The country attribution piece is where I nearly tripped.
Start by clarifying the metric definition: confirmed violations are moderation decisions with a final 'confirmed' status, attributed to the user's country at the time of the violating event. Then define a 7-day window with explicit boundaries (e.g., UTC days) and compute the rate as (distinct users with ≥1 confirmed violation in window) / (distinct active users in window) * 1000, using a single SQL query with CTEs for clarity.
Pro tip: Use a window function or a calendar table to ensure you include all days in the 7-day period, even if there are no violations, and always state your assumptions about late-arriving data (e.g., decisions arriving after the window closes) and how you handle them (e.g., exclude or include with a lookback).
State that 'confirmed violation rate per 1,000 active users' means the number of unique users with at least one confirmed violation in the 7-day window divided by the number of unique active users in the same window, multiplied by 1,000. Clarify that 'confirmed' means a moderation decision with a final status indicating a violation (e.g., 'confirmed_violation').
Choose a 7-day window with explicit boundaries, e.g., from 2024-01-01 00:00:00 UTC to 2024-01-07 23:59:59 UTC. For late-arriving decisions, decide whether to include decisions that arrive after the window but pertain to events within the window; a common approach is to use a lookback period (e.g., 7 days after window end) to capture late decisions, and state this assumption.
Attribute each violation to the country of the user at the time of the violating event. Use the user's country from the events table (e.g., event_country) or from a user profile snapshot if available; if not, use the country from the user's most recent event before the violation. Be explicit about the choice.
Construct a single BigQuery SQL query using CTEs: one for active users per country in the window, one for confirmed violations per country in the window (with late-arriving decisions included based on your rule), and then join and compute the rate. Use COUNT(DISTINCT user_id) for both numerator and denominator.
Mention potential edge cases: users with multiple violations counted once, users with no country (exclude or assign to 'unknown'), and decisions that are later overturned (only count final confirmed decisions). Also note that the denominator should include all active users, not just those with events in the window if active is defined differently.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The 48-hour match window is a strict half-open interval on the left and closed on the right, so flag_time < decision_time <= flag_time + INTERVAL 48 HOUR.
Start by deduplicating flags to the earliest per (content_id, rule_id) using a window function, then join to violation decisions within 48 hours to compute true positives. Finally, aggregate by rule_id to calculate precision as true positives divided by total flags.
Pro tip: Clarify the schema and assumptions (e.g., flag_time and decision_time columns) before writing SQL, and mention edge cases like multiple decisions or missing decisions to show thoroughness.
Use ROW_NUMBER() OVER (PARTITION BY content_id, rule_id ORDER BY flag_time) to select the earliest flag per pair.
Left join the deduplicated flags to violation decisions on content_id where decision_time is between flag_time and flag_time + 48 hours.
Mark a flag as a true positive if at least one violation decision falls within the 48-hour window.
Group by rule_id and compute precision as COUNT(true positives) / COUNT(total flags) for each rule.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.