This took me longer than I wanted to admit.
Start by clarifying the metric definition and edge cases, then outline the SQL logic using CTEs to compute the numerator and denominator separately before joining. Use a 7-day window to smooth daily fluctuations, ensuring the denominator includes all sessions with at least one home_feed impression on each date.
Pro tip: Mention that you would validate the query by checking for sessions that appear in the numerator but not the denominator, and consider using a window function to compute rolling averages for trend analysis.
Confirm definitions: what constitutes a 'home_feed impression', how to handle sessions with multiple impressions, and whether the 7-day window is rolling or fixed. Discuss time zone considerations and data freshness.
Write a CTE to select distinct session IDs that had at least one home_feed impression on each date. This forms the base population for the metric.
Write a CTE to select distinct session IDs that saw at least one impression from the shop ranked 5 or below on home_feed on that date. Ensure ranking is correctly interpreted (e.g., rank <= 5).
Join the numerator and denominator CTEs on date and shop, calculate the fraction as numerator count divided by denominator count, and output date, shop, score, numerator, and denominator.
Use a window function to compute a rolling 7-day average of the daily score per shop, or aggregate the numerator and denominator over the window before dividing, depending on the desired interpretation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Straightforward once the main query works.
First, clarify the SVS metric definition and the query result schema, including how 7-day average is computed and whether the window is fixed or rolling. Then, write a SQL query that aggregates SVS over the 7-day window per shop, ranks shops by average SVS descending, and uses total home_feed impressions as a tiebreaker. Finally, validate the results and discuss potential edge cases like missing data or timezone issues.
Pro tip: Always confirm whether the 7-day average should be computed as a simple average of daily SVS values or as a weighted average based on impressions; the latter is often more appropriate for ratio metrics like SVS. Also, explicitly state your assumptions about the time window (e.g., last 7 days from today) to avoid ambiguity.
Ask clarifying questions about SVS definition, how 7-day average is calculated, and the structure of the SVS query results (columns, granularity). Confirm the time window and tiebreaker logic.
Write a subquery or CTE to compute the 7-day average SVS per shop and sum home_feed impressions over the same window. Ensure proper filtering for the date range.
Use window functions (e.g., ROW_NUMBER() or RANK()) to order shops by average SVS descending, then by total impressions descending. Select the top 3.
Check for ties, missing data, or outliers. Discuss how the results might be used and any limitations (e.g., small sample sizes, seasonality).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The condition rank <= 5 handles gaps automatically since it's a numeric comparison, not a positional one.
First, clarify the definition of 'top-5 visibility'—whether it means the five highest-ranked sessions or sessions with rank ≤ 5. Then, analyze how gaps in the rank column affect the query's logic, particularly if it relies on rank values or row numbering. Finally, propose adjustments such as using DENSE_RANK or filtering by rank threshold to ensure correct capture.
Pro tip: Demonstrate awareness that rank gaps often signal missing data or business rules (e.g., deleted sessions), and that the choice between rank-based and row-number-based logic depends on the specific visibility metric. This shows you consider data context, not just syntax.
Ask whether 'top-5' means the five sessions with the highest ranks (regardless of rank values) or sessions with rank ≤ 5. This determines if gaps matter.
Identify if the query uses ROW_NUMBER, RANK, DENSE_RANK, or a simple rank filter. Gaps affect ROW_NUMBER and RANK differently.
If using ROW_NUMBER, gaps don't affect the count of top-5 rows. If using RANK or filtering rank ≤ 5, gaps can cause fewer than 5 rows or include unintended rows.
If needed, switch to DENSE_RANK to ignore gaps, or use ROW_NUMBER with ORDER BY rank to get exactly 5 rows. Alternatively, filter by rank ≤ 5 if that's the business rule.
Test with ranks like 2,4,6 and 1,3,5 to ensure the query returns the correct set under both interpretations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify what SVS represents and identify its components from the schema. Then, systematically think about how each component could be manipulated or biased, and propose SQL-based fixes that address the root cause without requiring external data.
Pro tip: Frame your answer around data integrity and fairness, showing that you consider both technical and business implications. Mention that fixes should be scalable and maintainable.
Define SVS and list the relevant columns. Identify how SVS is calculated from these columns.
Brainstorm ways users or systems could artificially inflate or deflate SVS using only the available columns.
Consider how data collection or schema limitations could introduce bias into SVS.
For each issue, design a SQL-only solution (e.g., filtering, weighting, normalization) that mitigates the problem.
Suggest how to test the fixes and monitor for new gaming or bias patterns.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.