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| Section | Weight | Objectives |
|---|---|---|
| Data Cloud Overview and Licensing | 7% | - Describe Data Cloud setup and configuration - Describe the business value of Data Cloud - Explain licensing model and capacity |
| Identity Resolution and Data Unification | 17% | - Understand unified records creation - Configure contact and account matching - Manage data overlap and deduplication - Configure identity resolution rules - Configure match rules and strategies |
| Data Sharing and Security | 8% | - Understand data governance requirements - Implement row-level security - Manage user access and permissions - Configure data sharing settings |
| Data Cloud Monitoring and Troubleshooting | 22% | - Use Data Cloud analytics and reports - Monitor data ingestion and sync status - Analyze identity resolution results - Troubleshoot data quality issues - Debug common configuration errors - Manage data retention and cleanup |
| Calculated Insights | 8% | - Create and configure calculated insights - Use formulas and functions in calculations - Define metrics and measurements - Publish insights to Data Cloud |
| Data Model Setup and Configuration | 15% | - Set up and configure Data Lake objects - Describe Harmonized Data Model concepts - Understand data types and field mapping - Map source data to harmonized data model - Configure custom objects, fields, and relationships |
| Data Actions | 8% | - Configure data sync to external systems - Set up segmentation and target audiences - Configure data actions for activation - Create and manage data bundles |
| Data Ingestion | 15% | - Configure batch data ingestion - Configure data sync and refresh schedules - Configure streaming data ingestion - Set up and manage data connectors - Troubleshoot ingestion issues |
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問題 #35
A consultant is troubleshooting a segment error.
Which error message is solved by using calculated insights Instead of nested segments?
答案：C
問題 #36
Which statement is true related to batch ingestions from Salesforce CRM?
答案：A
解題說明：
The design point is to preserve source fidelity while shaping data only where Data 360 processing needs it.
Here, When a column is added or removed, the CRM Connector performs a full refresh. fits because it changes the shape, keying, or refresh behavior at the Data 360 layer instead of forcing the source system to carry an analytics-specific design. In production, this keeps the upstream application simpler and gives the data team a repeatable way to prepare records for mapping, identity resolution, insights, or segmentation. The distractors fall short because they either move the problem into the wrong system, add needless duplication, ignore Data 360 object relationships, or rely on a feature built for a different lifecycle stage. In a real implementation, those choices usually create brittle pipelines, stale data, security exposure, or segments that look correct on paper but fail when activated. Thinking like an architect, the selected option places the logic where Data 360 can govern it and reuse it reliably.
問題 #37
Cumulus Financial uses Service Cloud as its CRM and stores mobile phone, home phone, and work phone as three separate fields for its customers on the Contact record. The company plans to use Data Cloud and ingest the Contact object via the CRM Connector.
What is the most efficient approach that a consultant should take when ingesting this data to ensure all the different phone numbers are properly mapped and available for use in activation?
答案：D
解題說明：
The most efficient approach that a consultant should take when ingesting this data to ensure all the different phone numbers are properly mapped and available for use in activation is B. Ingest the Contact object and use streaming transforms to normalize the phone numbers from the Contact data stream into a separate Phone data lake object (DLO) that contains three rows, and then map this new DLO to the Contact Point Phone data map object. This approach allows the consultant to use the streaming transforms feature of Data Cloud, which enables data manipulation and transformation at the time of ingestion, without requiring any additional processing or storage. Streaming transforms can be used to normalize the phone numbers from the Contact data stream, such as removing spaces, dashes, or parentheses, and adding country codes if needed. The normalized phone numbers can then be stored in a separate Phone DLO, which can have one row for each phone number type (work, home, mobile). The Phone DLO can then be mapped to the Contact Point Phone data map object, which is a standard object that represents a phone number associated with a contact point.
This way, the consultant can ensure that all the phone numbers are available for activation, such as sending SMS messages or making calls to the customers.
The other options are not as efficient as option B. Option A is incorrect because it does not normalize the phone numbers, which may cause issues with activation or identity resolution. Option C is incorrect because it requires creating a calculated insight, which is an additional step that consumes more resources and time than streaming transforms. Option D is incorrect because it requires creating formula fields in the Contact data stream, which may not be supported by the CRM Connector or may cause conflicts with the existing fields in the Contact object. References: Salesforce Data Cloud Consultant Exam Guide, Data Ingestion and Modeling, Streaming Transforms, Contact Point Phone
問題 #38
A customer service manager wants to implement generative AI in Salesforce to help agents answer customer inquiries. They are concerned that the AI might give generic or inaccurate responses if it doesn ' t have access to the company ' s internal knowledge and data. Which Salesforce Data 360 feature should help ensure the AI provides accurate, contextually relevant answers by using internal data?
答案：A
解題說明：
The AI pattern works when the model is grounded in appropriate Data 360 data and its outputs can be operationalized safely. Retrieval Augmented Generation (RAG) to ground AI prompts with relevant internal data fits because predictions or generative experiences are only useful when the data is representative, governed, and connected to Salesforce execution patterns such as scoring jobs, Flow, or grounded retrieval.
The distractors fall short because they either move the problem into the wrong system, add needless duplication, ignore Data 360 object relationships, or rely on a feature built for a different lifecycle stage. In a real implementation, those choices usually create brittle pipelines, stale data, security exposure, or segments that look correct on paper but fail when activated. Thinking like an architect, the selected option places the logic where Data 360 can govern it and reuse it reliably. This is the nuance exam questions often test: the platform capability must match both the technical layer and the business timing requirement, not just sound related to data.
問題 #39
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 standardIndividual data model object (DMO).
What should the consultant recommend to map this data to be used for both segmentation and identity resolution?
答案：A
解題說明：
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
問題 #40
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