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Start by clarifying the event schema and defining what constitutes a unique viewer and a completed order. Use a CTE to deduplicate exact duplicate events, then aggregate daily unique viewers and completed orders separately, and finally join them to compute the conversion rate. Ensure the query handles days with zero orders or viewers gracefully.
Pro tip: Explicitly state your assumptions about the data model (e.g., event types, user identifiers) and mention that you'd validate the deduplication logic with a quick count before and after. This shows you think about data quality and edge cases, which is crucial for a data scientist at Snowflake.
Ask about the event table schema, how to identify unique viewers (e.g., user_id, session_id), and what defines a completed order (e.g., event_type = 'order_completed'). Confirm the 7-day UTC window boundaries.
Use a CTE with SELECT DISTINCT or ROW_NUMBER() to remove exact duplicate rows, ensuring each event is counted once.
Write separate aggregations: one for unique viewers per day (COUNT(DISTINCT viewer_id)) and one for completed orders per day (COUNT(*) where event_type = 'order_completed').
Join the two daily aggregates on date, then calculate conversion rate as completed_orders / unique_viewers, handling division by zero.
Ensure the query is a single Standard SQL statement, uses proper date filtering for the 7-day window, and consider adding a sanity check for the conversion rate bounds.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The 'no correlated subqueries' constraint is the whole point of this question and I almost missed it.
Use a window function like MIN, MAX, and COUNT with an OVER clause partitioned by user_id and ordered by order timestamp, filtered to completed orders within the 7-day window. Ensure the window frame covers the entire partition to get the first and last timestamps and total count, and handle NULLs for users with no completed orders using a LEFT JOIN or COALESCE.
Pro tip: Mention that Snowflake's QUALIFY clause can filter window function results without a subquery, and that using IGNORE NULLS in FIRST_VALUE/LAST_VALUE can elegantly handle missing timestamps.
Restrict to completed orders within the 7-day window and select only the necessary columns (user_id, order_timestamp).
Use MIN(order_timestamp) OVER (PARTITION BY user_id) for first order, MAX(order_timestamp) OVER (PARTITION BY user_id) for last order, and COUNT(*) OVER (PARTITION BY user_id) for total count.
Use a LEFT JOIN from a distinct list of users to the aggregated results, or use COALESCE to return NULL for last order timestamp and 0 for count.
Since window functions return values for each row, use DISTINCT or GROUP BY to return one row per user, or use QUALIFY to filter to one row per user.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Straightforward ROW_NUMBER over the five columns, filter to rn=1.
Start by clarifying the deduplication logic and business context, then propose a SQL-based solution using window functions like ROW_NUMBER() to identify and remove duplicates. Discuss trade-offs between different methods (e.g., GROUP BY vs. window functions) and consider performance implications in Snowflake.
Pro tip: Mention that using ROW_NUMBER() with a deterministic ORDER BY (e.g., by ingestion timestamp) ensures reproducibility and allows you to keep the most recent record, which is often preferred in event deduplication.
Confirm the definition of duplicates and the desired outcome: keep one row per group, but ask which row to keep (e.g., first, last, or based on another column).
Decide between using GROUP BY with aggregate functions or window functions like ROW_NUMBER(). Window functions are more flexible for keeping specific rows.
Construct a query using ROW_NUMBER() OVER (PARTITION BY user_id, event_timestamp, event_type, product_id, device_id ORDER BY <tiebreaker>) and filter for row_number = 1.
Discuss how Snowflake handles window functions on large datasets, and mention clustering or partitioning strategies if needed.
Suggest validating the deduplication by checking counts before and after, and testing edge cases like nulls or ties in the ORDER BY column.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I sketched drop_duplicates on the five key columns, then a boolean mask for the date range, then groupby on the date part of the timestamp.
Walk through the SQL logic step by step, mapping each operation to a Pandas equivalent: timezone conversion, deduplication, and aggregation. Emphasize correctness and efficiency, using vectorized operations and proper handling of UTC boundaries. Conclude by validating results against expected SQL output.
Pro tip: Mention that you'd use `pd.Timestamp` with timezone-aware datetimes to avoid DST issues, and that deduplication should be done before aggregation to prevent double-counting. Also, highlight the importance of using `groupby` with `nunique` for distinct counts.
Read the data into a Pandas DataFrame, ensuring timestamp columns are parsed as datetime with UTC timezone. Convert to the desired timezone if needed.
Filter events to the correct UTC time window (e.g., last 30 days) using boolean indexing with timezone-aware comparisons.
Remove duplicate events based on a unique key (e.g., event_id or user_id + timestamp) using `drop_duplicates` before aggregation.
Group by date (after converting to date) and calculate unique viewers with `nunique` and completed orders with a filtered count or sum.
Compare results with SQL output, handle missing dates, and format the final DataFrame for presentation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.