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Which configuration supports separate Amazon S3 buckets for data ingestion and activation?
A. Dedicated S3 data sources in Data Cloud setup
B. Multiple S3 connectors in Data Cloud setup
C. Dedicated S3 data sources in activation setup
D. Separate user credentials for data stream and activation target
Explanation:
To support separate Amazon S3 buckets for data ingestion and activation, you need to configure dedicated S3 data sources in Data Cloud setup. Data sources are used to identify the origin and type of the data that you ingest into Data Cloud1. You can create different data sources for each S3 bucket that you want to use for ingestion or activation, and specify the bucket name, region, and access credentials2. This way, you can separate and organize your data by different criteria, such as brand, region, product, or business unit3. The other options are incorrect because they do not support separate S3 buckets for data ingestion and activation. Multiple S3 connectors are not a valid configuration in Data Cloud setup, as there is only one S3 connector available4. Dedicated S3 data sources in activation setup are not a valid configuration either, as activation setup does not require data sources, but activation targets5. Separate user credentials for data stream and activation target are not sufficient to support separate S3 buckets, as you also need to specify the bucket name and region for each data source2.
Cumulus Financial created a segment called High Investment Balance Customers. This is a foundational segment that includes several segmentation criteria the marketing team should consistently use. Which feature should the consultant suggest the marketing team use to ensure this consistency when creating future, more refined segments?
A. Create new segments using nested segments.
B. Create a High Investment Balance calculated insight.
C. Package High Investment Balance Customers in a data kit.
D. Create new segments by cloning High Investment Balance Customers.
Explanation:
Nested segments are segments that include or exclude one or more existing segments. They allow the marketing team to reuse filters and maintain consistency in their data by using an existing segment to build a new one. For example, the marketing team can create a nested segment that includes High Investment Balance Customers and excludes customers who have opted out of email marketing. This way, they can leverage the foundational segment and apply additional criteria without duplicating the rules. The other options are not the best features to ensure consistency because:
B. A calculated insight is a data object that performs calculations on data lake objects or CRM data and returns a result. It is not a segment and cannot be used for activation or personalization.
C. A data kit is a bundle of packageable metadata that can be exported and imported across Data Cloud orgs. It is not a feature for creating segments, but rather for sharing components.
D. Cloning a segment creates a copy of the segment with the same rules and filters. It does not allow the marketing team to add or remove criteria from the original segment, and it may create confusion and redundancy.
Cumulus Financial uses Service Cloud as its CRM and stores mobile phone, home phone, and work phone as three separate fields for its customers on the Contact record. The company plansz to use Data Cloud and ingest the Contact object via the CRM Connector. What is the most efficient approach that a consultant should take when ingesting this data to ensure all the different phone numbers are properly mapped and available for use in activation?
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.
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.
C. Ingest the Contact object and then create a calculated insight to normalize the phone numbers, and then map to the Contact Point Phone data map object.
D. Ingest the Contact object and create formula fields in the Contact data stream on the phone numbers, and then map to the Contact Point Phone data map object.
Explanation:
The most efficient approach is B: Ingest the Contact object and use streaming transforms to normalize phone numbers into a separate Phone DLO, which stores each phone number type (work, home, mobile) in three rows. This data is then mapped to the Contact Point Phone object, ensuring all phone numbers are available for activation (e.g., SMS, calls). Streaming transforms allow real-time normalization (removing spaces, dashes, adding country codes) during ingestion without extra processing or storage.
A customer needs to integrate in real time with Salesforce CRM. Which feature accomplishes this requirement?
A. Streaming transforms
B. Data model triggers
C. Sales and Service bundle
D. Data actions and Lightning web components
A Data Cloud consultant is in the process of setting up data streams for a new service based data source. When ingesting Case data, which field is recommended to be associated with the Event Time Field?
A. Last Modified Data
B. Creation Date
C. Escalation Date
D. Resolution Date
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