Used a window function to get the first date per user, then joined back on the same table filtering where the date difference was within 7 days.
Start by clarifying the table schema and assumptions (e.g., transaction date is a timestamp, user_id is unique). Then use a window function to compute each user's first transaction date, join it back to the original table, and filter transactions within 7 days of that date. Finally, group by user and count the filtered transactions.
Pro tip: Mention that you would validate the query by checking edge cases like users with only one transaction or transactions exactly on the 7th day, and discuss how to handle time zones if timestamps are involved.
Ask about the table structure, column names, data types, and whether 'within 7 days' includes the first day or is strictly after. Confirm if a user can have multiple transactions on the same day.
Use a window function like MIN(transaction_date) OVER (PARTITION BY user_id) or a subquery with GROUP BY user_id to get the first transaction date for each user.
Join the first transaction dates back to the original table on user_id, then filter rows where transaction_date is between first_transaction_date and first_transaction_date + INTERVAL '7 days' (inclusive or exclusive based on clarification).
Group by user_id and first_transaction_date, then count the number of transactions in the filtered set. Ensure the count includes the first transaction if it falls within the window.
Check for performance considerations (e.g., indexing on user_id and transaction_date) and discuss potential alternative approaches like using a self-join or correlated subquery.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one took me longer than it should have.
Clarify the definition of Day-7 retention (e.g., user returns exactly on day 7 or within 7 days) and the relevant time window. Then write a SQL query that identifies each user's first activity date (cohort date) and checks for activity on day 7, computing the ratio of retained users to total users.
Pro tip: Always state your assumptions about the retention window and timezone; in a streaming context, a user might be counted as retained if they watch any content on day 7, but some teams use a 24-hour grace period. Mentioning this shows you understand business nuances.
Clarify what 'Day-7 retention' means: is it activity exactly on day 7 after signup, or within a 7-day window? Also confirm the event that counts as activity (e.g., login, watch).
Determine each user's first activity date (cohort date) using a subquery or window function, typically from a user activity or signup table.
Join the cohort data with activity data to check if each user had any qualifying activity on the 7th day after their cohort date.
Calculate the ratio of users with Day-7 activity to the total number of users in the cohort, using COUNT and division.
Consider timezone consistency, incomplete data for recent cohorts, and whether to include users who never returned. Validate with a small sample if possible.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Sort the list, then two pointers from each end.
Start by clarifying the problem and edge cases, then propose an efficient algorithm. A common optimal approach is to sort the list and use a two-pointer technique to find the pair with sum closest to zero in O(n log n) time. Alternatively, you could mention a hash-based approach but note its trade-offs.
Pro tip: Mention that sorting modifies the input; if the original order must be preserved, you can store indices or use a different approach. Also, discuss how you would handle duplicates and return the actual values, not indices.
Ask about input constraints, duplicates, and whether the function should return the numbers or their indices. Confirm that the list has at least two elements and that the sum closest to zero is unique or how to handle ties.
Propose sorting the list and using two pointers (left and right) to find the pair with sum closest to zero. Explain that this balances efficiency and simplicity, with O(n log n) time due to sorting.
Initialize left at start, right at end. Compute sum, update closest if absolute sum is smaller. If sum < 0, increment left; if sum > 0, decrement right; if sum == 0, return immediately.
Consider cases like all positive or all negative numbers, duplicates, and ties. Return the two numbers (or indices) as specified. If ties, decide based on problem statement or mention ambiguity.
State time complexity O(n log n) and space O(1) if sorting in place. Suggest testing with small examples, including edge cases, to verify correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.