First, clarify the definition of response rate: it is the number of survey responses divided by the number of users who were eligible to receive the survey (i.e., notified). Then, write a SQL query that counts distinct survey responses and divides by the count of distinct notified users, ensuring you handle potential duplicates and missing data appropriately.
Pro tip: Always confirm the denominator: is it all users, users who were notified, or users who opened the notification? At Meta, the response rate is typically calculated as responses divided by notifications sent, so double-check with the interviewer to avoid a wrong metric.
Ask the interviewer to define 'response rate' precisely: numerator (e.g., completed surveys) and denominator (e.g., users notified). Confirm whether to count distinct users or responses.
Determine which columns in Surveys and Users indicate notification and response. For example, Surveys might have user_id, survey_id, response_date, and Users might have user_id, notification_date.
Create separate aggregations: one for the count of distinct users who responded, and one for the count of distinct users who were notified. Use CTEs for readability.
Divide the numerator by the denominator, using NULLIF to avoid division by zero. Consider if you need to filter by time period or survey type.
Walk through the query logic, state any assumptions (e.g., all notified users are in Users table), and suggest ways to validate results (e.g., check counts).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Write a SQL query that joins the user registration table with the notification survey ratings table, calculates tenure as the difference between survey date and registration date, buckets users into tenure groups (e.g., new vs. long-term), and compares average ratings across these groups. Use aggregation and window functions to compute the metrics and optionally test for statistical significance.
Pro tip: Always consider potential confounders like user activity level or survey timing, and mention that correlation does not imply causation—newer users might rate higher due to novelty or different expectations.
Locate the user registration table (with user_id and registration_date) and the notification survey ratings table (with user_id, survey_date, and rating). Ensure you understand the rating scale and any filters needed.
Compute tenure as the time between registration and survey response, typically using DATEDIFF or date subtraction. Decide on buckets (e.g., <30 days, 30-90 days, >90 days) or treat tenure as a continuous variable.
Write a SQL query that joins the tables, calculates tenure, groups by tenure bucket, and computes average rating and count of responses per group. Use AVG() and COUNT().
Compare average ratings across tenure groups. Optionally, perform a statistical test (e.g., t-test) using SQL or note that further analysis in Python/R is needed. Consider visualizing trends.
Mention the need to control for factors like user activity, survey type, or time trends. Suggest segmenting or using regression to isolate the effect of tenure.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.