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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Identity Resolution | 14% | - Identity use cases - Matching and reconciliation rules - Conflict resolution - Unified profile creation |
| Topic 2: Data Cloud Setup and Administration | 12% | - Data spaces and configuration - Data streams and data bundles - Permissions and access control - Monitoring and maintenance tools |
| Topic 3: Segmentation and Insights | 18% | - Insight implementation - Segment membership analysis - Calculated vs streaming insights - Segment creation and configuration |
| Topic 4: Act on Data | 18% | - Process automation and triggering - Attribute usage and management - Troubleshooting activations - Activation concepts and use cases |
| Topic 5: Data Cloud Overview | 18% | - Architecture and ecosystem - Core concepts and terminology - Business value and use cases |
| Topic 6: Data Ingestion and Modeling | 20% | - Ingestion from various sources - Data validation and inspection - Data modeling best practices - Transformation and mapping |
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NEW QUESTION # 64
Which permission setting should an administrator check if the custom CRM object is not available in New Data Stream configuration?
Answer: C
Explanation:
This permission setting should be checked if the custom CRM object is not available in New Data Stream configuration. The user who connects the Salesforce CRM connector must have View All permission for the custom object in the source CRM org. Reference: https://help.salesforce.com/s/articleView?id=sf.c360_a_data_cloud_salesforce_crm.htm&type=5
NEW QUESTION # 65
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: C
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 # 66
Which configuration can support separate Amazon S3 buckets for data ingestion and activation?
Answer: B
NEW QUESTION # 67
Which three features can be used to validate the data in the unified profile object?
Answer: A,C,D
Explanation:
These three features can be used to validate the data in the unified profile object. Data Explorer allows you to view the ingested data from different sources and how it is mapped to the unified profile object. Query API allows you to query the unified profile object using SOQL or SQL queries. Profile Explorer allows you to view the unified profile records and their attributes. References:
https://help.salesforce.com/s/articleView?id=sf.c360_a_data_explorer.htm&type=5
https://help.salesforce.com/s/articleView?id=sf.c360_a_query_api.htm&type=5
https://help.salesforce.com/s/articleView?id=sf.c360_a_profile_explorer.htm&type=5
NEW QUESTION # 68
How many Metrics can be present in 1 segment container?
Answer: C
Explanation:
Only one metric can be present in one segment container. A segment container is a logical grouping of segments that share the same metricand granularity. A metric is a calculated insight that defines and calculates a multidimensional measure on your data. A granularity is the level of detail at which you want to analyze your data, such as daily, weekly, or monthly. You can create multiple segment containers with different metrics and granularities, but each segment container can only have one metric. References: Segment Containers
NEW QUESTION # 69
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