My first instinct was an inner join and I almost went with it.
Clarify the schema and definitions first, then write a SQL query that counts distinct surveys sent and responses received, using a LEFT JOIN or subqueries to handle surveys with no responses. Compute the response rate as responses divided by surveys sent, ensuring the denominator includes all surveys sent even if they have zero responses.
Pro tip: Mention that you would validate the response rate by checking for edge cases like duplicate responses or surveys sent to the same user multiple times, and consider whether to use COUNT(DISTINCT) to avoid overcounting.
Ask about the table structures: what columns exist in surveys and survey_responses, and how they relate (e.g., survey_id). Confirm that 'surveys sent' means all records in the surveys table, and 'responses' means records in the responses table.
Decide between using a LEFT JOIN from surveys to responses to include surveys with no responses, or using separate subqueries to count surveys and responses independently. Both are valid; LEFT JOIN is more intuitive for this metric.
Construct the query: SELECT COUNT(r.survey_id) * 1.0 / COUNT(DISTINCT s.survey_id) AS response_rate FROM surveys s LEFT JOIN survey_responses r ON s.survey_id = r.survey_id; or use subqueries. Ensure proper handling of NULLs and division by zero.
Mention potential issues: duplicate responses per survey, surveys sent to multiple users, or missing survey_id in responses. Suggest using COUNT(DISTINCT) for responses if duplicates are possible, and consider if the denominator should be distinct surveys or total sends.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one tripped me up more than I expected.
First, define the aggregation level: one row per survey response, with a flag indicating whether the user was new (signed up <30 days before survey sent). Then compute the average rating for each group, ensuring you filter to only valid responses and handle potential duplicates. Finally, compare the averages, possibly with a statistical test or confidence intervals.
Pro tip: Always clarify the grain of your data before writing the query—many candidates mistakenly aggregate at the user level instead of the survey response level, which can skew results if users have multiple responses. Also, consider using a window function or subquery to avoid double-counting users who may have multiple surveys.
Determine whether the analysis should be at the survey response level or user level. Typically, each survey response is a separate observation, so aggregate at that level unless instructed otherwise.
Calculate the time difference between signup date and survey sent date. Flag users as 'new' if the difference is less than 30 days, otherwise 'existing'.
Exclude invalid or incomplete survey responses (e.g., null ratings) and ensure each survey response is unique. Consider deduplicating if necessary.
Use a GROUP BY on the user type flag to calculate the average rating for new and existing users separately.
Present the averages side by side, and optionally include counts and a statistical test (e.g., t-test) to determine if the difference is significant.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.