Start by clarifying the schema and definitions (e.g., what constitutes a listing interaction, how to handle time zones). Then outline a SQL query that filters interactions to the last 3 calendar days, aggregates counts per seller-product, filters products with count > 1, and finally counts distinct sellers with more than 3 such products.
Pro tip: Mention that you would validate the query with edge cases (e.g., sellers with exactly 3 products, interactions on the boundary of the 3-day window) and consider performance implications for large datasets.
Ask questions to confirm the table structure, definitions of 'listing interaction', 'product', and 'seller', and how to handle time zones and calendar days.
Use a date filter (e.g., WHERE interaction_date >= CURRENT_DATE - INTERVAL '3 days') to restrict the dataset to the relevant time window.
Group by seller_id and product_id, and count interactions to get the interaction count for each seller-product pair.
Apply a HAVING clause to keep only seller-product pairs where the interaction count exceeds 1.
Group by seller_id, count the number of qualifying products, filter for counts >3, and then count distinct sellers.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the definitions of key terms: 'listings created in the US within the last 7 days', 'total listing interactions', and 'Vehicle category'. Then outline a SQL-based approach to join listings and interactions, filter by date and country, and compute the percentage of interactions from Vehicle listings. Finally, discuss potential data quality issues and validation steps.
Pro tip: Mention that you would validate the category assignment by checking if 'Vehicle' is a top-level category or a subcategory, and consider whether interactions on Vehicle listings might be skewed by high-ticket items or bots. This shows you think about data nuances beyond the surface.
Confirm what constitutes a 'listing', 'interaction', and 'Vehicle category' (e.g., is it a top-level category or includes subcategories like cars, motorcycles?). Also clarify the time window: last 7 days from today or from a specific date?
Determine which tables contain listings (with creation date, country, category) and interactions (with listing ID, interaction type, timestamp). Ensure you can join them on listing ID.
Filter listings created in the US in the last 7 days, join with interactions, then calculate the percentage: (interactions on Vehicle listings / total interactions on those listings) * 100. Use appropriate date functions and handle time zones.
Check for missing data, duplicate interactions, or listings with no interactions. Compare with overall trends to see if the percentage seems reasonable. Consider edge cases like listings created near midnight.
Clearly state the percentage and provide context: e.g., 'Vehicle listings account for X% of interactions among new US listings in the last week.' Discuss potential business implications or next steps.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.