← Robinhood Interview Insights
My first instinct was to just filter the current snapshot for state = 'CA' and call it a day.
First, clarify the table schemas and the definition of 'always in CA' as of 2021-01-01. Then, use a window function or subquery to find the latest state before or on that date for each user, and filter for users whose state is CA and who have no prior non-CA state.
Pro tip: Mention that you would validate the query with edge cases, such as users with no edit logs or multiple edits on the same day, to ensure correctness.
Identify the user info table (e.g., user_id, state) and edit log table (e.g., user_id, state, edit_date). Clarify how state changes are recorded and whether the user info table reflects current state or initial state.
A user is 'always in CA as of 2021-01-01' if their state on that date is CA and they have never been in a non-CA state before that date. This means the earliest state change (if any) must be to CA, and no non-CA state appears before 2021-01-01.
For each user, find the most recent state change on or before 2021-01-01. If no change exists, use the initial state from the user info table. This can be done with a correlated subquery or window function.
Filter out users who have any record in the edit log with a non-CA state and an edit_date before 2021-01-01. Also consider the initial state from the user info table if it is non-CA.
Select users whose state as of 2021-01-01 is CA and who have no prior non-CA state. Use CTEs or subqueries for clarity, and consider performance implications.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the schema of the two tables (likely a users table and a location history table). Then, use a self-join or window functions to find users who have a record of moving to CA before 2021-01-01 and no subsequent move out of CA before or on that date, ensuring they were still in CA as of that date.
Pro tip: Explicitly state your assumptions about the table schemas and date semantics (e.g., whether the date represents the start of residence). This shows you think about data modeling and edge cases, which is crucial for a fintech company like Robinhood.
Ask or state the columns of the two tables, such as users(user_id, ...) and user_locations(user_id, state, move_date). Assume move_date is the date the user started living in that state.
Filter the location history for records where state = 'CA' and move_date < '2021-01-01'. This gives a set of users who moved to CA before the cutoff.
For each such user, check that there is no subsequent move to a different state with a move_date <= '2021-01-01'. This can be done with a NOT EXISTS subquery or by finding the latest state before the date.
Use a query that joins or filters to return distinct user_ids that satisfy both conditions. Consider using window functions like ROW_NUMBER to get the latest state per user before the date.
Mention testing scenarios like users who moved to CA exactly on 2021-01-01 (should be excluded), users who moved to CA then left before the date, and users with multiple moves.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Use a self-join on the user activity log table to find users who had a 'CA' entry on or before 2021-01-01 and a later entry with a different state. Ensure the later entry is the first state change after that date to avoid false positives from multiple moves.
Pro tip: Clarify the table schema and whether 'as of 2021-01-01' means the most recent state before that date or any state on that date. Also, consider using window functions for efficiency and to handle multiple moves correctly.
Identify the table containing user state logs, with columns like user_id, state, and timestamp. Clarify that 'in CA as of 2021-01-01' means the user's state was CA on that date, and 'subsequent log entry' means a later record showing a different state.
Select users who have a log entry with state = 'CA' and timestamp <= '2021-01-01', ensuring it's the latest entry before or on that date. Use a subquery or window function to get the most recent state per user up to that date.
For those users, find log entries after 2021-01-01 where the state is not 'CA'. Ensure that the change is from CA to another state, not just any non-CA entry.
Join the results to return distinct user_ids that satisfy both conditions. Optionally, include the new state and the date of change for context.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.