Took me a minute to internalize that 'first 30 days inclusive' means signup_date + 29, not + 30.
Start by clarifying the date range and the definition of 'new' (within 30 days of signup, inclusive). Then, write a query that joins the activity table with the users table, computes the date difference, and uses a CASE statement to label each row. Ensure that each user-day appears only once by aggregating or using DISTINCT if necessary.
Pro tip: In a real interview, mention that you would validate the date range with the stakeholder and consider edge cases like users who signed up before the range but were active during it. Also, discuss how you would handle time zones if the data spans multiple regions.
Confirm the specific date range for the output and the exact definition of 'new' (e.g., inclusive of the 30th day). Ask about any edge cases, such as users who signed up before the range.
Determine that you need the users table (user_id, signup_date) and the activity table (user_id, activity_date). Ensure you understand how to join them.
Use a CASE statement to check if activity_date is between signup_date and signup_date + 30 days (inclusive). Label as 'new' if true, else 'old'.
Apply a WHERE clause to restrict activity_date to the specified range. Use DISTINCT or GROUP BY to ensure each (user_id, activity_date) appears only once.
Construct the full query, explaining each part. Optionally, discuss performance considerations like indexing on date columns.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The per-day breakdown was fine, just a GROUP BY on date and label after filtering the window.
Clarify the definitions of 'active', 'new vs old', and 'label' from part one, then write SQL or pseudocode that computes daily counts and a window-level distinct count. Explicitly state timezone and boundary assumptions, and validate with edge cases like users active on multiple days.
Pro tip: Mention that you would confirm with stakeholders whether 'new' means first-ever activity or first activity in the window, as this changes the metric significantly. Also, note that distinct counts in a rolling window cannot be summed from daily counts, so you need a separate aggregation.
Restate the labels from part one (e.g., new vs old users) and define 'active' (e.g., any event). State timezone (e.g., UTC) and whether the 30-day window includes today and the start date.
Write a query that groups by date and label, counting distinct active users per day. Use a date filter for the last 30 days including today.
Write a separate query that counts distinct users by label across the entire 30-day window, ensuring no double-counting of users active on multiple days.
Specify how timestamps are converted to dates (e.g., UTC) and whether the window is [today-29 days, today] inclusive. Explain the impact of exclusive bounds.
Discuss potential pitfalls like users changing labels, late-arriving data, and performance considerations for large datasets. Suggest validation checks.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.