for each product_id on a monthly basis. It then ranks these averages in descending order. The clause groups records by product_id and month, while the function reformats the date into a 'Year-Month' format for easier distinction. The final RANK() function assigns a rank to each product_id based on its average monthly score.
To solve a related window function SQL problem on DataLemur's free online SQL code editor, solve this Amazon BI Engineer interview question:
A join in SQL allows you to retrieve data from multiple tables and combine it into a single set of results.
In SQL, there are four different types of JOINs. To demonstrate each kind, imagine you had two database tables, an advertising campaigns table which had information on Google Ads keywords and how much was bid for each keyword, and a sales table, which has data on how many products were sold and which Google Ads keyword drove that sale.
: An INNER JOIN retrieves rows from both tables where there is a match in the shared key or keys. For example, an INNER JOIN between the table and the table could be performed using the keyword column as the shared key. This would retrieve only the rows where the keyword in the table matches the keyword in the table.
: A LEFT JOIN retrieves all rows from the left table (in this case, the table) and any matching rows from the right table (the Sales table). If there is no match in the right table, values will be returned for the right table's columns.
: A RIGHT JOIN retrieves all rows from the right table (in this case, the Sales table) and any matching rows from the left table (the table). If there is no match in the left table, values will be returned for the left table's columns.
: A FULL OUTER JOIN retrieves all rows from both tables, regardless of whether there is a match in the shared key or keys. If there is no match, values will be returned for the columns of the non-matching table.
Ubisoft, a renowned video game company, wants to manage the sale of their games in multiple countries. They have different types of games belonging to various genres like Action, Adventure, Strategy, etc. Each game can belong to multiple genres. They also need to keep track of sales in each country. Channels (like direct sale or via some platform) also play a crucial part. They want to design a database to handle this scenario. Please design a database schema that captures all this information and can answer questions like "Which genre of games perform best in Country X?"
Consider the following tables:
game_id | game_name |
---|---|
1 | Assassin's Creed |
2 | Far Cry |
3 | Watch Dogs |
genre_id | genre_name |
---|---|
1 | Action |
2 | Adventure |
3 | Strategy |
game_id | genre_id |
---|---|
1 | 1 |
1 | 2 |
2 | 1 |
3 | 2 |
3 | 3 |
sale_id | game_id | country | channel | units_sold |
---|---|---|---|---|
1 | 1 | USA | Direct | 5000 |
2 | 1 | UK | Platform | 3500 |
3 | 2 | USA | Direct | 4000 |
4 | 2 | Germany | Platform | 2000 |
5 | 3 | USA | Direct | 7000 |
Write a PostgreSQL query to find out the best performing genre in the USA.
This SQL query joins the three tables , , and based on the relations they have with each other. It then groups the result by the genre name and sums the units sold for each genre. Finally, it orders the result in decreasing order of units sold and limits the result to the top 1 genre.
Stored procedures are a lot like functions in programming. They're used to encapsulate and organize business logic into one unit of code, and they can accept multiple input parameters and return multiple output values.
For example, if you were a Data Analyst at Ubisoft working on a HR analytics project, you might create a stored procedure to calculate the average salary for a given department:
To call this stored procedure and find the average salary for the Data Analytics department you'd write the following query:
Given a customer records database for the company Ubisoft, write a SQL query that retrieves customer records that have purchased games matching a specific genre. Specifically, filter for customers who have purchased games with 'Action' genre.
For the scope of this question, let's assume two tables: having customer information, and holding information on games each customer has purchased.
customer_id | first_name | last_name | |
---|---|---|---|
101 | John | Doe | johndoe@example.com |
102 | Jane | Smith | janesmith@example.com |
103 | Sam | Brown | sambrown@example.com |
104 | Emma | Johnson | emmajohnson@example.com |
105 | Oliver | Williams | oliverwilliams@example.com |
purchase_id | customer_id | game_title | genre |
---|---|---|---|
201 | 101 | Assassin's Creed | Action |
202 | 102 | Just Dance | Music |
203 | 101 | Far Cry | Action |
204 | 104 | The Division | Action |
205 | 102 | Watch Dogs | Action |
To retrieve this information from the database, we can use an SQL JOIN operator to combine rows from two or more tables, based on a related column between them, here is :
This query will give us a list of customers who have purchased games with 'Action' genre.
first_name | last_name | |
---|---|---|
John | Doe | johndoe@example.com |
Emma | Johnson | emmajohnson@example.com |
Jane | Smith | janesmith@example.com |
The and operators are both used to filter data based on certain criteria, but they work in different ways. is used to select values within a range, while is used to select values that match a list.
For instance, if you have a table called that contains the salary of each employee, along with which country they reside in, you could use the operator to find all employees who make between 160k:
To find all employees that reside in France and Germany, you could use the operator:
You are a data analyst for Ubisoft, a company that develops and sells video games. Ubisoft keeps a record of the games it sales in a PostgreSQL database with multiple tables. The and tables are defined as follows:
The table has the following structure:
game_id(p.k) | game_name | release_date | platform | genre |
---|---|---|---|---|
135 | Assassin's Creed Unity | 11/13/2014 | PS4 | Action-adventure |
246 | Rainbow Six Siege | 12/01/2015 | PC | First-person shooter |
317 | Watch Dogs 2 | 11/15/2016 | Xbox One | Action-adventure |
418 | Far Cry 5 | 03/27/2018 | PS4 | First-person shooter |
529 | Assassin's Creed Odyssey | 10/05/2018 | PC | Action-adventure |
The table has the following structure:
customer_id(p.k) | game_id(f.k) | purchase_date | rating |
---|---|---|---|
1590 | 135 | 11/20/2014 | 8 |
2650 | 246 | 12/10/2015 | 9 |
3170 | 317 | 11/24/2016 | 7 |
4180 | 246 | 01/09/2016 | 8 |
5290 | 529 | 11/10/2018 | 10 |
You are interested in getting a list of all Ubisoft games and the average customer rating. Your task is to join the and tables, aggregate the average rating by game name, and return the list in ascending order of game names.
Your PostgreSQL query would be something like:
This query is utilizing the to merge the and tables based on the field. We are then using the clause to separate games based on their names. The function is used to calculate the mean customer rating for each game. The clause is used to then sort the games in ascending order by their names.
When you run the query above, you will get an output that lists each game by name and gives an average customer rating.
Because join questions come up routinely during SQL interviews, take a stab at this interactive Snapchat Join SQL question:
The best way to prepare for a Ubisoft SQL interview is to practice, practice, practice. Beyond just solving the earlier Ubisoft SQL interview questions, you should also solve the 200+ SQL questions from real Data Science & Analytics interviews which come from companies like Netflix, Airbnb, and Amazon.
Each interview question has multiple hints, step-by-step solutions and crucially, there's an interactive SQL code editor so you can right online code up your SQL query answer and have it executed.
To prep for the Ubisoft SQL interview it is also helpful to solve SQL problems from other tech companies like:
In case your SQL coding skills are weak, don't worry about jumping right into solving questions – strengthen your SQL foundations with this interactive SQL tutorial.
This tutorial covers topics including CTE vs. Subquery and CASE/WHEN statements – both of which come up frequently during Ubisoft SQL assessments.
Besides SQL interview questions, the other types of problems to prepare for the Ubisoft Data Science Interview are:
The best way to prepare for Ubisoft Data Science interviews is by reading Ace the Data Science Interview. The book's got: