Compute view prevalence from views and labels
Company: Meta
Role: Data Scientist
Category: Data Manipulation (SQL/Python)
Difficulty: Medium
Interview Round: Technical Screen
Given the tables below, write SQL to compute view prevalence of violating content. Use “today” = 2025-09-01 and report the last 7 days (2025-08-26 to 2025-09-01 inclusive). Provide two variants per country and day: Ex-ante prevalence counts a view as violating only if a VIOLATION decision existed at or before the view timestamp; Ex-post prevalence uses the final decision regardless of when it was detected. Then, list the top 3 creators by ex-post violating_view_share over this window and break down their violating views by surface. Schema and small samples:
Tables:
content_views(view_ts TIMESTAMP, user_id INT, content_id INT, surface VARCHAR, country VARCHAR)
content_moderation(content_id INT, decision VARCHAR, violation_type VARCHAR, detected_ts TIMESTAMP, source VARCHAR, is_removed BOOLEAN)
content(content_id INT, creator_id INT, created_ts TIMESTAMP, country VARCHAR)
Samples:
content
| content_id | creator_id | created_ts | country |
| 101 | 1001 | 2025-08-25 10:00:00 | US |
| 102 | 1002 | 2025-08-30 12:00:00 | US |
| 103 | 1003 | 2025-08-31 08:00:00 | CA |
content_views
| view_ts | user_id | content_id | surface | country |
| 2025-08-26 09:00:00 | 504 | 101 | feed | US |
| 2025-08-30 18:00:00 | 501 | 102 | feed | US |
| 2025-08-31 20:00:00 | 501 | 101 | feed | US |
| 2025-09-01 09:00:00 | 502 | 102 | feed | US |
| 2025-09-01 10:00:00 | 503 | 103 | search | CA |
content_moderation
| content_id | decision | violation_type | detected_ts | source | is_removed |
| 101 | VIOLATION | spam | 2025-08-31 21:00:00 | human | true |
| 102 | CLEAN | NULL | 2025-08-30 14:00:00 | model | false |
| 103 | VIOLATION | hate_speech | 2025-09-01 12:00:00 | model | true |
Implementation details to reflect in SQL: treat the latest decision per content_id as the final decision for ex-post; for ex-ante, use the most recent decision with detected_ts <= view_ts. Assume contents without any moderation row at view time are CLEAN for ex-ante. Return: (view_date, country, total_views, violating_views_ex_ante, violating_views_ex_post, ex_ante_prevalence, ex_post_prevalence). Then compute the creator-level breakdown requested.
Quick Answer: This question evaluates a candidate's competency in temporal joins, stateful deduplication of moderation decisions, time-aware aggregation, and prevalence metric calculations within content-moderation analytics.