8 Take Two Interactive SQL Interview Questions (Updated 2024)
At Take Two Interactive, SQL is used across the company for analyzing player behavior data to enhance game experiences, and managing large databases for maintaining gameplay and customer transaction records. That's why Take 2 Interactive almost always asks SQL coding questions during interviews for Data Science, Analytics, and & Data Engineering jobs.
So, to help you ace the Take 2 Interactive SQL interview, we've curated 8 Take 2 Interactive SQL interview questions – how many can you solve?
8 Take 2 Interactive SQL Interview Questions
SQL Question 1: Calculate the monthly average rating for each product
Take 2 Interactive is a leading game developer and distributor. They have a table which keeps track of all user reviews for their various products. Each review has a , , (timestamp containing date and hour), , and (1-5).
They want to better understand their users' feedback by calculating the average rating for each product on a monthly basis. Write a SQL query to calculate the monthly average stars for each product.
Example Input:
| review_id | user_id | submit_date | product_id | stars |
|---|---|---|---|---|
| 6171 | 123 | 06/08/2022 00:00:00 | 50001 | 4 |
| 7802 | 265 | 06/10/2022 00:00:00 | 69852 | 4 |
| 5293 | 362 | 06/18/2022 00:00:00 | 50001 | 3 |
| 6352 | 192 | 07/26/2022 00:00:00 | 69852 | 3 |
| 4517 | 981 | 07/05/2022 00:00:00 | 69852 | 2 |
Answer:
The following SQL query will solve the problem
The query uses the GROUP BY statement to group the data by the product and the month. Then, it calculates the average rating for each product per month using the AVG() aggregate function. The query also orders the results by month and product_id for easy viewing. The date_part function is used to extract the month portion from the submit_date timestamp.
To solve a related window function SQL problem on DataLemur's free interactive coding environment, solve this Amazon SQL Interview Question:
SQL Question 2: Track and Analyze Video Game Sales
As a Data Analyst at Take-Two Interactive, you are tasked to help track and analyze video game sales. To do this, you need to work with a database that contains two tables: and . Each game has a unique and belongs to a .
The table contains the , , and . The table has a , , , , and .
You are expected to find out the total revenue per category for the month of June 2022. The revenue is calculated by multiplying by for each transaction.
Sample Table:
| game_id | game_name | category |
|---|---|---|
| 1 | Red Dead Redemption II | Action |
| 2 | Grand Theft Auto V | Action |
| 3 | NBA 2K20 | Sports |
| 4 | Civilization VI | Strategy |
Sample Table:
| transaction_id | game_id | sale_date | units_sold | sale_price |
|---|---|---|---|---|
| 1001 | 1 | 06/05/2022 09:00:00 | 12 | 60 |
| 1002 | 2 | 06/07/2022 15:00:00 | 8 | 20 |
| 1003 | 3 | 06/18/2022 16:00:00 | 10 | 50 |
| 1004 | 1 | 06/21/2022 10:30:00 | 6 | 60 |
| 1005 | 4 | 06/30/2022 18:00:00 | 5 | 40 |
Answer:
To solve this problem, we can join the two tables on and filter for transactions that occurred in June 2022. We then group the data by category and calculate the total revenue.
Here is the PostgreSQL query that could be used:
This query first connects the 'sales' table to the 'games' table via a JOIN clause. The WHERE clause filters out records that are not in June 2022. Finally, the GROUP BY clause and the SUM function compute the total revenue by game category.
SQL Question 3: What's the main difference between ‘BETWEEN’ and ‘IN’ operators?
is used to select rows that match a range of values, whereas the operator checks for values in a specified list of values.
For example, say you were a Data Analyst at Take 2 Interactive and had a table of advertising campaign data.
To find campaigns with between 10k in spend, you could use BETWEEN`:
To find ad campaigns that were run on Facebook and Google's Display Network, you could use :
SQL Question 4: Filtering Customer Records
Imagine you are a data analyst at Take 2 Interactive, an American video game publisher, and you have been tasked to generate a report of active customers who have made at least one purchase in the past year and have spent over $500 in total. For each customer, the report should include their customer_id, full name, total amount spent, and their most recent purchase date.
Your task is to write a SQL query to filter the customer records and come up with this report using the following tables:
Example Input:
| customer_id | full_name | date_joined | |
|---|---|---|---|
| 101 | John Smith | john.smith@example.com | 2018-05-05 |
| 102 | Jane Doe | jane.doe@example.com | 2019-10-19 |
| 103 | Charlie Brown | charlie.brown@example.com | 2021-01-01 |
| 104 | Alice Johnson | alice.johnson@example.com | 2017-02-02 |
Example Input:
| order_id | customer_id | order_date | total_amount |
|---|---|---|---|
| 2001 | 101 | 2022-01-18 | $25.99 |
| 2002 | 101 | 2022-06-25 | $49.99 |
| 2003 | 102 | 2021-12-08 | $25.99 |
| 2004 | 102 | 2021-05-21 | $75.99 |
| 2005 | 103 | 2022-07-10 | $99.99 |
| 2006 | 104 | 2021-07-15 | $399.99 |
| 2007 | 101 | 2022-09-30 | $499.99 |
Answer:
This query starts by joining the and tables on . Then, it filters the records to include only the customers that have made a purchase in the last year and whose total spent amount is more than $500. The statement is used to aggregate the data per customer. Finally, the results are sorted in descending order of the date of the most recent purchase.
SQL Question 5: Can you give some examples of when denormalization might be a good idea?
Database normalization is a good idea to implement because it can reduce redundancy which can improve performance and database flexibility.
By dividing larger tables into smaller, more modular and specific tables which are linked via foreign keys, this can usually improve the speed of some queries because it minimizes the amount of random data the query has to sift through. However, it's not always 100% true that there is a query speed-up, because joins are an expensive operation.
Nonetheless, by reducing redundancy, besides improved performance you also get more database flexibility. By making tables more modular and reducing overlap, you're able to more easily, since one table can be changed more effortlessly without affecting others. This makes it easier to adapt the database schema to Take 2 Interactive's evolving business needs.
SQL Question 6: Calculate Click-through Conversion Rate
As a data analyst for Take 2 Interactive, you are asked to analyze the click-through rate from viewing a game to adding it to the cart. The company wants to understand which games are performing well in terms of attracting interest and turning that interest into potential purchases.
You have two tables. The first one, 'game_views', records each time a user views a product page for a game. The second table, 'cart_adds', records each time a user adds a game to their cart.
For this question, calculate the click-through conversion rate for each game, which is defined as the number of times a game is added to a cart divided by the number of times that game's product page is viewed.
Example Input
| view_id | user_id | view_date | game_id |
|---|---|---|---|
| 1187 | 316 | 16/08/2022 08:40:00 | 39001 |
| 7352 | 178 | 16/08/2022 11:24:00 | 28472 |
| 7213 | 452 | 16/08/2022 14:22:00 | 39001 |
| 1267 | 619 | 16/08/2022 16:20:00 | 28472 |
| 3487 | 932 | 17/08/2022 09:12:00 | 49031 |
Example Input
| add_id | user_id | add_date | game_id |
|---|---|---|---|
| 2918 | 316 | 16/08/2022 08:45:00 | 39001 |
| 8532 | 619 | 16/08/2022 16:28:00 | 28472 |
| 1829 | 452 | 17/08/2022 10:32:00 | 39001 |
| 4521 | 992 | 17/08/2022 11:07:00 | 49031 |
Answer:
This SQL query calculates the click-through conversion rate by joining the 'game_views' and 'cart_adds' tables on the 'user_id' and 'game_id' fields, then for each game asks for the number of distinct 'add_id' from 'cart_adds' divided by the number of distinct 'view_id' from 'game_views'. We multiply the numerator by 1.0 to force the division result into a float for more accurate results.
To solve another question about calculating rates, try this SQL interview question from TikTok within DataLemur's interactive SQL code editor:
SQL Question 7: Can you explain the purpose of the constraint and give an example of when you might use it?
The CHECK constraint is used to enforce rules on the data in a specific column. If a row is inserted or updated with data that does not follow the CHECK constraint's rule, the operation will fail. The CHECK constraint is often used with other constraints, such as NOT NULL or UNIQUE, to ensure that data meets certain conditions. You may want to use a CHECK constraint in your database if you want to maintain the quality and reliability of your data by ensuring that it meets specific criteria.
For example, you might use a CHECK constraint to ensure that a column contains only positive numbers, or that a date is within a certain range.
SQL Question 8: Average Daily Revenue Generated from Each Game
In your role at Take 2 Interactive, you are often required to monitor the daily average revenue generated from each game. Using the data available in the "sales" table, can you write a query to determine the daily average revenue for each game in the month of June 2022? Please assume revenue is measured in USD.
Example Input:
| sale_id | game_id | sale_date | revenue |
|---|---|---|---|
| 1675 | 101 | 06/08/2022 00:00:00 | 45.99 |
| 8930 | 202 | 06/10/2022 00:00:00 | 59.99 |
| 5126 | 101 | 06/18/2022 00:00:00 | 45.99 |
| 7942 | 203 | 07/26/2022 00:00:00 | 49.99 |
| 3729 | 201 | 07/05/2022 00:00:00 | 39.99 |
Answer:
In this query, we are selecting the game_id and date (with time portion removed) from the "sales" table. We are then computing the average revenue for each game on each date. The WHERE clause restricts the data to only include sales made in the month of June 2022. The grouping is done by game_id and date to calculate the average daily revenue for each game, and the result is then sorted by game_id and date.
Preparing For The Take 2 Interactive SQL Interview
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Take 2 Interactive SQL interview is to solve as many practice SQL interview questions as you can!
Beyond just solving the earlier Take 2 Interactive SQL interview questions, you should also solve the 200+ SQL questions on DataLemur which come from companies like Google, Microsoft and Silicon Valley startups.
Each DataLemur SQL question has multiple hints, detailed solutions and most importantly, there is an interactive SQL code editor so you can easily right in the browser your SQL query and have it checked.
To prep for the Take 2 Interactive SQL interview it is also a great idea to solve interview questions from other gaming tech companies like:
However, if your SQL 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 how window functions work and math functions in SQL – both of which pop up often in Take 2 Interactive SQL assessments.
Take 2 Interactive Data Science Interview Tips
What Do Take 2 Interactive Data Science Interviews Cover?
In addition to SQL query questions, the other types of problems covered in the Take 2 Interactive Data Science Interview are:
- Probability & Stats Questions
- Python Pandas or R Coding Questions
- Product Metrics Interview Questions
- Machine Learning and Predictive Modeling Questions
- Behavioral Interview Questions
How To Prepare for Take 2 Interactive Data Science Interviews?
The best way to prepare for Take 2 Interactive Data Science interviews is by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from FAANG (FB, Apple, Amazon, Netflix, Google)
- A Refresher on SQL, Product-Sense & ML
- Great Reviews (900+ reviews, 4.5-star rating)