The join to the surveys table to pull in version was the easy part.
Start by clarifying the schema and definitions (e.g., what constitutes a unique clicked impression, how survey versions are linked). Then, write a SQL query that joins the tables appropriately, filters for the last 7 days, and aggregates daily metrics by survey version, ensuring correct handling of unique counts and division.
Pro tip: Always confirm whether 'unique clicked impressions' means distinct users or distinct click events, and whether the denominator should include only impressions that had a chance to be surveyed. This shows attention to metric definition and avoids misinterpretation.
Ask about table structures, join keys, and precise definitions of 'unique clicked impressions', 'survey version', and 'daily'. Confirm the time window and timezone.
Determine how impressions, clicks, surveys, and survey_responses relate. Typically, impressions link to clicks via impression_id, clicks link to surveys via click_id or survey_id, and survey_responses link to surveys via survey_id.
Filter all tables to the last 7 days. For each day and survey version, compute total impressions and unique clicked impressions (e.g., COUNT(DISTINCT click_id) or user_id).
Calculate the ratio of unique clicked impressions to total impressions per day per survey version. Use NULLIF or CASE to avoid division by zero. Consider if multiple surveys per impression affect the denominator.
Construct the final SQL with CTEs for clarity, ensuring correct grouping and ordering. Validate with sample data or explain how you'd test for correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pretty mechanical once you have the daily CTE set up.
First, clarify the definition of response rate (e.g., completed surveys divided by delivered invitations) and confirm the data sources. Then compute the overall rate for the full 7-day window by aggregating all responses and all invitations, and break it out by survey version using the same definition. Present both the overall and segmented rates, and briefly discuss any notable differences.
Pro tip: Always check for data quality issues like duplicate responses or partial completes, and consider whether the 7-day window is consistent across survey versions. Mentioning these nuances shows you think like a data scientist, not just a calculator.
Confirm what 'response rate' means (e.g., completed surveys / delivered invitations) and ensure the 7-day window is clearly defined for all versions.
Sum the total number of completed responses and total invitations across all survey versions for the 7-day period, then compute the overall response rate.
For each survey version, calculate the response rate using the same formula, ensuring consistent handling of the 7-day window.
Check for anomalies such as missing data, duplicate responses, or version misclassification, and verify that the sum of version-specific numerators and denominators matches the overall totals.
Report the overall rate and the breakdown by version, and briefly comment on any significant differences or trends that could inform product decisions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The first-score-only metric needed a ROW_NUMBER() partitioned by user_id and survey_id ordered by ts, then filter to rank = 1.
First, clarify the data model and define the 7-day window and survey version field. Then, for each metric, write SQL that deduplicates by user using a window function for the first-score metric, and a simple average for the all-scores metric, ensuring both are computed over the same window and grouped by survey version.
Pro tip: Mention that the 'first score' metric is essentially a user-level deduplication to avoid bias from power users, and that you'd validate the difference between the two metrics to understand duplicate behavior—this shows you think about data quality and metric reliability.
Confirm the definition of the 7-day window (e.g., rolling or fixed), the survey version field, and how to identify a user. Ask about the expected output format (e.g., one row per survey version with both metrics).
Use a window function like ROW_NUMBER() OVER (PARTITION BY user_id, survey_version ORDER BY timestamp) to select each user's first score per survey version, then average those scores grouped by survey version.
Simply average all scores in the 7-day window grouped by survey version, without deduplication, to include duplicates from the same user.
Join or union the two metrics into a single result set by survey version. Check for anomalies, such as large discrepancies, and consider edge cases like users with no scores or multiple versions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the join logic: you need to join survey responses to impressions on user_id and survey_id, with the impression timestamp within 7 days before the response. Then filter to keep only responses where the response timestamp is at or after the first click for that user and survey on the same day, and exclude any responses without a matching impression. Finally, write the modified query using a LEFT JOIN with conditions and a subquery for first click.
Pro tip: Mention that you would use a LEFT JOIN and then filter out NULLs to exclude responses without impressions, but also consider using an INNER JOIN for efficiency if the dataset is large. Also, highlight the importance of handling time zones consistently, as Meta operates globally.
Restate the problem to ensure you understand: we need to count survey responses only if there is an impression for the same user and survey within 7 days before the response, and the response timestamp is at or after the first click on that day. Exclude responses with no matching impression.
Determine that you need to join the survey responses table with the impressions table on user_id and survey_id, and also with a clicks table (or subquery) to get the first click per user per survey per day.
Use a LEFT JOIN from responses to impressions with the condition that impression timestamp is between response timestamp - 7 days and response timestamp. Also, join to a subquery that calculates the first click timestamp for each user, survey, and day.
Filter out rows where the impression is NULL (to exclude responses with no matching impression) and where the response timestamp is before the first click timestamp on that day.
Compose the SQL query with the appropriate joins, subqueries, and WHERE clauses, ensuring correct date/time functions and handling of time zones.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I said I'd assume UTC throughout and that impressions with no clicks get a NULL clicked count (treated as 0 for the rate).
Start by clarifying the schema and join relationships, then build the query incrementally using CTEs to compute each metric separately before combining them. Explicitly state assumptions about timezone normalization and how missing joins (e.g., surveys with no responses) are handled, and validate the final result set against edge cases.
Pro tip: Mention that you would validate the response rate denominator by checking whether all impressions are tied to a survey version, and consider using a left join from impressions to responses to avoid dropping surveys with zero responses.
Identify the fact and dimension tables (impressions, clicks, responses, surveys) and determine the correct join keys and granularity. State whether joins are inner or left, and how missing matches affect counts.
Assume all timestamps are in UTC unless specified, and convert to the reporting timezone (e.g., PT) for date grouping. Mention that date truncation should be consistent across all metrics.
Use separate CTEs to calculate total impressions, unique clicked impressions, and response counts per date and survey version. For average scores, use conditional aggregation to compute first-only and all-scores averages.
Join the CTEs on date and survey version, using left joins to preserve all dates/versions. Use COALESCE to replace nulls with zeros for counts and nulls for averages where appropriate.
Check that response rate is between 0 and 1, and that averages are within expected ranges. Explicitly list assumptions about timezone, missing joins, and how first-only is defined (e.g., first response per user per survey).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.