Straightforward GROUP BY on event_date with a WHERE filter and COUNT(DISTINCT user_id).
Start by clarifying the schema and definitions (e.g., session events table with user_id and event_date). Then write a query that filters events to the 7-day window, groups by event_date, and counts distinct user_ids per day. If the window is relative to a specific date, use a subquery or CTE to define the window and join or filter accordingly.
Pro tip: Mention that you would validate the query against a known metric or sample data to ensure correctness, and discuss how you'd handle edge cases like users with multiple sessions in a day or missing dates.
Confirm the table structure (e.g., events table with user_id, event_type, event_timestamp) and define 'session event' and 'daily active user'. Also clarify the 7-day window (e.g., last 7 days from today or a specific date range).
Use a WHERE clause to restrict events to the desired date range, ensuring you only consider session events. If the window is dynamic, use a subquery to calculate the date range.
Group the filtered events by the date (truncating timestamp to day if needed) and use COUNT(DISTINCT user_id) to compute daily active users for each day.
If days with no events should appear with zero DAU, consider left joining a date spine or using a calendar table to ensure all 7 days are represented.
Check the query against sample data or known metrics. Discuss indexing on event_date and user_id for performance, and consider if the distinct count can be optimized.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the definitions: the 7-day signup window, the 7-day conversion window from each user's signup date, and that a converter is a user with at least one purchase within that window. Then, outline a SQL-based approach: filter users by signup date, join with purchases on user_id and purchase_date within 7 days, deduplicate users, and aggregate by channel to compute conversion rate as distinct converters divided by distinct users per channel.
Pro tip: Emphasize that the conversion window is relative to each user's signup date, not a fixed calendar window, and mention that you would validate the query by checking edge cases like users with multiple purchases and users who signed up on the boundary dates.
Confirm the 7-day signup window (e.g., a specific date range) and that the conversion window is 7 days from each user's signup date. Clarify that a converter is a user with at least one purchase in that window, and that users with multiple purchases are counted once.
Select users who signed up within the specified 7-day window, and extract their acquisition channel (e.g., from a users table). Ensure each user is assigned to exactly one channel.
For each user, check if they have at least one purchase within 7 days of their signup date. Use a left join or exists clause to flag converters, ensuring no double-counting of users with multiple purchases.
Group by channel, count distinct users and distinct converters, then calculate conversion rate as (distinct converters / distinct users) * 100. Present results sorted by channel or conversion rate.
Sanity-check the numbers: ensure total users match the cohort size, and consider edge cases like users with no purchases or purchases exactly on day 7. Discuss any limitations, such as incomplete data for recent signups.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Self-join on the events table, matching user_id where one row has event_date = D and the other has event_date = D+1, both session events.
Use self-joins or correlated subqueries to compare each user's activity on day D with day D+1, then aggregate by cohort date to compute retention. Alternatively, use GROUP BY with conditional aggregation to count retained users and cohort sizes.
Pro tip: Clarify the definition of 'day-1 retention'—whether it's based on calendar days or 24-hour periods—and mention that you'd handle edge cases like users with multiple sessions or timezone differences.
Identify the session table with user_id and session_date. Clarify that cohort day D is the first day a user appears, and retention is measured on D+1.
For each day D, find all distinct users who had a session. This forms the cohort for that day.
For each cohort day D, find users who also had a session on D+1. This can be done with a self-join or EXISTS clause.
Group by cohort date, count distinct retained users and cohort size, then calculate retention rate as retained/cohort size.
Consider users with multiple sessions, missing dates, and timezone issues. Validate results with a small sample.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
MIN(CASE WHEN event_type = 'purchase' THEN event_date END) for first purchase date, SUM(amount) or COALESCE(SUM(amount), 0) for lifetime revenue.
First, clarify the schema and definitions: identify the user, purchase date, and revenue columns, and confirm that 'first purchase date' means the minimum purchase date per user. Then write a single query that groups by user and computes MIN(purchase_date) and SUM(revenue), ensuring you handle potential NULLs or duplicate transactions appropriately.
Pro tip: Mention that while window functions are prohibited, you can still achieve the result with a self-join or subquery if needed, but a simple GROUP BY is sufficient here. Also, note that in a real interview, you'd validate the output against a sample to catch edge cases like users with no purchases.
Ask clarifying questions about the table structure, column names, and definitions (e.g., what constitutes a purchase, how revenue is calculated). Confirm that 'first purchase date' is the earliest date per user and 'total lifetime revenue' is the sum of all purchases.
Determine which table(s) contain user IDs, purchase dates, and revenue amounts. If multiple tables are involved, plan the join logic to combine them before aggregation.
Use a GROUP BY on the user ID column and apply MIN(purchase_date) for the first purchase date and SUM(revenue) for total lifetime revenue. Ensure you handle NULLs appropriately (e.g., exclude NULL revenues or treat as zero).
Check for users with no purchases (they might be excluded or included with NULL/0 values depending on requirements). Also consider time zones, date formats, and whether revenue should be summed per transaction or per user.
Discuss potential performance considerations (e.g., indexing on user_id and purchase_date) and explain why this approach is efficient without window functions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First clarify the schema and definitions (e.g., purchase revenue, channel, date range). Then write a SQL query that filters the 7-day period, aggregates revenue per channel, and selects the top channel using ORDER BY total_revenue DESC, channel ASC with LIMIT 1. If discussing algorithms, explain that sorting or a single-pass max with tie-breaking achieves O(n) time.
Pro tip: Explicitly state your assumptions about the data model and edge cases (e.g., ties, nulls, timezone) before writing the query—this shows you think like a data scientist who cares about correctness, not just syntax.
Ask about the table structure, column names, definition of 'purchase revenue', and how the 7-day period is defined (inclusive dates, timezone). Confirm tie-breaking rule: lexicographically smallest channel name.
Decide to group by channel, sum revenue, then order by total revenue descending and channel ascending, and limit to 1 row. Consider if you need to handle ties explicitly or if ORDER BY with LIMIT suffices.
Construct a query like: SELECT channel, SUM(revenue) AS total_revenue FROM purchases WHERE date BETWEEN 'start' AND 'end' GROUP BY channel ORDER BY total_revenue DESC, channel ASC LIMIT 1;
If asked about implementation without SQL, explain that you can scan the data once, maintain a running max per channel, and apply tie-breaking. Mention time complexity O(n) and space O(k) for k channels.
Mention testing with ties, empty results, and null channels. Ensure the query returns exactly one row and that tie-breaking is correctly applied.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.