You are given two CSV files exported from a document collaboration product. ## Data ### 1) `doc_access.csv` Each row represents a user accessing (viewing/editing) a document page. | column | type | description | |---|---|---| | `page_id` | STRING/INT | Document/page identifier | | `user_id` | STRING/INT | User who accessed the document | | `event_ts` | TIMESTAMP | When the access happened (assume UTC) | | `is_creator` | BOOLEAN | `TRUE` if this user is the creator/owner of the document; otherwise the user is a collaborator | | `collab_source` | STRING | For collaborators only (`is_creator=FALSE`): how the user became a collaborator (e.g., invited, link share, org default, etc.) | Notes/assumptions: - A “collaborator” is any record with `is_creator = FALSE`. - A user may access the same `page_id` multiple times; unless otherwise specified, treat collaboration at the **document level** (i.e., dedupe by `(page_id, user_id)` where appropriate). ### 2) `users.csv` | column | type | description | |---|---|---| | `user_id` | STRING/INT | User identifier | | `country` | STRING | User’s country | ## Tasks ### Q1) Distribution of collaborator sources Compute the distribution of how collaborators got onto documents. **Output:** - `collab_source` - `num_collaborators` (count of collaborator users; specify whether you count unique users overall or unique `(page_id, user_id)` pairs) - `pct_of_collaborators` ### Q2) For a given user, who did they collaborate with most? Given an input parameter `target_user_id`, find the other user they collaborated with the most. Define “collaborated with” as: the two users both appear on the same `page_id` (after deduping to unique `(page_id, user_id)` memberships). **Output:** - `target_user_id` - `top_collaborator_user_id` - `num_shared_pages` (number of distinct `page_id` where both users appear) Also describe how you would break ties (e.g., smallest user_id, most recent shared collaboration, etc.). ### Q3) Which country collaborates the most? Determine which country’s users are most likely to collaborate with others. Because “collaboration rate” can be defined multiple ways, do the following: 1. Propose a clear numerator and denominator for a per-country collaboration rate. - Example definition (acceptable): - Numerator: # of users in the country who have collaborated with at least one other distinct user on at least one page. - Denominator: total # of users in the country. 2. Compute the collaboration rate by country and return the top country. **Output:** - `country` - `collaboration_rate` - any intermediate counts you used (e.g., `num_users_in_country`, `num_users_who_collaborated`).