Compute 7-day views and reactions by relationship

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Q: What difficulty level is this coding question?

This is a Medium difficulty Data Manipulation (SQL/Python) question, commonly asked during Technical Screen rounds at Meta.

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This question is commonly asked for Data Scientist candidates at Meta during technical interviews.

Question

Use the schemas and sample data below to answer two tasks. Assume dates are strings in 'YYYY-MM-DD'. Treat "today" as 2025-09-01; "last/past 7 days" means ds between 2025-08-26 and 2025-09-01 inclusive. If a viewer has multiple views of the same post on the same day, deduplicate by keeping the row with the maximum duration.

Schemas:
- info_stream_views(post_id BIGINT, viewer_id BIGINT, relationship STRING CHECK (relationship IN ('Friend','Followee','Unconnected')), duration DOUBLE, ds STRING)
- post_reactions(post_id INTEGER, viewer_id INTEGER, post_action STRING CHECK (post_action IN ('like','comment','reshare')), ds STRING)

ASCII sample tables:
info_stream_views
post_id | viewer_id | relationship | duration | ds
101 | 10 | Friend | 45 | 2025-08-26
101 | 10 | Friend | 75 | 2025-08-27
101 | 11 | Unconnected | 61 | 2025-08-28
102 | 12 | Unconnected | 30 | 2025-08-28
103 | 13 | Followee | 90 | 2025-08-29
104 | 14 | Unconnected | 120 | 2025-08-30
104 | 14 | Unconnected | 20 | 2025-08-30
105 | 10 | Unconnected | 59 | 2025-09-01

post_reactions
post_id | viewer_id | post_action | ds
101 | 10 | like | 2025-08-27
101 | 11 | comment | 2025-08-28
104 | 14 | reshare | 2025-08-30
104 | 14 | like | 2025-08-30
103 | 13 | like | 2025-08-29
105 | 10 | like | 2025-09-01

Tasks:
Q1) Write a single SQL query that returns the count of DISTINCT post_id that had at least one Unconnected view with duration > 60 seconds within 2025-08-26..2025-09-01. Ensure a post is counted once even if multiple qualifying viewers/days exist.
Q2) For the same 7-day window, compute two numbers: avg_reactions_per_post for Friend and for Unconnected. Define reactions-to-relationship by joining reactions to views on (post_id, viewer_id, ds) and, if multiple same-day views exist, attribute the reaction to the view with the longest duration. For each relationship group G in {'Friend','Unconnected'}: numerator = total count of reactions made by viewers labeled G; denominator = count of DISTINCT post_id that had at least one view by viewers labeled G in the window (include posts with zero reactions in the denominator). Return a two-row result set: relationship, avg_reactions_per_post (as a decimal). Use window functions or CTEs to handle deduping and joins, and be robust to posts that appear in both groups.

PracHub · Accepted Answer

This question evaluates skills in SQL-based data manipulation—specifically deduplication by longest duration, join-based attribution of reactions to views, window functions, distinct counts, aggregations, and date-range filtering within the Data Manipulation (SQL/Python) domain.

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