The country join tripped me up at first because you pull country from the users table but the experiment table only has user_id.
Start by clarifying the schema and definitions: identify the user table with country and exposure date, the experiment assignment table with group, and the group call events table. Then write a query that filters to treatment users exposed on or before the cutoff, left joins to call events within the experiment week, and computes the share of users with at least one call per country.
Pro tip: Explicitly state your assumptions about the experiment week (e.g., 7 days from exposure) and whether 'started or joined' means any call event; this shows you think about metric definitions and avoids ambiguity. Also, use a LEFT JOIN with a conditional COUNT to handle users with zero calls correctly.
Ask about table structures, column names, and precise definitions: what constitutes a group call event, how exposure date is recorded, and the exact experiment week window. Confirm that 'share' means the proportion of treatment users with at least one call.
Select users in the treatment group who were exposed on or before the cutoff date. Ensure you have their country and a unique user identifier.
Join the cohort to the group call events table on user ID, filtering events to the experiment week (e.g., between exposure date and exposure date + 6 days). Use a LEFT JOIN to retain users with no calls.
Group by country and compute the share as COUNT(DISTINCT users with at least one call) / COUNT(DISTINCT all users in cohort). Use conditional aggregation or a subquery to count distinct users with calls.
Check for edge cases: users with multiple calls, missing country data, or exposure dates outside the cutoff. Present the query with clear comments and explain how you would validate the results.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
DiD in SQL is not something I practice enough.
First, clarify the experiment design: treatment vs control groups, pre-period and experiment week, and the metric (per-user 1:1 call counts). Then, write a SQL query that aggregates call counts per user per period, computes group-level averages, and calculates the difference-in-differences estimate as (treatment_post - treatment_pre) - (control_post - control_pre).
Pro tip: Always check the parallel trends assumption by comparing pre-period trends; if violated, consider alternative methods like synthetic control. Also, use per-user averages rather than total counts to avoid bias from group size differences.
Confirm the treatment and control groups, the pre-period and experiment week dates, and that the metric is per-user 1:1 call counts. Ensure you understand how users are assigned to groups.
Write a subquery to count 1:1 calls for each user in each period (pre and post), joining with the experiment assignment table to label users as treatment or control.
Calculate the average per-user call count for each group (treatment/control) and period (pre/post) by aggregating the user-level counts.
Compute the DiD estimate as (treatment_post_avg - treatment_pre_avg) - (control_post_avg - control_pre_avg). This isolates the treatment effect from underlying trends.
Check parallel trends in the pre-period and consider statistical significance. Interpret the DiD estimate as the cannibalization effect if negative.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the data model and defining what constitutes a 'call' and how participants are linked to calls. Then, write a query that joins call participants with experiment assignments, filters to the experiment week, groups by call, and counts distinct treatment and control participants per call. Finally, filter to calls with both variants present, order by total participants (or another metric), and limit to top 5.
Pro tip: Mention that you would validate the query by checking for edge cases like calls with only one variant or missing experiment assignments, and consider using window functions for ranking if ties are possible.
Ask about the schema: how calls are identified, how participants are linked to calls, and how experiment assignments are stored. Confirm the definition of 'experiment week' and 'top' (e.g., by total participants).
Filter experiment assignments to the experiment week and join with call participants to get each participant's variant per call. Ensure you handle only calls that occurred during the experiment week.
Group by call ID and count the number of distinct treatment and control participants. Use conditional aggregation (e.g., SUM(CASE WHEN variant = 'treatment' THEN 1 ELSE 0 END)) to get counts per variant.
Filter to calls where both treatment and control counts are greater than zero. Then order by total participants (or another relevant metric) descending and limit to 5.
Check for duplicates, missing data, and performance. Consider indexing on join keys and using window functions if ties need handling.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.