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| Section | Objectives |
|---|---|
| Identity Resolution | - Describe matching and reconciliation rules - Explain the purpose and configuration of Identity Resolution |
| Data Cloud Overview | - Explain the purpose and functionality of Data Cloud - Describe the Data Cloud ecosystem and how it fits into the Salesforce platform |
| Segmentation and Calculated Insights | - Create and manage segments - Explain calculated insights |
| Data Cloud Reporting and Analytics | - Utilize Data Cloud reports and dashboards |
| Activation | - Describe activation use cases - Configure activations to various targets |
| Data Ingestion and Modeling | - Explain data modeling concepts including Data Model Objects (DMOs) - Describe data streams and data sources |
| Data Cloud Setup and Administration | - Manage Data Cloud permissions and security - Configure Data Cloud settings |
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NEW QUESTION # 170
A consultant is helping a beauty company ingest its profile data into Data Cloud. The company's source data includes several fields, such as eye color, skin type, and hair color, that are not fields in the standard Individual data model object (DMO).
What should the consultant recommend to map this data to be used for both segmentation and identity resolution?
Answer: D
Explanation:
The best option to map the data to be used for both segmentation and identity resolution is to create custom fields on the standard Individual DMO. This way, the consultant can leverage the existing fields and functionality of the Individual DMO, such as identity resolution rulesets, calculated insights, and data actions, while adding the additional fields that are specific to the beauty company's data1. Creating a custom DMO from scratch or duplicating the standard Individual DMO would require more effort and maintenance, and might not be compatible with the existing features of Data Cloud. Creating a custom DMO with only the additional fields and mapping it to the standard Individual DMO would create unnecessary complexity and redundancy, and might not allow the use of the custom fields for identity resolution. References:
1: Data Model Objects in Data Cloud
NEW QUESTION # 171
Which consideration related to the way Data Cloud ingests CRM data is true?
Answer: B
Explanation:
The correct answer is D. The CRM Connector allows standard fields to stream into Data Cloud in real time.
This means that any changes to the standard fields in the CRM data source are reflected in Data Cloud almost instantly, without waiting for the next scheduled synchronization. This feature enables Data Cloud to have the most up-to-date and accurate CRM data for segmentation and activation1.
The other options are incorrect for the following reasons:
A). CRM data can be manually refreshed at any time by clicking the Refresh button on the data stream detail page2. This option is false.
B). The CRM Connector's synchronization times can be customized to up to 60-minute intervals, not 15- minute intervals3. This option is false.
C). Formula fields are not refreshed at regular sync intervals, but only at the next full refresh4. A full refresh is a complete data ingestion process that occurs once every 24 hours or when manually triggered. This option is false.
1: Connect and Ingest Data in Data Cloud article on Salesforce Help
2: Data Sources in Data Cloud unit on Trailhead
3: Data Cloud for Admins module on Trailhead
4: [Formula Fields in Data Cloud] unit on Trailhead
[Data Streams in Data Cloud] unit on Trailhead
NEW QUESTION # 172
Which three objects are created as a result of Identity Resolution?
Answer: A,C,E
Explanation:
Identity Resolution creates three types of objects8:
Unified Individual: This object represents the unified profile of an individual, which contains attributes from multiple sources.
Unified Contact Point: This object represents the unified contact information of an individual, such as email, phone, address, or app.
Unified Link: This object represents the link between an Individual object and a Unified Individual object, or between a Contact Point object and a Unified Contact Point object.
NEW QUESTION # 173
How many Metrics can be present in 1 segment container?
Answer: D
Explanation:
Only one metric can be present in one segment container. A segment container is a logical grouping of segments that share the same metric and 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. Reference: Segment Containers
NEW QUESTION # 174
Which two common use cases can be addressed with Data Cloud?
Answer: A,D
Explanation:
Data Cloud can address these two common use cases by providing a platform that allows customers to ingest, map, enrich, and activate data from various sources using the Customer 360 data model, and by enabling customers to create segments, insights, and activations based on their customer data. Reference: [Data Cloud Overview], [Customer 360 Data Model], [Data Activation]
NEW QUESTION # 175
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