The is_deleted filter is easy to forget under pressure and I almost did.
Start by clarifying the schema and definitions (e.g., what counts as a post, comment, non-deleted, and the 24-hour window). Then outline a SQL solution using a LEFT JOIN between posts and comments with a time condition, aggregate per post to flag if it received a qualifying comment, and finally group by post date to compute daily totals and percentages.
Pro tip: Mention that you would validate the 24-hour window using timestamp differences and consider time zones, as Meta operates globally and day boundaries can shift. Also, discuss how to handle posts with no comments (they should count in the denominator but not numerator).
Ask about table structures, definitions of 'non-deleted', and whether the 24-hour window is inclusive. Confirm that the percentage is based on posts created each day, not comments.
Use a subquery or CTE to join posts with comments on post_id, filter for non-deleted comments, and check if comment timestamp is within 24 hours of post creation. Then flag each post as having at least one such comment.
Group by post to get a binary flag per post, then group by the post's creation date to count total posts and sum the flag to get the number of posts with comments.
Calculate the percentage as (posts with comments / total posts) * 100. Ensure days with zero posts are handled (e.g., excluded or shown as 0%).
Consider indexing on post_id and timestamps, and validate results with a small sample. Discuss potential performance issues with large datasets.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This builds directly on the first question which is either helpful or a trap depending on how you structured your CTE.
Start by clarifying the schema and definitions (e.g., what constitutes a post, comment, and non-deleted). Then outline a SQL-based solution that computes per-user post counts and the number of posts with a qualifying comment within 24 hours, filters users with at least 5 posts, calculates the rate, and ranks users with the specified tie-breaking rules.
Pro tip: Explicitly state your assumptions about the data model (e.g., timestamps, deletion flags) and mention how you would handle edge cases like posts with no comments or users with exactly 5 posts. This shows attention to detail and prevents misinterpretation.
Ask clarifying questions about the tables (users, posts, comments), definitions of 'non-deleted comment', 'within 24 hours', and whether the 30-day window applies to post creation or comment creation. Confirm tie-breaking rules.
Select posts created in the last 30 days and comments that are non-deleted. Ensure you consider only comments made within 24 hours of the post's creation time.
For each user, count total posts (with at least 5 posts) and count posts that received at least one qualifying comment within 24 hours. Compute the rate as qualifying posts divided by total posts.
Order users by rate descending, then by total post count descending, then by user_id ascending. Select the top 3.
Mention potential edge cases: posts with multiple comments, comments exactly at 24 hours, users with exactly 5 posts, and how to handle ties beyond the top 3. Suggest validating results with a small sample.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the data model and definitions (e.g., what constitutes a delivered send, an open, and a click). Then outline a SQL-based approach that filters to the campaign, excludes bounced sends, deduplicates opens and clicks per send_id, and aggregates daily metrics. Finally, compute open rate and CTR as unique opens/clicks divided by delivered sends, ensuring the denominator is correct.
Pro tip: Always confirm whether 'delivered' means successfully sent (excluding bounces) or also excluding unsubscribes/complaints; in many email systems, delivered = sent - bounced. Also, consider time zone alignment for daily aggregation, as opens/clicks may be recorded in UTC while the business expects local dates.
Ask about the schema: how sends, opens, and clicks are tracked (e.g., separate event tables or flags). Confirm that 'delivered' excludes bounced sends and that unique opens/clicks are per send_id.
Filter to campaign 'reengage_v1' and exclude bounced sends. For opens and clicks, use DISTINCT on send_id (and date) to count at most one per send per day.
Group by campaign and date. Count delivered sends, unique opens, and unique clicks. Ensure the date is derived consistently (e.g., from send timestamp or event timestamp).
Calculate open rate = unique opens / delivered sends, and CTR = unique clicks / delivered sends. Use safe division to avoid divide-by-zero errors.
Sanity-check numbers (e.g., opens ≤ delivered, clicks ≤ opens). Present the final table with campaign, date, delivered sends, unique opens, unique clicks, open rate, and CTR.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.