The active pixel filter tripped me up a bit.
Start by clarifying the schema and definitions (e.g., what constitutes an active pixel, valid event rate). Then, write a query that joins the tables, filters for active pixels, and aggregates per advertiser per day using conditional aggregation for the weighted average. Finally, validate the logic with edge cases like zero events or null rates.
Pro tip: Explicitly state your assumptions about the data model (e.g., grain of each table, how to handle missing days) before writing SQL—this shows you think like a data scientist who cares about data quality and reproducibility.
Ask about the columns and relationships: advertisers (advertiser_id), pixels (pixel_id, advertiser_id), daily_pixel_signals (pixel_id, date, events, valid_events). Define 'active pixel' as having at least one event that day, and 'valid event rate' as valid_events / events.
From daily_pixel_signals, filter to rows where events > 0 (active pixels). Compute per pixel per day: events and valid_events (or valid rate).
Join the filtered pixel-day data with pixels to get advertiser_id, then group by advertiser_id and date. Count distinct pixel_id for active pixels, sum events, and compute weighted average valid rate as SUM(valid_events) / SUM(events).
Ensure no division by zero (e.g., use NULLIF or CASE). Consider advertisers with no active pixels that day—decide whether to include them with zeros or exclude. Write the final SQL with proper aliases and ordering.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one was harder to hold in my head all at once.
Start by defining the per-advertiser daily quality score as the event-weighted average of valid_event_rate times match_rate, then bucket advertiser-days into low, medium, and high tiers using thresholds like tertiles or business-defined cutoffs. Finally, aggregate spend, clicks, conversions, CVR, and ROAS by bucket across the 30-day window, ensuring proper weighting and handling of edge cases.
Pro tip: When defining quality tiers, avoid arbitrary thresholds; instead, use quantile-based bucketing (e.g., tertiles) to ensure balanced groups and interpretability. Also, consider that ROAS and CVR should be computed as weighted averages (e.g., total conversions / total clicks) rather than averages of ratios.
Compute a per-advertiser daily quality score as the event-weighted average of valid_event_rate * match_rate. Ensure you have the necessary event-level data and that weights are appropriate (e.g., by impressions or events).
Assign each advertiser-day to a low, medium, or high quality tier based on the quality score. Use quantile-based thresholds (e.g., tertiles) or business-defined cutoffs, and document the rationale.
For each quality tier, aggregate spend, clicks, conversions, CVR, and ROAS across the 30-day window. Compute CVR as total conversions / total clicks and ROAS as total revenue / total spend to avoid ratio bias.
Check for data quality issues, outliers, and ensure the aggregation is correct. Interpret the results to see if higher quality tiers correlate with better ad performance metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.