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Salesforce Data-Cloud-Consultant Exam Syllabus Topics:

Topic	Details
Topic 1	Data Cloud Overview: This topic covers Data Cloud's function, key terminology, business value, typical use cases, the Data Cloud lifecycle, dependencies, and principles of data ethics. These sub-topics provide an overview of Data Cloud's capabilities and applications.
Topic 2	Data Cloud Setup and Administration: This topic includes applying Data Cloud permissions, permission sets, org-wide settings. It describes and configures data stream types, and data bundles. Moreover, it discusses use cases for data spaces, creating data spaces, managing and administering Data Cloud using reports, dashboards, flows, packaging, data kits, diagnosing and exploring data using Data Explorer, Profile Explorer, and APIs.
Topic 3	Data Ingestion and Modeling: This topic covers the different transformation capabilities within Data Cloud. It includes describing processes and considerations for data ingestion from various sources, defining, mapping, and modeling data using best practices aligned with identity resolution. Lastly, it discusses using available tools to inspect and validate ingested and modeled data.
Topic 4	Act on Data: This topic defines activations and their basic use cases, using attributes and related attributes, identifying and analyzing timing dependencies affecting the Data Cloud lifecycle. Additionally it focuses on troubleshooting common problems with activations, and using data actions, including their requirements and intended use cases.
Topic 5	Identity Resolution: It describes matching and how its rule sets are applied. Furthermore, it discusses reconciling data and its rule sets, the results of identity resolution, and use cases.

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Salesforce Certified Data Cloud Consultant Sample Questions (Q115-Q120):

NEW QUESTION # 115
How does Data Cloud handle an individual's Right to be Forgotten?

A. Deletes the specified Individual and records from any data model object/data lake object related to the Individual.
B. Deletes the specified Individual record and its Unified Individual Link record.
C. Deletes the records from all data source objects, and any downstream data model objects are updated at the next scheduled ingestion
D. Deletes the specified Individual and records from any data source object mapped to the Individual data model object.

Answer: A

Explanation:
Explanation
Data Cloud handles an individual's Right to be Forgotten by deleting the specified Individual and records from any data model object/data lake object related to the Individual. This means that Data Cloud removes all the data associated with the individual from the data space, including the data from the source objects, the unified individual profile, and any related objects. Data Cloud also deletes the Unified Individual Link record that links the individual to the source records. Data Cloud uses the Consent API to process the Right to be Forgotten requests, which are reprocessed at 30, 60, and 90 days to ensure a full deletion.
The other options are not correct descriptions of how Data Cloud handles an individual's Right to be Forgotten. Data Cloud does not delete the records from all data source objects, as this would affect the data integrity and availability of the source systems. Data Cloud also does not delete only the specified Individual record and its Unified Individual Link record, as this would leave the source records and the related records intact. Data Cloud also does not delete only the specified Individual and records from any data source object mapped to the Individual data model object, as this would leave the related records intact.
References:
* Requesting Data Deletion or Right to Be Forgotten
* Data Deletion for Data Cloud
* Use the Consent API with Data Cloud
* Data and Identity in Data Cloud

NEW QUESTION # 116
How can a consultant modify attribute names to match a naming convention in Cloud File Storage targets?

A. Set preferred attribute names when configuring activation.
B. Use a formula field to update the field name in an activation.
C. Update attribute names in the data stream configuration.
D. Update field names in the data model object.

Answer: A

NEW QUESTION # 117
A consultant is troubleshooting a segment error.
Which error message is solved by using calculated insights Instead of nested segments?

A. Segment population count failed.
B. Multiple population counts are in progress.
C. Segment is too complex.
D. Segment can't be published.

Answer: C

Explanation:
Segment Errors in Data Cloud: Segments in Salesforce Data Cloud can encounter errors due to various reasons, including complexity and nested segments.
Calculated Insights vs. Nested Segments:
* Complex Segments: If a segment is too complex due to extensive nesting or numerous conditions, it can lead to errors.
* Simplification with Calculated Insights: Using calculated insights can simplify segment creation by pre-computing and storing complex logic or aggregations, which can then be referenced directly in the segment.
Solution:
* Step 1: Identify the segment causing the "Segment is too complex" error.
* Step 2: Break down complex logic into calculated insights.
* Step 3: Use these calculated insights in segment definitions to reduce complexity.
References:
* Salesforce Data Cloud Calculated Insights
* Salesforce Data Cloud Segment Creation

NEW QUESTION # 118
A consultant is setting up Data Cloud for a multi-brand organization and is using data spaces to segregate its data for various brands.
While starting the mapping of a data stream, the consultant notices that they cannot map the object for one of the brands.
What should the consultant do to make the object available for a new data space?

A. Create a batch transform to split data between different data spaces.
B. Navigate to the Data Space tab and select the object to be included in the new data space.
C. Create a new data stream and map the second data stream to the data space.
D. Copy data from the default data space to a new DMO using the Data Copy feature and link this DMO to the new data space.

Answer: B

Explanation:
When setting up Data Cloud for a multi-brand organization, if a consultant cannot map an object for one of the brands during data stream setup, they should navigate to the Data Space tab and select the object to include it in the new data space. Here's why:
Understanding the Issue
The consultant is using data spaces to segregate data for different brands.
While mapping a data stream, they notice that an object is unavailable for one of the brands.
This indicates that the object has not been associated with the new data space.
Why Navigate to the Data Space Tab?
Data Spaces and Object Availability :
Objects must be explicitly added to a data space before they can be used in mappings or transformations within that space.
If an object is missing, it means it has not been included in the data space configuration.
Solution Approach :
By navigating to the Data Space tab , the consultant can add the required object to the new data space.
This ensures the object becomes available for mapping and use in the data stream.
Steps to Resolve the Issue
Step 1: Navigate to the Data Space Tab
Go to Data Cloud > Data Spaces and locate the new data space for the brand.
Step 2: Add the Missing Object
Select the data space and click on Edit .
Add the required object (e.g., a Data Model Object or Data Lake Object) to the data space.
Step 3: Save and Verify
Save the changes and return to the data stream setup.
Verify that the object is now available for mapping.
Step 4: Complete the Mapping
Proceed with mapping the object in the data stream.
Why Not Other Options?
A . Create a new data stream and map the second data stream to the data space :
Creating a new data stream is unnecessary if the issue is simply object availability in the data space.
B . Copy data from the default data space to a new DMO using the Data Copy feature and link this DMO to the new data space :
This is overly complex and not required if the object can simply be added to the data space.
C . Create a batch transform to split data between different data spaces :
Batch transforms are used for data processing, not for resolving object availability issues.
Conclusion
The correct solution is to navigate to the Data Space tab and select the object to include it in the new data space . This ensures the object is available for mapping and resolves the issue efficiently.

NEW QUESTION # 119
What is a typical use case for Salesforce Data Cloud?

A. Sending personalized emails at scale
B. Storing CRM data on promises
C. Data synchronization across the Salesforce ecosystem
D. Data harmonization across multiple platforms

Answer: D

Explanation:
A typical use case for Salesforce Data Cloud is data harmonization across multiple platforms . Here's why:
Understanding Salesforce Data Cloud
Salesforce Data Cloud is designed to aggregate, unify, and analyze customer data from multiple sources, including CRM, Marketing Cloud, external systems, and third-party platforms.
Its primary purpose is to provide a unified view of customer data for personalized experiences and actionable insights.
Why Data Harmonization Across Multiple Platforms?
Data Harmonization :
Data Cloud harmonizes data by standardizing and cleansing it from disparate sources.
This ensures consistency and accuracy across platforms, enabling organizations to create a single source of truth for customer data.
Use Case Alignment :
Data harmonization is a core functionality of Data Cloud, making it the most relevant use case among the options provided.
Other Options Are Less Relevant :
A . Data synchronization across the Salesforce ecosystem : While Data Cloud integrates with Salesforce products, its primary focus is on unifying data from multiple platforms, not just Salesforce.
B . Storing CRM data on premises : Data Cloud is a cloud-based solution and does not support on-premises storage.
D . Sending personalized emails at scale : This is a use case for Marketing Cloud, not Data Cloud.
Steps to Achieve Data Harmonization
Step 1: Ingest Data
Bring in customer data from multiple sources (e.g., CRM, Marketing Cloud, external systems) into Data Cloud.
Step 2: Standardize and Cleanse Data
Use batch or streaming transformations to standardize formats, remove duplicates, and cleanse data.
Step 3: Create Unified Profiles
Use identity resolution to merge related records into a single unified profile.
Step 4: Activate Insights
Leverage the harmonized data for segmentation, personalization, and analytics.
Conclusion
The most typical use case for Salesforce Data Cloud is data harmonization across multiple platforms , enabling organizations to unify and leverage customer data effectively.

NEW QUESTION # 120
......

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Newest Data-Cloud-Consultant Test Dumps.zip & Leading Offer in Qualification Exams & Unparalleled Data-Cloud-Consultant: Salesforce Certified Data Cloud Consultant

Salesforce Data-Cloud-Consultant Exam Syllabus Topics:

Data-Cloud-Consultant Test Dumps.zip - Free PDF Data-Cloud-Consultant - Salesforce Certified Data Cloud Consultant First-grade Dumps Discount

Salesforce Certified Data Cloud Consultant Sample Questions (Q115-Q120):

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