The DISTINCT buyer part is where people slip up.
Start by clarifying the table schemas and the definition of 'buyer' and 'interaction'. Then write a query that groups interactions by product, computes COUNT(DISTINCT buyer_id) and COUNT(*) per product, filters with HAVING on both conditions, and finally counts the resulting products. Explain that HAVING is used because the thresholds apply to aggregated values, not individual rows.
Pro tip: Mention that COUNT(DISTINCT buyer_id) can be expensive on large datasets, so in a real Meta-scale scenario you might pre-aggregate or use approximate distinct counts (e.g., HyperLogLog) if exactness isn't critical.
Ask or state assumptions about the columns in the interactions and products tables, and define what constitutes a 'buyer' (e.g., user_id with a purchase event) and an 'interaction' (e.g., any row in the interactions table).
Write a subquery or CTE that groups the interactions table by product_id and computes COUNT(DISTINCT buyer_id) AS distinct_buyers and COUNT(*) AS total_interactions.
In the same aggregation query, add a HAVING clause with conditions distinct_buyers > 3 AND total_interactions > 10 to filter groups that meet both thresholds.
Wrap the filtered aggregation in an outer query that returns COUNT(*) AS product_count, or simply count the rows from the CTE.
Articulate that WHERE filters rows before grouping, while HAVING filters groups after aggregation; since the conditions depend on aggregate results, HAVING is required.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The zero denominator question is the part I almost glossed over.
Write a SQL query that filters interactions to the 7-day window ending 2025-09-01 and uses an INNER JOIN to restrict to US products, then compute the percentage of 'validate' interactions using a safe division that handles zero denominators. Explain that INNER JOIN is semantically correct because only US products should be included, and that it is more efficient than LEFT JOIN. Round the final percentage to two decimal places.
Pro tip: Mention that you would validate the denominator is non-zero before dividing, and that you would also check for edge cases like missing dates or product IDs. This shows you think about data quality, not just query syntax.
Filter interactions to the 7-day window ending 2025-09-01, i.e., from 2025-08-26 to 2025-09-01 inclusive. Use a WHERE clause on the interaction date.
Use an INNER JOIN between the interactions table and the products table on product_id, with a condition that the product's country is 'US'. This ensures only US products are included.
Calculate the total interaction_count for all interactions in the window (denominator) and the sum of interaction_count where interaction_type = 'validate' (numerator).
Use a CASE statement or NULLIF to avoid division by zero, returning 0 or NULL as appropriate. Round the result to two decimal places using ROUND.
Justify INNER JOIN over LEFT JOIN: INNER JOIN is more efficient and semantically correct because we only want US products; LEFT JOIN would include non-US products with NULLs, requiring extra filtering. Mention handling of zero denominator and rounding.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Indexes part was straightforward: composite index on interactions(product_id, interaction_type, interaction_date) covers the filtering and grouping, and products(product_id, country) supports the join plus the country filter.
First, restate the two queries to confirm understanding, then write clean SQL with explicit JOINs and aggregations. For indexing, propose composite indexes that cover the WHERE and GROUP BY columns, and explain that HAVING filters after aggregation while WHERE filters rows before grouping.
Pro tip: Mention that indexes should be designed based on query patterns, and that covering indexes can avoid table lookups. Also note that HAVING can sometimes be pushed down by the optimizer, but semantically it's post-aggregation.
Restate the two parts to ensure you understand the required aggregations, filters, and joins. Ask clarifying questions if needed.
Write the complete SQL for both queries, using proper JOINs, GROUP BY, and HAVING clauses. Ensure aliases and column references are correct.
For each table, suggest composite indexes on columns used in WHERE, JOIN, and GROUP BY. Explain how they improve performance.
Conceptually explain that WHERE filters rows before grouping, while HAVING filters groups after aggregation. Provide a simple example.
Summarize your answer, and mention any trade-offs or alternative approaches, such as using subqueries or window functions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.