Start by clarifying the schema and metric definitions, then build a daily spine and join the relevant tables to compute each metric per day, gender, and age bucket. Use CTEs to modularize the logic, and return two result sets: the aggregated report and the list of user IDs missing photos as of 2025-09-01.
Pro tip: Explicitly state your assumptions about ambiguous terms like 'photo present rate' and 'match rate' before writing SQL, and use a date spine to ensure all days are represented even if there is no activity.
Ask about the schema, definitions of metrics (e.g., photo present rate, completion rate, match rate), and how to handle missing data. Confirm that ages are computed as of each report date.
Generate a date series from 2025-08-25 to 2025-09-01. Create CTEs for profiles, photos, views, likes, and matches, filtered to the relevant date range.
Join the base tables to the date spine, calculate age buckets from birthdate, and aggregate metrics using conditional sums and counts. Ensure all combinations of day, gender, and age bucket are present.
Write a separate query to return user IDs whose profiles are missing a photo as of 2025-09-01, using a left join or anti-join on the photos table.
Check for edge cases (e.g., division by zero, nulls) and ensure the output matches the required format: one row per day, gender, and age bucket for the first result set, and a list of user IDs for the second.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.