9 Paradox Interactive SQL Interview Questions (Updated 2024)
At Paradox Interactive, SQL is used all the damn time for analyzing gaming data patterns and optimizing user data for targeted marketing strategies. Because of this, Paradox Interactive LOVES to ask SQL coding questions during interviews for Data Science and Data Engineering positions.
So, to help you prepare for the Paradox Interactive SQL interview, here’s 9 Paradox Interactive SQL interview questions – can you solve them?
9 Paradox Interactive SQL Interview Questions
SQL Question 1: Calculate the monthly average rating for each game product
Paradox Interactive has a large portfolio of games and maintains a database of user reviews for each game. Each review has a user_id that submitted it, time of submission and the number of stars awarded by the user. The task is to write a SQL query that calculates the monthly average star rating for each game product.
Assuming that the data is stored in a table called , here is an example of how the input data might look:
Example Input:
| review_id | user_id | submit_date | product_id | stars |
|---|---|---|---|---|
| 6171 | 123 | 06/08/2022 00:00:00 | 50001 | 4 |
| 7802 | 265 | 06/08/2022 00:00:00 | 50002 | 4 |
| 5293 | 362 | 06/18/2022 00:00:00 | 50001 | 3 |
| 6352 | 192 | 07/26/2022 00:00:00 | 50002 | 3 |
| 4517 | 981 | 07/05/2022 00:00:00 | 50001 | 2 |
Answer:
We will use Window function to first calculate row wise monthly average, followed by group by over product and month to calculate individual average ratings
Next, with this result we have average rating for each row (Product and Month), now calculate average of these row wise averages for each Product and Month, this will yield the final results:
This query calculates the average star rating for each product on a monthly basis. The WINDOW function is used to create a partition for each product monthly and compute the average. The final result provides productID, month-year and average star rating across reviews for that month.
For more window function practice, try this Uber SQL problem on DataLemur's online SQL coding environment:
SQL Question 2: Filter Customer Game Purchases
As a Data Analyst at Paradox Interactive, you are tasked with identifying customers who purchased strategy games in 2021, and spent over $100 in total but never bought any DLCs (downloadable content).
You will be provided with sales data structured in two tables:
Example Input:
| purchase_id | customer_id | purchase_date | game_id | price |
|---|---|---|---|---|
| 1001 | 500 | 2021-02-01 | 8001 | 30 |
| 1002 | 500 | 2021-02-15 | 8020 | 40 |
| 1003 | 623 | 2021-04-07 | 8001 | 30 |
| 1004 | 623 | 2021-07-10 | 8040 | 20 |
| 1005 | 500 | 2021-08-03 | 8030 | 35 |
| 1006 | 623 | 2021-09-06 | 8001 | 30 |
| 1007 | 623 | 2021-11-15 | 8040 | 20 |
Example Input:
| dlc_id | customer_id | purchase_date | game_id | price |
|---|---|---|---|---|
| 2001 | 500 | 2021-03-15 | 8001 | 5 |
| 2002 | 500 | 2021-06-25 | 8011 | 8 |
| 2003 | 623 | 2021-09-03 | 8030 | 12 |
Answer:
This query filters to include only purchases from 2021 where the game_id corresponds to a strategy game and the customer_id does not appear in . We then group by and include only those customers whose total spending exceeds $100.
SQL Question 3: What does the function do, and when would you use it?
The COALESCE() function returns the first non-NULL value from a list of values. This function is often used to replace a NULL with some default value, so that you can then take a or of some column without NULLs messing things up.
For example, suppose you ran a customer satisfaction survey for Paradox Interactive and had statements like "I'd buy from Paradox Interactive again". In the survey, customers would then answer how strongly they agreed with a statement on a scale of 1 to 5 (strongly disagree, disagree, neutral, agreee, strongly agree).
Because attention spans are short, many customers skipped many of the questions, and thus our survey data might be filled with NULLs:
| customer_id | question_id | agree_scale |
|---|---|---|
| 101 | 1 | 4 |
| 101 | 2 | 5 |
| 202 | 1 | 4 |
| 202 | 2 | NULL |
| 303 | 1 | 5 |
| 303 | 2 | NULL |
Before doing further analytics on this customer survey data, you could replace the NULLs in the column with the value of (because that corresponds to the default 'neutral' answer) using the function:
This would result in the following:
| customer_id | question_id | agree_scale |
|---|---|---|
| 101 | 1 | 4 |
| 101 | 2 | 5 |
| 202 | 1 | 4 |
| 202 | 2 | 3 |
| 303 | 1 | 5 |
| 303 | 2 | 3 |
SQL Question 4: Calculate the Click-Through Rate for Paradox Interactive
Paradox Interactive, a company that designs digital games, is interested in understanding the effectiveness of their online marketing campaigns. They want to calculate the click-through rate (CTR) of their digital ads.
The click-through rate (CTR) is the ratio of users who click on a specific link to the number of total users who view a page, email, or advertisement. It is commonly used to measure the success of an online advertising campaign for a particular website.
Their marketing data is stored in two tables named and . The table logs each time a user is shown an ad. The table logs each time a user clicks on an ad.
Example Input:
| impression_id | user_id | ad_id | impression_time |
|---|---|---|---|
| 101 | 65 | 355 | 01/05/2022 10:00:00 |
| 102 | 215 | 421 | 01/06/2022 11:30:00 |
| 103 | 74 | 312 | 01/07/2022 08:15:00 |
| 104 | 211 | 421 | 01/08/2022 14:45:00 |
| 105 | 308 | 355 | 01/09/2022 16:30:00 |
Example Input:
| click_id | user_id | ad_id | click_time |
|---|---|---|---|
| 981 | 65 | 355 | 01/05/2022 10:05:00 |
| 987 | 211 | 421 | 01/08/2022 15:00:00 |
| 995 | 215 | 421 | 01/06/2022 15:00:00 |
Answer:
In the SQL above, we join the and tables on both and . We use a left join to include all impressions, regardless of whether they resulted in a click.
We then group by , resulting in a row for each ad. For each ad, we calculate the total number of impressions, the total number of clicks, and the click-through rate, which is the number of clicks divided by the number of impressions. The is needed to perform the division in floating point arithmetic, as PostgreSQL would otherwise do integer division.
With this query, Paradox Interactive can see the click-through rate for each ad and thus evaluate the effectiveness of each ad campaign.
To practice a similar SQL interview question on DataLemur's free interactive coding environment, attempt this SQL interview question asked by Facebook:
SQL Question 5: What are SQL constraints, and can you give some examples?
Constraints are just rules for your DBMS to follow when updating/inserting/deleting data.
Say you had a table of Paradox Interactive employees, and their salaries, job titles, and performance review data. Here's some examples of SQL constraints you could implement:
NOT NULL: This constraint could be used to ensure that certain columns in the employee table, such as the employee's first and last name, cannot contain NULL values.
UNIQUE: This constraint could be used to ensure that the employee ID is unique. This would prevent duplicate entries in the employee table.
PRIMARY KEY: This constraint could be used to combine the NOT NULL and UNIQUE constraints to create a primary key for the employee table. The employee ID could serve as the primary key.
FOREIGN KEY: This constraint could be used to establish relationships between the employee table and other tables in the database. For example, you could use a foreign key to link the employee ID to the department ID in a department table to track which department each employee belongs to.
CHECK: This constraint could be used to ensure that certain data meets specific conditions. For example, you could use a CHECK constraint to ensure that salary values are always positive numbers.
DEFAULT: This constraint could be used to specify default values for certain columns. For example, you could use a DEFAULT constraint to set the employee hire date to the current date if no value is provided when a new employee is added to the database.
SQL Question 6: Paradox Interactive Game Ratings
As a database analyst at Paradox Interactive, your boss has asked you to analyze player reviews across various games over the past 12 months. They are interested in knowing the average rating for each game on a monthly basis. This will help identify trends, as well as games that might be well received or require improvements based on player feedback.
Create a query that provides the average rating (stars) for each game (product_id) for each month in the year of 2022.
The reviews are stored in a table with the following structure:
Example Input:
| review_id | user_id | submit_date | product_id | stars |
|---|---|---|---|---|
| 613 | 995 | 01/15/2022 00:00:00 | 78659 | 4 |
| 1245 | 374 | 02/20/2022 00:00:00 | 19426 | 5 |
| 869 | 562 | 02/24/2022 00:00:00 | 78659 | 3 |
| 785 | 456 | 03/13/2022 00:00:00 | 19426 | 4 |
| 349 | 259 | 03/20/2022 00:00:00 | 78659 | 4 |
Answer:
This query groups the reviews by month and product ID, and then calculates the average star rating for each group. The WHERE clause limits the data to the year 2022. The EXTRACT function is used to get the month part of the submit_date. This information can be used by Paradox Interactive to identify trends in player feedback and pinpoint areas for potential improvements. The ORDER BY clause arranges the result by month and product ID for better analysis.
SQL Question 7: Can you explain the distinction between an inner and a full outer join?
An inner join returns only the rows that match the join condition between the two tables, whereas a full outer join returns all rows from both tables, including any unmatched rows.
Example:
Example:
SQL Question 8: Find Users with a Specific Email Domain
As a database manager at Paradox Interactive, you have access to the table which holds the company's user data. The table includes the users' emails among other details. The company is planning to send out some specific product offers to users whose email domain is "Gmail". Your task is to write an SQL query to filter all users whose email ends with "@gmail.com".
Provide:
- A list of user names and emails
- Sort the result in ascending order by user names
Example Input:
| user_id | user_name | |
|---|---|---|
| 123 | Michael | michael@yahoo.com |
| 265 | Emma | emma@gmail.com |
| 362 | Oliver | oliver@outlook.com |
| 192 | Sophia | sophia@gmail.com |
| 981 | Lucas | lucas@gmail.com |
Answer:
This SQL query filters out records from the 'users' table where the email ends with '@gmail.com'. The result is then sorted in ascending order by 'user_name'. Using the SQL LIKE keyword, we effectively searched for a specific pattern within a string field (in this case, the email field).
SQL Question 9: Calculating Player Stats
Paradox Interactive wants to calculate the stats for players from their game logs. Specifically, they want to calculate the player's batting average (hits/attempts), then round that average to the nearest hundredth. They have data on the number of hits and the number of attempts for each player's turn during a gaming session. The game logs also include the players' IDs and the session date.
Use the Players, GameSessions, and GameLog tables to calculate the batting average for each player on each date. Round the batting averages to the nearest hundredth (two decimal places). Include results for players and dates even if there were no hits or attempts (i.e., the number of attempts was zero). Treat these cases as a batting average of zero.
Example Input:
| player_id | player_name |
|---|---|
| 1001 | Player 1 |
| 1002 | Player 2 |
| 1003 | Player 3 |
Example Input:
| session_id | player_id | session_date |
|---|---|---|
| 101 | 1001 | 2022-07-04 |
| 102 | 1002 | 2022-07-04 |
| 103 | 1003 | 2022-07-05 |
Example Input:
| session_id | hits | attempts |
|---|---|---|
| 101 | 2 | 5 |
| 102 | 1 | 3 |
| 103 | 0 | 4 |
Answer:
Example Output:
| player_id | player_name | session_date | batting_average |
|---|---|---|---|
| 1001 | Player 1 | 2022-07-04 | 0.40 |
| 1002 | Player 2 | 2022-07-04 | 0.33 |
| 1003 | Player 3 | 2022-07-05 | 0.00 |
This PostgreSQL query calculates the batting average for each player as the total number of hits divided by the total number of attempts. Null attempts are replaced with 0, which triggers a null-batting-average but prevents division by zero, and null hits are replaced with 0 to keep these averages as zero as well. Batting averages are rounded to the nearest hundredth using the ROUND() function, and this is done for each player per session_date.
To practice a very similar question try this interactive Snapchat Sending vs. Opening Snaps Question which is similar for using percentages and performing a function to round up values or this Alibaba Compressed Mean Question which is similar for performing calculations and rounding to a specific decimal point.
Paradox Interactive SQL Interview Tips
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Paradox Interactive SQL interview is to solve as many practice SQL interview questions as you can!
Besides solving the above Paradox Interactive 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 hints to guide you, fully explained answers along with a discussion board to see how others solved it and most importantly, there is an online SQL coding environment so you can right in the browser run your SQL query and have it executed.
To prep for the Paradox Interactive SQL interview it is also useful to solve SQL problems from other tech companies like:
However, if your SQL skills are weak, don't worry about diving straight into solving questions – improve your SQL foundations with this DataLemur SQL Tutorial.
This tutorial covers topics including LAG window function and filtering data with boolean operators – both of which come up frequently in Paradox Interactive SQL interviews.
Paradox Interactive Data Science Interview Tips
What Do Paradox Interactive Data Science Interviews Cover?
In addition to SQL interview questions, the other types of problems covered in the Paradox Interactive Data Science Interview are:
- Statistics & Probability Questions
- Python or R Programming Questions
- Business Sense and Product-Sense Questions
- Machine Learning Questions
- Behavioral & Resume-Based Questions
How To Prepare for Paradox Interactive Data Science Interviews?
The best way to prepare for Paradox Interactive Data Science interviews is by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from FAANG, tech startups, and Wall Street
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