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| Section | Objectives |
|---|---|
| Topic 1: Data Cloud Overview | - Explain the purpose and functionality of Data Cloud - Describe the Data Cloud ecosystem and how it fits into the Salesforce platform |
| Topic 2: Segmentation and Calculated Insights | - Create and manage segments - Explain calculated insights |
| Topic 3: Data Ingestion and Modeling | - Explain data modeling concepts including Data Model Objects (DMOs) - Describe data streams and data sources |
| Topic 4: Data Cloud Reporting and Analytics | - Utilize Data Cloud reports and dashboards |
| Topic 5: Identity Resolution | - Describe matching and reconciliation rules - Explain the purpose and configuration of Identity Resolution |
| Topic 6: Data Cloud Setup and Administration | - Configure Data Cloud settings - Manage Data Cloud permissions and security |
| Topic 7: Activation | - Configure activations to various targets - Describe activation use cases |
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NEW QUESTION # 55
What is the first step to set up and configure a Data Cloud instance after it has been provisioned?
Answer: A
Explanation:
This is the first step to set up and configure a Data Cloud instance after it has been provisioned. The customer needs to connect to the Salesforce CRM org that Data Cloud is provisioned in, using the Salesforce CRM connector. This will allow Data Cloud to access the CRM data and map it to the Customer 360 data model. Reference: [Salesforce CRM Connector], [Data Cloud Setup]
NEW QUESTION # 56
A bank collects customer data for its loan applicants and high net worth customers. A customer can be both a load applicant and a high net worth customer, resulting in duplicate data.
How should a consultant ingest and map this data in Data Cloud?
Answer: B
Explanation:
To handle duplicate data for customers who are both loan applicants and high net worth individuals, the consultant should ingest the data into two separate Data Lake Objects (DLOs) and map them to the Individual and Contact Point Email Data Model Objects (DMOs). Here's why and how this works:
Understanding the Problem :
Customers may exist in both datasets (loan applicants and high net worth individuals), leading to potential duplication.
To avoid redundancy while maintaining data integrity, the data must be ingested and mapped carefully.
Why Two DLOs?
By ingesting the data into two DLOs, you can maintain separation between the two datasets while still leveraging shared attributes (e.g., email addresses).
Mapping both DLOs to the Individual and Contact Point Email DMOs ensures that identity resolution can consolidate duplicate records based on shared identifiers like email.
Steps to Implement This Solution :
Step 1: Create two DLOs-one for loan applicants and another for high net worth customers.
Step 2: Map both DLOs to the Individual DMO to consolidate customer profiles.
Step 3: Map the email fields from both DLOs to the Contact Point Email DMO to enable identity resolution based on email addresses.
Step 4: Configure identity resolution rules to merge duplicate records based on shared attributes like email.
Why Not Other Options?
A). Use a data transform to consolidate the data into one DLO: Consolidating into a single DLO before mapping would lose the distinction between the two datasets and make it harder to manage updates or changes.
C). Ingest the data into two DLOs and then map to two custom DMOs: Creating custom DMOs is unnecessary complexity when the standard Individual and Contact Point Email DMOs can handle this scenario.
D). Ingest the data into one DLO and then map to one custom DMO: Using a single DLO would result in data loss or confusion, as the distinction between loan applicants and high net worth customers would be lost.
By using two DLOs and mapping them to the standard DMOs, the consultant ensures clean data ingestion and effective identity resolution.
NEW QUESTION # 57
What are the two distinct phases of data model management in Data Cloud?
Answer: A,C
Explanation:
These two phases are distinct phases of data model management in Data Cloud. Data ingestion is the process of bringing data from different sources into Data Cloud using connectors or APIs. Data modeling is the process of mapping the ingested data to the Data Cloud canonical model or creating custom data model objects. References:
https://help.salesforce.com/s/articleView?id=sf.c360_a_data_cloud_data_model.htm&type=5
NEW QUESTION # 58
Which three actions can be applied to a previously created segment?
Answer: C,D,E
Explanation:
Explanation
These three actions can be applied to a previously created segment. You can export a segment to a CSV file, delete a segment from Data Cloud, or copy a segment to create a duplicate segment with the same criteria.
References:https://help.salesforce.com/s/articleView?id=sf.c360_a_segmentation_actions.htm&type=5
NEW QUESTION # 59
Which data stream category should be assigned to use the data for time-based operations in segmentation and calculated insights?
Answer: C
Explanation:
Data streams are the sources of data that are ingested into Data Cloud and mapped to the data model. Data streams have different categories that determine how the data is processed and used in Data Cloud.
Transaction data streams are used for time-based operations in segmentation and calculated insights, such as filtering by date range, aggregating by time period, or calculating time-to-event metrics. Transaction data streams are typically used for event data, such as purchases, clicks, or visits, that have a timestamp and a value associated with them. References: Data Streams, Data Stream Categories
NEW QUESTION # 60
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