What are characteristics of advanced data ingestion techniques in Adobe RT-CDP? (Select two.)

Question

Options

• ALeveraging machine learning for data normalization.
• BConfiguring ingestion workflows for hybrid batch and streaming use cases.
• CMapping data directly to campaign activation rules.
• DUsing custom schemas for flexible data structures.

• A
  11% (5)
• B
  71% (32)
• C
  18% (8)

Explanation

Advanced RT-CDP data ingestion centers on flexibility at the data layer - specifically in how data arrives and what structure it arrives in. Option B is correct because RT-CDP natively supports hybrid ingestion pipelines that handle both scheduled batch uploads (e.g., CRM exports) and real-time streaming sources (e.g., web events via the Edge Network) within the same workflow. Option D is correct because RT-CDP's ingestion framework uses the Experience Data Model (XDM), which supports custom schemas - allowing organizations to map proprietary or non-standard data structures into the platform without being locked into rigid field definitions.

Option A is wrong because machine learning for normalization is not part of RT-CDP's ingestion layer; data prep and field mapping (not ML) handle normalization at ingestion time. Option C is wrong because mapping data to campaign activation rules describes downstream segmentation and activation logic, not an ingestion technique.

Memory tip: Think "Batch/streaming Bridge + Dynamic schemas = ingestion." If an answer describes what happens after data lands (ML transforms, activation rules), it belongs to a different layer - ingestion is only about how data enters and what shape it takes.

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