The B2C Commerce Order Bundle is a data bundle that creates a data stream to flow order data from a B2C Commerce instance to Data Cloud. However, this data bundle does not ingest any historical data and only ingests new orders from the time the data stream is created.Therefore, if a consultant wants to ingest historical order data, they need to use a different method, such as exporting the data from B2C Commerce and importing it to Data Cloud using a CSV file12.Reference:

Create a B2C Commerce Data Bundle

Data Access and Export for B2C Commerce and Commerce Marketplace

When activating to Amazon S3, the information that is provided in a .csv file is the activated data payload.The activated data payload is the data that is sent from Data Cloud to the activation target, which in this case is an Amazon S3 bucket1.The activated data payload contains the attributes and values of the individuals or entities that are included in the segment that is being activated2.The activated data payload can be used for various purposes, such as marketing, sales, service, or analytics3. The other options are incorrect because they are not provided in a .csv file when activating to Amazon S3.Option A is incorrect because an audit log is not provided in a .csv file, but it can be viewed in the Data Cloud UI under the Activation History tab4.Option C is incorrect because the metadata regarding the segment definition is not provided in a .csv file, but it can be viewed in the Data Cloud UI under the Segmentation tab5. Option D is incorrect because the manifest of origin sources within Data Cloud is not provided in a .csv file, but it can be viewed in the Data Cloud UI under the Data Sources tab.Reference:Data Activation Overview,Create and Activate Segments in Data Cloud,Data Activation Use Cases,View Activation History,Segmentation Overview, [Data Sources Overview]

Data Streams and DLOs: In Salesforce Data Cloud, data streams are used to ingest data, which is then stored in Data Lake Objects (DLOs).

Deletion Considerations: Before deleting a data stream, it's crucial to consider the dependencies and usage of the underlying DLO.

Data Transform Usage:

Impact of Deletion: If the underlying DLO is used in a data transform, deleting the data stream will affect any transforms relying on that DLO.

Dependency Check: Ensure that the DLO is not part of any active data transformations or processes that could be disrupted by its deletion.

Reference:

Salesforce Data Cloud Documentation: Data Streams

Salesforce Data Cloud Documentation: Data Transforms

To support identity resolution in Data Cloud, the fields from the Master Customer table should be mapped to the standard data model objects that are designed for this purpose. The Individual object is used to store the name and other personally identifiable information (PII) of a customer, while the Contact Phone Email object is used to store the primary email address and other contact information of a customer. These objects are linked by a relationship field that indicates the contact information belongs to the individual. By mapping the fields to these objects, Data Cloud can use the identity resolution rules to match and reconcile the profiles from different sources based on the name and email address fields. The other options are not recommended because they either create a new custom object that is not part of the standard data model, or map all fields to the Customer object that is not intended for identity resolution, or map all fields to the Individual object that does not have a standard email address field.Reference:Data Modeling Requirements for Identity Resolution,Create Unified Individual Profiles

A typical use case for Salesforce Data Cloud is data harmonization across multiple platforms . Here's why:

Understanding Salesforce Data Cloud

Salesforce Data Cloud is designed to aggregate, unify, and analyze customer data from multiple sources, including CRM, Marketing Cloud, external systems, and third-party platforms.

Its primary purpose is to provide a unified view of customer data for personalized experiences and actionable insights.

Why Data Harmonization Across Multiple Platforms?

Data Harmonization :

Data Cloud harmonizes data by standardizing and cleansing it from disparate sources.

This ensures consistency and accuracy across platforms, enabling organizations to create a single source of truth for customer data.

Use Case Alignment :

Data harmonization is a core functionality of Data Cloud, making it the most relevant use case among the options provided.

Other Options Are Less Relevant :

A . Data synchronization across the Salesforce ecosystem : While Data Cloud integrates with Salesforce products, its primary focus is on unifying data from multiple platforms, not just Salesforce.

B . Storing CRM data on premises : Data Cloud is a cloud-based solution and does not support on-premises storage.

D . Sending personalized emails at scale : This is a use case for Marketing Cloud, not Data Cloud.

Steps to Achieve Data Harmonization

Step 1: Ingest Data

Bring in customer data from multiple sources (e.g., CRM, Marketing Cloud, external systems) into Data Cloud.

Step 2: Standardize and Cleanse Data

Use batch or streaming transformations to standardize formats, remove duplicates, and cleanse data.

Step 3: Create Unified Profiles

Use identity resolution to merge related records into a single unified profile.

Step 4: Activate Insights

Leverage the harmonized data for segmentation, personalization, and analytics.

Conclusion

The most typical use case for Salesforce Data Cloud is data harmonization across multiple platforms , enabling organizations to unify and leverage customer data effectively.