Which are two considerations to keep in mind when architecting data using dimensional entities? (Choose two.)

Question

Options

• ADimensional entities can be used as lookups or classifications to simplify the data modeling
• BDimensional entities require secondary identities to link to profiles through schema relationships.
• CProfile data populated through dimensional entities require batch segmentation evaluation.
• DDimensional entities can be either time series data or record data.

• A
  72% (23)
• B
  19% (6)
• D
  9% (3)

Explanation

Dimensional entities serve as reference or lookup tables (e.g., product catalogs, store locations) that simplify your data model by avoiding redundant data stored directly in profiles - making A correct. When a segment definition relies on data joined from a dimensional entity, the platform cannot evaluate it in real time and must fall back to batch segmentation - making C correct.

B is wrong because dimensional entities use a primary identity (not a secondary one) to establish the schema relationship back to the profile; the relationship is defined on the profile side referencing the dimensional entity's primary key.

D is wrong because dimensional entities are strictly record data - they represent static or slowly-changing reference information. Time series data (like ExperienceEvents) is a separate behavioral type in XDM and is never dimensional.

Memory tip: Think "Dimensional = Dictionary" - just like a dictionary is a lookup reference (A) that you batch-check rather than stream (C), and a dictionary is a static record, not a timeline of events (ruling out D).

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