This one took me longer than I expected to set up cleanly.
Break the problem into logical CTEs: first filter active profiles and their latest approved review version, then join to engagement and quality metrics, and finally compute derived metrics like like rate. Use window functions to identify the latest version per profile and ensure the snapshot timestamp is respected.
Pro tip: Explicitly state your assumptions about the data model (e.g., how reviews, versions, and profiles relate) and clarify ambiguous terms like 'latest version' and 'final approved review' before writing SQL. This shows you think like a data scientist who validates requirements.
Ask clarifying questions about table schemas, relationships, and definitions (e.g., what constitutes a 'final approved review', how to identify deleted profiles, and whether metrics are cumulative or per snapshot).
Use a CTE to select non-deleted profiles and join to reviews, filtering for approved status and using a window function to get the latest version per profile as of the snapshot timestamp.
In separate CTEs, compute per-profile metrics such as total views, total likes, photo count, and bio length, ensuring they are calculated as of the snapshot timestamp.
Join the filtered profiles with the metrics CTEs and calculate like rate (likes/views) while handling division by zero.
Select the required columns, apply any final filters (e.g., only profiles with at least one view), and order the results for readability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Straightforward GROUP BY once the snapshot CTE is solid.
Start by clarifying the grain of the data and the definition of each metric, then outline a SQL query that aggregates from the profile-level snapshot to country-level. Use conditional aggregation to compute counts and sums, and derive percentages and rates from those aggregates. Finally, validate the results and consider edge cases like missing data or profiles with no activity.
Pro tip: Always confirm whether the metrics should be computed over all profiles or only approved profiles, and whether the 7-day window is fixed or rolling. This shows attention to detail and prevents misinterpretation.
Confirm the definition of 'approved profiles', 'photo', 'bio', '7-day views/likes', and the time window. Ensure you understand the profile-level snapshot table structure.
Decide how to compute each metric: counts for approved profiles, conditional counts for photo/bio percentages, sums for views/likes, and rate as sum(likes)/sum(views).
Use GROUP BY country and aggregate functions like COUNT, SUM, and CASE WHEN to calculate the required metrics in a single query.
Check for NULLs, division by zero, and profiles with no views. Consider whether to filter out countries with small sample sizes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The idempotency one is where I think a lot of people slip up.
Start by clarifying the data model and snapshot semantics, then structure your answer around four validation queries that each address a specific integrity check. For each query, explain the logic, the join conditions, and how time-bounding ensures deterministic reruns. Emphasize that these checks are essential for data quality and reproducibility in a production pipeline.
Pro tip: Mention that you would run these validation queries as part of a data quality gate before downstream consumption, and that you'd log any violations for root cause analysis. This shows you think about operationalizing data quality, not just writing ad-hoc queries.
Ask about the schema: profiles, reviews, events, and how snapshot cutoff is defined. Confirm that 'latest version' means the most recent review version per profile as of the snapshot.
Use a window function to rank reviews by version per profile, then compare the final review's version to the max version. Filter where they differ.
Group by profile and count final reviews (e.g., where is_final = true). Flag profiles with count > 1.
Filter events where event_timestamp > snapshot_cutoff. This identifies late-arriving data that could affect aggregations.
Add a WHERE clause to all event aggregations that restricts events to those with event_timestamp <= snapshot_cutoff. This guarantees deterministic results across reruns.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.