Break the problem into two independent metrics, each requiring a different aggregation window and population definition. For metric 1, compute a rolling 7-day window per report date and identify callers with at least 20 calls in that window, then divide by all active callers in the same window. For metric 2, filter to Great Britain users, compute daily call participations per user (as caller or recipient), and calculate the percentage with 50 or more participations on each date.
Pro tip: Clarify ambiguous terms like 'active caller' and 'call participation' before writing SQL—interviewers often expect you to ask about edge cases such as time zones, duplicate calls, or whether recipients are counted once per call. Also, consider using window functions or self-joins efficiently to avoid performance pitfalls.
Confirm what 'active caller' means (e.g., made at least one call in the window), how to handle time zones, and whether 'call participation' counts each call once per user even if they are both caller and recipient. State any assumptions clearly.
For each report date, aggregate calls over the past 7 days per caller, count callers with >=20 calls, and divide by the total distinct callers active in that same 7-day window. Use a self-join or window function to handle the rolling window.
Filter users to Great Britain, then for each date count participations per user (as caller or recipient). Identify users with >=50 participations on that date and divide by all GB daily active users (those with at least one participation that day).
Full outer join the two metric results on report date, ensuring all dates from either metric are included. Format percentages as decimals or percentages as required.
Check for edge cases like missing dates, zero denominators, and duplicate calls. Consider indexing or partitioning strategies for large datasets, and verify results with sample calculations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.