The leakage exclusion is where I got tripped up first.
Start by defining the analysis population: users first exposed between June 1 and June 30, 2025, excluding anyone with a payment before that first exposure. Then, for each user, determine if they converted (made a payment) within 60 days of first exposure, and aggregate by variant to compute counts, conversion rates, and 95% Wald confidence intervals. Use a CTE or subquery to identify first exposures and pre-exposure payments, then join to payments to flag conversions.
Pro tip: Always clarify the definition of 'conversion' and 'exposure' with the interviewer—whether conversion includes any payment or only the first paid conversion, and whether exposure is a specific event like viewing the trial. Also, mention that Wald confidence intervals can be unreliable for small sample sizes or extreme proportions, and suggest alternatives like Wilson intervals if needed.
Use a subquery to select users whose first exposure occurred between June 1 and June 30, 2025, ensuring each user is counted once with their earliest exposure date.
Filter out users who had any payment before their first exposure date, as they are not eligible for the intent-to-treat analysis.
For each eligible user, check if they made a payment within 60 days after their first exposure; flag them as converters if so.
Group by variant (control vs. trial) to calculate the number of exposed users, number of converters, and the conversion rate.
For each variant, compute the standard error of the proportion and derive the 95% Wald confidence interval using the formula: p ± 1.96 * sqrt(p*(1-p)/n).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Simpler than the ITT query structurally, but the conceptual point is that you're no longer doing a clean randomized comparison since trial starters are self-selected.
Start by identifying users in the trial variant who initiated a trial, then join to exposure data to get first exposure timestamps. Compute paid conversion within 60 days of first exposure by checking if a paid conversion event occurred within that window, and finally calculate the conversion rate as the proportion of converted users among trial starters.
Pro tip: Be explicit about how you handle users with multiple exposures or trials—use the first exposure and first trial start to avoid double-counting, and clarify whether the 60-day window is inclusive.
Select users assigned to the trial variant who have a trial start event, ensuring you capture the correct population.
For each user, find the earliest exposure timestamp to establish the start of the 60-day window.
Check for paid conversion events that occur within 60 days after the first exposure timestamp, flagging users who converted.
Compute the paid conversion rate as the number of converted users divided by the total number of trial starters in the trial variant.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Four separate checks, so I wrote four subqueries and unioned them with a label column.
Start by clarifying the schema and definitions of exposures and trials, then write separate SQL queries for each anomaly using CTEs or subqueries for readability. Use window functions and joins to detect duplicates, multiple exposures per day, and temporal inconsistencies, and finally union the results into a single flagging output.
Pro tip: In interviews, always state your assumptions about the data model (e.g., one row per exposure, trial_started_at is a timestamp) and mention that you'd validate these with the data team before finalizing the query. Also, consider edge cases like null timestamps and time zones.
Ask about table structures, column meanings, and what constitutes an 'exposure' and a 'trial record'. Confirm whether user_id is unique in the users table and how exposures are linked to users.
Write individual SQL queries for each anomaly: (1) trial without prior exposure, (2) multiple exposures per day, (3) exposure after trial_started_at, (4) duplicate user_ids. Use CTEs to isolate logic.
For multiple exposures per day, use ROW_NUMBER() or COUNT() OVER (PARTITION BY user_id, DATE(exposure_time)). For trial without prior exposure, use LEFT JOIN and compare timestamps. For duplicates, use GROUP BY with HAVING COUNT(*) > 1.
Use UNION ALL to stack anomaly flags, ensuring consistent columns (e.g., user_id, anomaly_type, details). Alternatively, create a single query with CASE statements if the logic is simple enough.
Mention how you would test the query on sample data, handle nulls, time zones, and performance considerations (e.g., indexing on user_id and timestamps).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.