Hotspot Question From a website analytics system, you receive data extracts about user interactions such as downloads, link clicks, form submissions, and video plays. The data contains the following…

The correct answer is Date: Date Dimension; EventCategory: Event Dimension; EventAction: Event Dimension; EventLabel: Event Dimension; ChannelGrouping: Channel Dimension; TotalEvents: Fact Table; UniqueEvents: Fact Table; SessionsWithEvents: Fact Table. This question tests your ability to design a star schema by correctly classifying columns from a website analytics dataset into either a Fact table or one of the Dimension tables (Date, User, Event/Interaction).

Submitted by lucia.co· Mar 6, 2026Plan and implement data platform resources

Question

Hotspot Question From a website analytics system, you receive data extracts about user interactions such as downloads, link clicks, form submissions, and video plays. The data contains the following columns: You need to design a star schema to support analytical queries of the data. The star schema will contain four tables including a date dimension. To which table should you add each column? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point. Answer:

Exhibits

Answer Area

DateDate Dimension
Fact TableDate DimensionEvent DimensionChannel Dimension
EventCategoryEvent Dimension
Fact TableDate DimensionEvent DimensionChannel Dimension
EventActionEvent Dimension
Fact TableDate DimensionEvent DimensionChannel Dimension
EventLabelEvent Dimension
Fact TableDate DimensionEvent DimensionChannel Dimension
ChannelGroupingChannel Dimension
Fact TableDate DimensionEvent DimensionChannel Dimension
TotalEventsFact Table
Fact TableDate DimensionEvent DimensionChannel Dimension
UniqueEventsFact Table
Fact TableDate DimensionEvent DimensionChannel Dimension
SessionsWithEventsFact Table
Fact TableDate DimensionEvent DimensionChannel Dimension

Explanation

This question tests your ability to design a star schema by correctly classifying columns from a website analytics dataset into either a Fact table or one of the Dimension tables (Date, User, Event/Interaction).

Approach. In a star schema, the Fact table contains quantitative, measurable metrics (measures) and foreign keys to dimension tables. Dimension tables contain descriptive, contextual attributes used for filtering and grouping. Columns like 'interaction_id', 'duration', 'file_size', or numeric counts belong in the Fact table along with foreign keys (date_key, user_key, event_key). Descriptive attributes like 'event_type' (download, click, form submit, video play), 'event_name', or 'page_name' go into an Event/Interaction dimension. User-related attributes like 'user_agent', 'browser', 'country', or 'device_type' go into a User dimension. Date-related fields like 'year', 'month', 'quarter', 'day_of_week' belong in the Date dimension, while the raw date/timestamp (as a surrogate key) links back to the Fact table.

Concept tested. Star schema design: identifying fact tables (numeric measures + foreign keys) versus dimension tables (descriptive attributes), and correctly partitioning columns from a source dataset into the appropriate table type within a dimensional data warehouse model.

Reference. Microsoft Learn - Design a star schema in Azure Synapse Analytics / Data Warehouse dimensional modeling best practices (Kimball methodology)

Topics

#Star schema#data warehousing#fact table#dimension table#data modeling

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