Start by clarifying the schema and definitions (e.g., what constitutes a view, how to handle nulls). Then write a SQL query that aggregates views per user-director, computes unique viewers and total watch-hours, and uses a window function or subquery to calculate the median watch-time per viewer for each director.
Pro tip: Mention that you would validate the results by checking edge cases (e.g., directors with few viewers) and consider performance implications of median calculation on large datasets, perhaps using approximate percentiles if exact median is too slow.
Identify the relevant tables (views, directors, users) and their columns. Clarify what 'unique viewer' means (distinct user_id per director) and how to compute watch-hours (sum of seconds / 3600).
Filter views to August 2022. Group by director and user to sum watch seconds per user per director, ensuring multiple views by the same user for the same director are combined.
From the aggregated data, count distinct users per director for unique viewers and sum the total seconds per director, then convert to hours.
Use a window function (e.g., PERCENTILE_CONT) or a subquery with row numbering to find the median of the summed seconds per user for each director.
Join the unique viewers, total watch-hours, and median watch-time results by director, ensuring the output is clean and formatted.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the experiment design and data schema, then write a SQL query that filters users to those exposed before any cancellation, computes cancellation events within 7 days post-exposure, and aggregates rates by variant. Validate the logic with edge cases and ensure the denominator includes only eligible users.
Pro tip: Always confirm the definition of 'cancellation' and 'exposure' with the interviewer—whether it's subscription cancellation, content cancellation, or something else—and check if multiple exposures per user exist. Also, consider using a window function to handle the 'before any cancellation' condition efficiently.
Ask about the experiment design, variant assignment, exposure and cancellation event tables, and how to identify users. Confirm the exact definition of cancellation and the 7-day window.
Select users whose exposure timestamp is strictly before their first cancellation timestamp (or who have no cancellation). This ensures we only include users at risk of cancelling after exposure.
For each eligible user, check if they have a cancellation event that occurs strictly after exposure and within 7 days (i.e., cancellation_time > exposure_time AND cancellation_time <= exposure_time + 7 days).
Group by variant and compute the cancellation rate as the number of users with an in-window cancellation divided by the total number of eligible users in that variant.
Sanity-check the results (e.g., rates between 0 and 1, no missing variants), and consider statistical significance if needed. Present the rates clearly, perhaps with confidence intervals.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining the exposure cohort: users who were exposed to the experiment before their first view in August 2022. Then filter August views to those users, aggregate unique viewers and total watch-hours per title per variant, and rank titles within each variant using the specified tie-breakers. Finally, select the top 3 titles per variant.
Pro tip: Clarify whether 'exposed before their first August view' means exposure timestamp < first view timestamp in August, and ensure you handle users with multiple exposures correctly. Also, confirm that unique viewers are counted per title per variant, not globally.
Identify users who were exposed to the experiment (variant A or B) before their first view in August 2022. This requires joining exposure data with view data and comparing timestamps.
Restrict to views that occurred in August 2022 and belong to users in the exposure cohort. Ensure each view is associated with the user's assigned variant.
For each title and variant, compute the number of unique viewers and total watch-hours. Use COUNT(DISTINCT user_id) for unique viewers and SUM(watch_hours) for total watch-hours.
Within each variant, rank titles by unique viewers descending, then by total watch-hours descending, then by title alphabetically ascending. Select the top 3 titles per variant.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.