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Start by clarifying the schema and definitions (e.g., what constitutes a click, positive stay time, and how to handle deduplication). Then outline a multi-CTE query: generate a date spine, deduplicate clicks using window functions, aggregate daily metrics, and compute the rolling 7-day unique users. Finally, join the date spine with aggregated metrics to fill zero-activity days.
Pro tip: Mention that you would validate the deduplication logic by checking edge cases like clicks exactly 5 minutes apart, and consider using a self-join or window function with a range frame for the rolling unique count to avoid performance pitfalls.
Ask about table structures, definitions of DAU, click, positive stay time, and the 7-day window. Confirm whether the rolling unique count is per day or over the entire window.
Use a recursive CTE or a dates table to create a series of 7 consecutive dates covering the analysis window.
Use window functions (e.g., LAG or ROW_NUMBER with a 5-minute threshold) to flag and remove duplicate clicks for the same user-pin pair within 5 minutes.
Compute DAU (distinct users), click counts, and average positive stay time per day from the deduplicated data.
Calculate the rolling 7-day unique user count using a window function with a RANGE frame, then left join the date spine with the aggregated metrics to fill zero-activity days.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the metric definition and edge cases, then outline the SQL logic using a self-join or conditional aggregation on user-level daily activity. Finally, discuss how to interpret the result and potential pitfalls like time zones or bot traffic.
Pro tip: Always confirm whether the metric should be computed at the user level (unique users) or session level, and whether the denominator includes only users with clicks on the first day or all users active that day. This shows attention to detail and avoids misinterpretation.
Define 'shopping click' (e.g., any click on a shopping pin or product) and confirm the date range and time zone. Ask if the denominator is users with at least one shopping click on 2025-08-31 and numerator is those who also had at least one on 2025-09-01.
Determine which tables contain user click events (e.g., event logs, clicks table) and how to filter for shopping clicks. Ensure you have user IDs and event timestamps.
Use a self-join or conditional aggregation: select distinct users with shopping clicks on day 1, then check if they also appear on day 2. Compute the ratio of users in both days to users on day 1.
Check for duplicate events, bot traffic, and time zone consistency. Consider if users must be active on both days or if any click on day 2 counts. Also, decide how to handle users with no activity on day 2 (they count as not retained).
Present the retention rate as a percentage, and discuss potential business implications (e.g., engagement, product changes). Mention any caveats or limitations of the analysis.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the definitions of 'shopping click' and 'positive stay time' and confirm the date window boundaries. Then, write a SQL query that aggregates deduplicated clicks per user-pin, ranks pins using the specified tie-breakers, and filters to the top 2 per user. Finally, validate the results with edge cases like ties and missing data.
Pro tip: Explicitly state your assumptions about deduplication (e.g., unique user-pin-day) and tie-breaking order, as these details are often ambiguous and can significantly impact results. Also, mention how you would handle users with fewer than 2 pins.
Confirm what constitutes a 'shopping click' (e.g., event type) and 'positive stay time' (e.g., time spent > 0). Verify the 7-day window: 2025-08-26 to 2025-09-01 inclusive.
Compute deduplicated shopping click count (e.g., count distinct click IDs or user-pin-day combinations) and total positive stay time for each user-pin pair within the window.
Use a window function (e.g., ROW_NUMBER) partitioned by user, ordered by click count DESC, stay time DESC, and pin ID ASC to assign ranks.
Select ranks 1 and 2 per user. Validate results by checking for ties, ensuring correct ordering, and handling users with fewer than 2 pins.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Four sub-tasks rolled into one and they all interact.
Break the problem into clear stages: data cleaning (mapping, handling negatives, filling NaNs), sorting, and aggregation. For the final metric, filter to the last 7 days and positive stay times, then group by user and category to compute total stay time, and select the top category per user with alphabetical tie-breaking. Use pandas operations like map, replace, fillna, sort_values, groupby, and idxmax or nlargest with careful handling of ties.
Pro tip: When computing averages that exclude zeros, use a masked groupby (e.g., df[df.stay_time_sec > 0].groupby('user_id').mean()) rather than filling zeros and then filtering, to avoid skewing results. Also, for tie-breaking alphabetically, sort categories ascending before using idxmax to ensure deterministic selection.
Use df['category'].map(category_dict).fillna('Unknown') to map codes to names and handle missing codes. Replace negative stay_time_sec with NaN using df.loc[df.stay_time_sec < 0, 'stay_time_sec'] = np.nan.
Fill remaining NaN stay_time_sec with 0 for aggregation. Sort the DataFrame by user_id ascending, timestamp ascending, and stay_time_sec descending using df.sort_values(by=['user_id', 'timestamp', 'stay_time_sec'], ascending=[True, True, False]).
Determine the reference date (e.g., max timestamp) and filter to events within the last 7 days. Also filter to stay_time_sec > 0 to exclude zeros from averages and totals.
Group by user_id and category, sum stay_time_sec, then for each user select the category with the highest total. For ties, sort categories alphabetically and pick the first.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.