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Salesforce Certified Data Cloud Consultant · Salesforce
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Letter B. Streaming Transforms: allows to process and normalize data in real-time as it is ingested into Data Cloud. In this case, the consultant can use streaming transforms to split the three phone numbers (mobile, home, and work) into separate rows in a new Phone Data Lake Object (DLO). Normalization: by normalizing the phone numbers into a separate DLO, each phone number can be treated as an individual record, making it easier to map and use for activation. Mapping to Contact Point Phone: once the phone numbers are normalized into the new DLO, they can be mapped to the Contact Point Phone data map object, which is the standard object in Data Cloud for managing phone-related data. It's not A because this approach does not normalize the phone numbers into separate rows, making it less efficient for activation and segmentation.
The most efficient approach to ensure all the different phone numbers are properly mapped and available for use in activation is: A. Ingest the Contact object and map the Work Phone, Mobile Phone, and Home Phone to the Contact Point Phone data map object from the Contact data stream by adding custom fields for Work and Home Phone. This approach allows you to directly map each phone number type to the appropriate fields in the Contact Point Phone data map object, ensuring that all phone numbers are properly ingested and available for use in activation.
B. Ingest the Contact object and use streaming transforms to normalize the phone numbers from the Contact data stream into a separate Phone data lake object (DLO) that contains three rows, and then map this new DLO to the Contact Point Phone data map object. This approach ensures that each phone number type (Work, Mobile, Home) is properly normalized and mapped into a structured format that can be easily used for activation. By creating a separate Phone data lake object (DLO) with three rows, you ensure that each phone number is treated as a distinct contact point, which aligns with best practices for data normalization and mapping in Salesforce Data Cloud.