This question is basically five questions stapled together and they want you to treat it like one coherent answer.
Start by clarifying the feature's goal (e.g., meaningful social interactions) and define a primary success metric that captures that goal, such as Daily Active Users (DAU) or a more specific engagement metric like 'Daily Engaged Users' (users who like, comment, or share). Then walk through the metric's exact formula, guardrails, data handling, and trade-offs, ensuring you cover all requested aspects in a structured manner.
Pro tip: Emphasize that metrics must be actionable and aligned with long-term user value, not just short-term engagement; mention how you'd validate the metric with A/B tests and monitor for unintended consequences.
Choose a metric that reflects the feature's core goal, such as 'Daily Engaged Users' (users who perform at least one meaningful interaction). Provide an exact formula, e.g., DEU = count(distinct user_id) where event_type in ('like','comment','share') and date = current_date.
Select metrics to ensure the feature doesn't harm other aspects, such as user retention, time spent, or report rate. For example, 7-day retention rate and average session length.
Specify who is included (e.g., users who have seen the feed at least once) and the unit (e.g., user-day). Exclude new users during onboarding or users in experiments.
Describe how to filter bots (e.g., using user agent or behavior patterns), treat outliers (e.g., winsorize or exclude extreme values), and normalize time zones (e.g., convert all timestamps to UTC or user's local time for daily aggregation).
Write SQL-like pseudocode for each metric, e.g., SELECT date, count(distinct user_id) FROM events WHERE event_type IN (...) GROUP BY date. Then discuss trade-offs: optimizing for engagement might reduce quality (e.g., clickbait) or reliability (e.g., system latency), so balance with guardrails.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.