The denominator piece is where I almost tripped up.
Start by identifying the correct denominator: all users from the people_users table, including those with zero notifications. Then, determine which users have at least one unread notification by joining to the notifications table and filtering for unread status. Finally, compute the numerator, denominator, and percentage, rounding the percentage to two decimal places.
Pro tip: Always clarify the definition of 'unread' (e.g., a status column or a read_at timestamp) and confirm whether the denominator should include all users or only active users. Explicitly state your assumptions to avoid ambiguity.
Identify the relevant columns: user ID in people_users, and user ID, notification status (e.g., is_read or read_at) in notifications. Confirm what 'unread' means and whether all users should be included in the denominator.
Count all users from the people_users table. This ensures users with zero notifications are included.
Count distinct users who have at least one unread notification. Use a subquery or join with a filter on unread status, and ensure each user is counted only once.
Divide the numerator by the denominator, multiply by 100, and round to two decimal places. Return all three values.
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I found this trickier than part (a) because you need a cross join or a similar expansion to correctly assign each user a row per type, then check for unread presence per type.
Start by clarifying the schema and definitions: what constitutes an unread notification, how notification types are identified, and whether the user count is over all users or only those who received that notification type. Then outline a SQL query that joins notifications with users, filters for unread, and aggregates per notification type, computing distinct user counts and percentages relative to the total user base. Finally, discuss how to handle edge cases like users with no notifications or multiple unread notifications of the same type.
Pro tip: Always clarify whether the denominator should be all users or only users who received that notification type, as this drastically changes the percentage and is a common point of confusion in product analytics interviews.
Ask questions to confirm what 'unread' means (e.g., status = 'unread'), how notification types are defined, and whether the total user count is the entire user base or only users who received that type. Also confirm if a user can have multiple unread notifications of the same type and should be counted once.
Determine the tables needed: likely a notifications table with user_id, notification_type, and status; and a users table for the total user count. Ensure you know the join keys and any filters (e.g., date range, active users).
Use a subquery or CTE to get distinct user counts per notification type for unread notifications. Then compute the percentage by dividing by the total user count (or the count of users who received that type, depending on clarification). Use LEFT JOIN or UNION to ensure all notification types are included even if no unread notifications exist.
Check for anomalies: percentages over 100%, missing types, or unexpected counts. Discuss how to interpret the results in a product context, such as which notification types have high unread rates and potential reasons.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Two separate percentages in one question, which is easy to conflate.
Clarify the definitions of 'distinct persons' and 'associated user account', then compute the two percentages using SQL or equivalent by grouping user accounts by person. Ensure to handle edge cases like nulls and duplicates, and consider the time frame for the analysis.
Pro tip: Always state your assumptions about what constitutes a 'person' (e.g., email, phone, or a unique person ID) and how accounts are linked; this shows you understand data modeling nuances and prevents misinterpretation.
Define what a 'distinct person' means (e.g., based on a person_id or a combination of identifiers) and what constitutes an 'associated user account'. State any assumptions about data completeness and linkage.
Locate the tables containing person identifiers and user account identifiers. Determine the join keys and any filters needed (e.g., active accounts, time period).
Calculate the percentage of distinct persons who have more than one user account. This involves grouping by person and counting accounts, then dividing the count of persons with >1 account by the total distinct persons.
Calculate the percentage of all user_ids that belong to multi-account persons. This involves summing the number of accounts for persons with >1 account and dividing by the total number of user_ids.
Check for data quality issues (e.g., duplicates, nulls) and validate the results. Interpret the percentages in the context of the business question, considering implications for user behavior or product metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I added a CTE at the top that does SELECT DISTINCT on notification_id, taking the MIN or MAX of the other columns, or alternatively used ROW_NUMBER() partitioned by notification_id to keep one row per id.
Start by clarifying the deduplication assumption: whether duplicate rows are exact duplicates or represent multiple events, and which columns define uniqueness. Then propose SQL techniques like DISTINCT, ROW_NUMBER() with a window function, or GROUP BY to deduplicate, and explain how to embed the assumption in a comment for maintainability.
Pro tip: Mention that deduplication should happen as early as possible in the query pipeline to avoid skewing downstream aggregations, and always validate the assumption with a quick COUNT vs COUNT(DISTINCT) check before applying logic.
Determine whether duplicates are exact copies or distinct events, and identify the key columns (e.g., notification_id) that define uniqueness. Document this assumption in a SQL comment.
Select an appropriate SQL method: DISTINCT for exact duplicates, ROW_NUMBER() for keeping the latest/earliest record, or GROUP BY for aggregating duplicates.
Apply the chosen method in a subquery or CTE to create a clean dataset before joining or aggregating, ensuring duplicates don't affect results.
Run sanity checks like comparing row counts before and after deduplication, and test edge cases (e.g., all duplicates, no duplicates) to ensure robustness.
Discuss performance implications (e.g., window functions vs. DISTINCT) and correctness trade-offs (e.g., losing event-level detail) to demonstrate technical maturity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.