Analyze User Flags and Review Outcomes for Moderation Prioritization

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PracHub provides an interactive SQL console where you can write and test queries against real database schemas. Get instant feedback and compare your solution with the expected output.

Q: What difficulty level is this coding question?

This is a Medium difficulty Data Manipulation (SQL/Python) question, commonly asked during Technical Screen rounds at Google.

Q: What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Google during technical interviews.

Question

UserFlags

+---------------+--------------+----------+---------+
| User_FirstName| User_LastName| Video_ID | Flag_ID |
+---------------+--------------+----------+---------+
| Alice         | Zhang        | v1       | f101    |
| Bob           | Singh        | v1       | f102    |
| Alice         | Zhang        | v2       | f103    |
| Carol         | Lee          | v3       | f104    |
| Bob           | Singh        | v3       | f105    |
+---------------+--------------+----------+---------+

​

FlagReviews

+----------+---------+--------------+-----------------+
| Video_ID | Flag_ID | Reviewed_date| Reviewed_outcome|
+----------+---------+--------------+-----------------+
| v1       | f101    | 2023-01-02   | APPROVED        |
| v1       | f102    | 2023-01-03   | REJECTED        |
| v2       | f103    | 2023-01-05   | APPROVED        |
| v3       | f104    | 2023-01-04   | APPROVED        |
| v3       | f105    | NULL         | NULL            |
+----------+---------+--------------+-----------------+

##### Scenario

YouTube Trust & Safety team wants to analyze user-generated video flags and their review outcomes to prioritize moderation resources.

##### Question

Q1. Given table UserFlags(User_FirstName, User_LastName, Video_ID, Flag_ID), write a SQL query that returns, for every Video_ID, the number of flags submitted by distinct users. Q2. Using UserFlags and FlagReviews(Video_ID, Flag_ID, Reviewed_date, Reviewed_outcome), find the count of flags reviewed by YouTube for the single video that received the highest total number of user flags. Q3. Combining both tables, determine which user (concatenate first and last name) flagged the greatest number of videos that were ultimately APPROVED by YouTube. Q4. Write a query that lists every row from any provided table where at least one column contains NULL.

##### Hints

Think joins, distinct counts, grouping, and IS NULL filters. For Q3, count unique Video_IDs per user where Reviewed_outcome = 'APPROVED'.

PracHub · Accepted Answer

This question evaluates a data scientist's proficiency in SQL-based data manipulation, including joins, distinct counting and grouping, aggregations, string concatenation for user identity, and NULL handling across event and review tables.

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