##### Question You are given article view events and article metadata. **Table 1: `article_views`** — one row per article view event. | Column | Type | Description | | --- | --- | --- | | `user_id` | INT | The user who viewed the article | | `article_id` | INT | The article that was viewed | | `view_date` | DATE | UTC calendar date of the view | **Table 2: `articles`** — maps each article to its type. | Column | Type | Description | | --- | --- | --- | | `article_id` | INT | Article identifier | | `article_type` | VARCHAR | The category/type of the article | **Relationship:** `article_views.article_id = articles.article_id` **Assumptions** - Each row in `article_views` represents one article view event. - `view_date` is a calendar date; treat dates as UTC and assume no timezone conversion is needed. - If a user views multiple articles of the same `article_type`, that type is counted **only once** for that user (distinct types). Write SQL for the following: 1. **Daily distinct types per user.** For `2019-01-01`, return the number of distinct article types viewed by each user. - Output columns: `user_id, num_article_types` 2. **Histogram across all dates.** Build a histogram of the number of distinct article types viewed per user across the full dataset (all available dates). For each possible value of `num_article_types`, return how many users viewed exactly that many distinct article types. - Output columns: `num_article_types, num_users`