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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Ingestion and Modeling | 20% | - Transformation and mapping - Ingestion from various sources - Data validation and inspection - Data modeling best practices |
| Topic 2: Identity Resolution | 14% | - Conflict resolution - Matching and reconciliation rules - Identity use cases - Unified profile creation |
| Topic 3: Segmentation and Insights | 18% | - Segment membership analysis - Insight implementation - Segment creation and configuration - Calculated vs streaming insights |
| Topic 4: Data Cloud Overview | 18% | - Business value and use cases - Architecture and ecosystem - Core concepts and terminology |
| Topic 5: Data Cloud Setup and Administration | 12% | - Data streams and data bundles - Monitoring and maintenance tools - Permissions and access control - Data spaces and configuration |
| Topic 6: Act on Data | 18% | - Process automation and triggering - Attribute usage and management - Activation concepts and use cases - Troubleshooting activations |
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NEW QUESTION # 145
Northern Trail Outfitters (NTO) wants to connect their B2C Commerce data with Data Cloud and bring two years of transactional history into Data Cloud.
What should NTO use to achieve this?
Answer: A
Explanation:
The B2C Commerce Starter Bundles are predefined data streams that ingest order and product data from B2C Commerce into Data Cloud. However, the starter bundles only bring in the last 90 days of data by default. To bring in two years of transactional history, NTO needs to use a custom extract from B2C Commerce that includes the historical data and configure the data stream to use the custom extract as the source. The other options are not sufficient to achieve this because:
A). B2C Commerce Starter Bundles only ingest the last 90 days of data by default.
B). Direct Sales Order entity ingestion is not a supported method for connecting B2C Commerce data with Data Cloud. Data Cloud does not provide a direct-access connection for B2C Commerce data, only data ingestion.
C). Direct Sales Product entity ingestion is not a supported method for connecting B2C Commerce data with Data Cloud. Data Cloud does not provide a direct-access connection for B2C Commerce data, only data ingestion. References: Create a B2C Commerce Data Bundle - Salesforce, B2C Commerce Connector - Salesforce, Salesforce B2C Commerce Pricing Plans & Costs
NEW QUESTION # 146
What are the two distinct phases of data model management in Data Cloud?
Answer: A,B
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 mappingthe 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 # 147
Which three out-of-the-box connectors are available for Data Cloud?
Answer: A,B,C
Explanation:
Explanation
These three out-of-the-box connectors are available for Data Cloud. They allow you to ingest data from Marketing Cloud, B2C Commerce, or Amazon S3 into Data Cloud and map it to the Data Cloud data model.
References:https://help.salesforce.com/s/articleView?id=sf.c360_a_connectors.htm&type=5
NEW QUESTION # 148
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: A
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 # 149
A company is seeking advice from a consultant on how to address the challenge of having multiple leads and contacts in Salesforce that share the same email address. The consultant wants to provide a detailed and comprehensive explanation on how Data Cloud can be leveraged to effectively solve this issue.
What should the consultant highlight to address this company's business challenge?
Answer: C
Explanation:
Issue Overview: When multiple leads and contacts share the same email address in Salesforce, it can lead to data duplication, inaccurate customer views, and inefficient marketing and sales efforts.
Data Cloud Identity Resolution: Salesforce Data Cloud offers Identity Resolution as a powerful tool to address this issue. It helps in merging and unifying data from multiple sources to create a single, comprehensive customer profile.
Process:
Data Ingestion: Import lead and contact data into Salesforce Data Cloud.
Identity Resolution Rules: Configure Identity Resolution rules to match and merge records based on key identifiers like email addresses.
Unification: The tool consolidates records that share the same email address, eliminating duplicates and ensuring a single view of each customer.
Continuous Updates: As new data comes in, Identity Resolution continuously updates and maintains the unified profiles.
Benefits:
Accurate Customer View: Reduces duplicate records and provides a complete view of each customer's interactions and history.
Improved Efficiency: Streamlines marketing and sales efforts by targeting a unified customer profile.
References:
Salesforce Data Cloud Identity Resolution
Salesforce Help: Identity Resolution Overview
NEW QUESTION # 150
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