Salesforce Data Cloud Consultant Practice Exams
Last updated on Oct 28,2026- Exam Code: Data Cloud Consultant
- Exam Name: Salesforce Certified Data Cloud Consultant
- Certification Provider: Salesforce
- Latest update: Oct 28,2026
Question #1
A marketing manager at Northern Trail Outfitters wants to Improve marketing return on investment (ROI) by tapping into Insights from Data Cloud Segment Intelligence.
Which permission set does a user need to set this up?
- A . Data Cloud Data Aware Specialist
- B . Data Cloud User
- C . Cloud Marketing Manager
- D . Data Cloud Admin
Correct Answer: D
D
Explanation:
To configure and use Segment Intelligence in Salesforce Data Cloud for improving marketing ROI, the user requires administrative privileges. Here’s the detailed analysis: Data Cloud Admin (Option D):
Permission Set Scope:
The Data Cloud Admin permission set grants full access to configure advanced Data Cloud features, including Segment Intelligence, which provides AI-driven insights (e.g., audience trends, engagement metrics).
Admins can define metrics, enable predictive models, and analyze segment performance, all critical for optimizing marketing ROI.
Official Documentation:
Salesforce’s Data Cloud Permission Sets Guide explicitly states that Segment
Intelligence configuration and management require administrative privileges. Only the Data Cloud Admin role can modify data model settings, access AI/ML tools, and apply segment recommendations (Source: "Admin vs. Standard User Permissions").
Why "Cloud Marketing Manager (C)" Is Incorrect:
No Standard Permission Set:
"Cloud Marketing Manager" is not a standard Salesforce Data Cloud permission set. This option may conflate Marketing Cloud roles (e.g., Marketing Manager) with Data Cloud’s permission structure. Marketing Cloud vs. Data Cloud:
While Marketing Cloud has roles like "Marketing Manager," Data Cloud uses distinct permission sets
(Admin, User, Data Aware Specialist). Segment Intelligence is a Data Cloud feature and requires Data
Cloud-specific permissions.
Other Options:
Data Cloud Data Aware Specialist (A): Provides read-only access to data governance tools but lacks permissions to configure Segment Intelligence.
Data Cloud User (B): Allows basic segment activation and viewing but cannot set up AI-driven insights.
Steps to Validate:
Step 1: Assign the Data Cloud Admin permission set via Setup > Users > Permission Sets.
Step 2: Navigate to Data Cloud > Segment Intelligence to configure analytics, review AI recommendations, and optimize segments.
Step 3: Use insights to refine targeting and measure ROI improvements.
Conclusion: The Data Cloud Admin permission set is required to configure and leverage Segment Intelligence, as it provides the necessary administrative rights to Data Cloud’s advanced analytics and AI tools. "Cloud Marketing Manager" is not a valid permission set in Data Cloud.
D
Explanation:
To configure and use Segment Intelligence in Salesforce Data Cloud for improving marketing ROI, the user requires administrative privileges. Here’s the detailed analysis: Data Cloud Admin (Option D):
Permission Set Scope:
The Data Cloud Admin permission set grants full access to configure advanced Data Cloud features, including Segment Intelligence, which provides AI-driven insights (e.g., audience trends, engagement metrics).
Admins can define metrics, enable predictive models, and analyze segment performance, all critical for optimizing marketing ROI.
Official Documentation:
Salesforce’s Data Cloud Permission Sets Guide explicitly states that Segment
Intelligence configuration and management require administrative privileges. Only the Data Cloud Admin role can modify data model settings, access AI/ML tools, and apply segment recommendations (Source: "Admin vs. Standard User Permissions").
Why "Cloud Marketing Manager (C)" Is Incorrect:
No Standard Permission Set:
"Cloud Marketing Manager" is not a standard Salesforce Data Cloud permission set. This option may conflate Marketing Cloud roles (e.g., Marketing Manager) with Data Cloud’s permission structure. Marketing Cloud vs. Data Cloud:
While Marketing Cloud has roles like "Marketing Manager," Data Cloud uses distinct permission sets
(Admin, User, Data Aware Specialist). Segment Intelligence is a Data Cloud feature and requires Data
Cloud-specific permissions.
Other Options:
Data Cloud Data Aware Specialist (A): Provides read-only access to data governance tools but lacks permissions to configure Segment Intelligence.
Data Cloud User (B): Allows basic segment activation and viewing but cannot set up AI-driven insights.
Steps to Validate:
Step 1: Assign the Data Cloud Admin permission set via Setup > Users > Permission Sets.
Step 2: Navigate to Data Cloud > Segment Intelligence to configure analytics, review AI recommendations, and optimize segments.
Step 3: Use insights to refine targeting and measure ROI improvements.
Conclusion: The Data Cloud Admin permission set is required to configure and leverage Segment Intelligence, as it provides the necessary administrative rights to Data Cloud’s advanced analytics and AI tools. "Cloud Marketing Manager" is not a valid permission set in Data Cloud.
Question #2
Which data stream category should be assigned to use the data for time-based operations in segmentation and calculated insights?
- A . Individual
- B . Transaction
- C . Sales Order
- D . Engagement
Correct Answer: B
B
Explanation:
Data streams are the sources of data that are ingested into Data Cloud and mapped to the data model. Data streams have different categories that determine how the data is processed and used in Data Cloud. Transaction data streams are used for time-based operations in segmentation and calculated insights, such as filtering by date range, aggregating by time period, or calculating time-to-event metrics. Transaction data streams are typically used for event data, such as purchases, clicks, or visits, that have a timestamp and a value associated with them.
Reference: Data Streams, Data Stream Categories
B
Explanation:
Data streams are the sources of data that are ingested into Data Cloud and mapped to the data model. Data streams have different categories that determine how the data is processed and used in Data Cloud. Transaction data streams are used for time-based operations in segmentation and calculated insights, such as filtering by date range, aggregating by time period, or calculating time-to-event metrics. Transaction data streams are typically used for event data, such as purchases, clicks, or visits, that have a timestamp and a value associated with them.
Reference: Data Streams, Data Stream Categories
Question #3
What is the primary purpose of Data Cloud?
- A . Providing a golden record of a customer
- B . Managing sales cycles and opportunities
- C . Analyzing marketing data results
- D . Integrating and unifying customer data
Correct Answer: D
D
Explanation:
Primary Purpose of Data Cloud:
Salesforce Data Cloud’s main function is to integrate and unify customer data from various sources, creating a single, comprehensive view of each customer.
Reference: Salesforce Data Cloud Overview
Benefits of Data Integration and Unification:
Golden Record: Providing a unified, accurate view of the customer.
Enhanced Analysis: Enabling better insights and analytics through comprehensive data. Improved Customer Engagement: Facilitating personalized and consistent customer experiences across channels.
Reference: Salesforce Data Cloud Benefits Documentation Steps for Data Integration:
Ingest data from multiple sources (CRM, marketing, service platforms).
Use data harmonization and reconciliation processes to unify data into a single profile.
Reference: Salesforce Data Integration and Unification Guide
Practical Application:
Example: A retail company integrates customer data from online purchases, in-store transactions, and customer service interactions to create a unified customer profile.
This unified data enables personalized marketing campaigns and improved customer service.
Reference: Salesforce Unified Customer Profile Case Studies
D
Explanation:
Primary Purpose of Data Cloud:
Salesforce Data Cloud’s main function is to integrate and unify customer data from various sources, creating a single, comprehensive view of each customer.
Reference: Salesforce Data Cloud Overview
Benefits of Data Integration and Unification:
Golden Record: Providing a unified, accurate view of the customer.
Enhanced Analysis: Enabling better insights and analytics through comprehensive data. Improved Customer Engagement: Facilitating personalized and consistent customer experiences across channels.
Reference: Salesforce Data Cloud Benefits Documentation Steps for Data Integration:
Ingest data from multiple sources (CRM, marketing, service platforms).
Use data harmonization and reconciliation processes to unify data into a single profile.
Reference: Salesforce Data Integration and Unification Guide
Practical Application:
Example: A retail company integrates customer data from online purchases, in-store transactions, and customer service interactions to create a unified customer profile.
This unified data enables personalized marketing campaigns and improved customer service.
Reference: Salesforce Unified Customer Profile Case Studies
Question #4
How does Data Cloud handle an individual’s Right to be Forgotten?
- A . Deletes the records from all data source objects, and any downstream data model objects are updated at the next scheduled ingestion
- B . Deletes the specified Individual record and its Unified Individual Link record.
- C . Deletes the specified Individual and records from any data source object mapped to the Individual data model object.
- D . Deletes the specified Individual and records from any data model object/data lake object related to the Individual.
Correct Answer: D
D
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.
Reference: 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
D
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.
Reference: 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
Question #5
A company wants to include certain personalized fields in an email by including related attributes during the activation in Data Cloud. It notices that some values, such as purchased product names, do not have consistent casing in Marketing Cloud Engagement. For example, purchased product names appear as follows: Jacket, jacket, shoes, SHOES. The company wants to normalize all names to proper case and replace any null values with a default value.
How should a consultant fulfill this requirement within Data Cloud?
- A . Create a streaming insight with a data action.
- B . Use formula fields when ingesting at the data stream level.
- C . Create one batch data transform per data stream.
- D . Create one batch data transform that creates a new DLO.
Correct Answer: D
D
Explanation:
To normalize purchased product names (e.g., converting casing to proper case and replacing null values with a default value) within Salesforce Data Cloud, the best approach is to create a batch data transform that generates a new DLO. Here’s the detailed explanation: Understanding the Problem:
The company wants to ensure that product names in Marketing Cloud Engagement are consistent and properly formatted. The inconsistencies in casing (e.g., "Jacket," "jacket," "shoes," "SHOES") and the presence of null values need to be addressed before activation.
Why Batch Data Transform?
A batch data transform allows you to process large volumes of data in bulk, making it ideal for cleaning and normalizing datasets.
By creating a new DLO, you ensure that the original data remains intact while providing a clean, transformed dataset for downstream use cases like email personalization. Steps to Implement This Solution:
Step 1: Navigate to the Data Streams section in Salesforce Data Cloud and identify the data stream containing the purchased product names.
Step 2: Create a new batch data transform by selecting the relevant data stream as the source.
Step 3: Use transformation functions to normalize the product names:
Apply the PROPER () function to convert all product names to proper case.
Use the COALESCE () function to replace null values with a default value (e.g., "Unknown Product").
Step 4: Configure the batch data transform to output the results into a new DLO. This ensures that the transformed data is stored separately from the original dataset.
Step 5: Activate the new DLO for use in Marketing Cloud Engagement. Ensure that the email templates pull product names from the transformed DLO instead of the original dataset.
Why Not Other Options?
D
Explanation:
To normalize purchased product names (e.g., converting casing to proper case and replacing null values with a default value) within Salesforce Data Cloud, the best approach is to create a batch data transform that generates a new DLO. Here’s the detailed explanation: Understanding the Problem:
The company wants to ensure that product names in Marketing Cloud Engagement are consistent and properly formatted. The inconsistencies in casing (e.g., "Jacket," "jacket," "shoes," "SHOES") and the presence of null values need to be addressed before activation.
Why Batch Data Transform?
A batch data transform allows you to process large volumes of data in bulk, making it ideal for cleaning and normalizing datasets.
By creating a new DLO, you ensure that the original data remains intact while providing a clean, transformed dataset for downstream use cases like email personalization. Steps to Implement This Solution:
Step 1: Navigate to the Data Streams section in Salesforce Data Cloud and identify the data stream containing the purchased product names.
Step 2: Create a new batch data transform by selecting the relevant data stream as the source.
Step 3: Use transformation functions to normalize the product names:
Apply the PROPER () function to convert all product names to proper case.
Use the COALESCE () function to replace null values with a default value (e.g., "Unknown Product").
Step 4: Configure the batch data transform to output the results into a new DLO. This ensures that the transformed data is stored separately from the original dataset.
Step 5: Activate the new DLO for use in Marketing Cloud Engagement. Ensure that the email templates pull product names from the transformed DLO instead of the original dataset.
Why Not Other Options?
Question #6
A user has built a segment in Data Cloud and is in the process of creating an activation. When selecting related attributes, they cannot find a specific set of attributes they know to be related to the individual.
Which statement explains why these attributes are not available?
- A . The segment is not segmenting on profile data.
- B . The attributes are being used in another activation.
- C . The desired attributes reside on different related paths.
- D . Activations can only include 1-to-1 attributes.
Correct Answer: C
C
Explanation:
The correct answer is C, the desired attributes reside on different related paths. When creating an activation in Data Cloud, you can select related attributes from data model objects that are linked to the segment entity. However, not all related attributes are available for every activation. The availability of related attributes depends on the container path, which is the sequence of data model objects that connects the segment entity to the related entity. For example, if you segment on the Unified Individual entity, you can select related attributes from the Order Product entity, but only if the container path is Unified Individual > Order > Order Product. If the container path is Unified Individual > Order Line Item > Order Product, then the related attributes from Order Product are not available for activation. This is because Data Cloud only supports one-to-many relationships for related attributes, and Order Line Item is a many-to-many junction object between Order and Order Product. Therefore, you need to ensure that the desired attributes reside on the same related path as the segment entity, and that the path does not include any many-to-many junction objects. The other options are incorrect because they do not explain why the related attributes are not available. The segment entity can be any data model object, not just profile data. The attributes are not restricted by being used in another activation. Activations can include one-to-many attributes, not just one-to-one attributes.
Reference: Related Attributes in Activation
Considerations for Selecting Related Attributes
Salesforce Launches: Data Cloud Consultant Certification
Create a Segment in Data Cloud
C
Explanation:
The correct answer is C, the desired attributes reside on different related paths. When creating an activation in Data Cloud, you can select related attributes from data model objects that are linked to the segment entity. However, not all related attributes are available for every activation. The availability of related attributes depends on the container path, which is the sequence of data model objects that connects the segment entity to the related entity. For example, if you segment on the Unified Individual entity, you can select related attributes from the Order Product entity, but only if the container path is Unified Individual > Order > Order Product. If the container path is Unified Individual > Order Line Item > Order Product, then the related attributes from Order Product are not available for activation. This is because Data Cloud only supports one-to-many relationships for related attributes, and Order Line Item is a many-to-many junction object between Order and Order Product. Therefore, you need to ensure that the desired attributes reside on the same related path as the segment entity, and that the path does not include any many-to-many junction objects. The other options are incorrect because they do not explain why the related attributes are not available. The segment entity can be any data model object, not just profile data. The attributes are not restricted by being used in another activation. Activations can include one-to-many attributes, not just one-to-one attributes.
Reference: Related Attributes in Activation
Considerations for Selecting Related Attributes
Salesforce Launches: Data Cloud Consultant Certification
Create a Segment in Data Cloud
Question #6
A user has built a segment in Data Cloud and is in the process of creating an activation. When selecting related attributes, they cannot find a specific set of attributes they know to be related to the individual.
Which statement explains why these attributes are not available?
- A . The segment is not segmenting on profile data.
- B . The attributes are being used in another activation.
- C . The desired attributes reside on different related paths.
- D . Activations can only include 1-to-1 attributes.
Correct Answer: C
C
Explanation:
The correct answer is C, the desired attributes reside on different related paths. When creating an activation in Data Cloud, you can select related attributes from data model objects that are linked to the segment entity. However, not all related attributes are available for every activation. The availability of related attributes depends on the container path, which is the sequence of data model objects that connects the segment entity to the related entity. For example, if you segment on the Unified Individual entity, you can select related attributes from the Order Product entity, but only if the container path is Unified Individual > Order > Order Product. If the container path is Unified Individual > Order Line Item > Order Product, then the related attributes from Order Product are not available for activation. This is because Data Cloud only supports one-to-many relationships for related attributes, and Order Line Item is a many-to-many junction object between Order and Order Product. Therefore, you need to ensure that the desired attributes reside on the same related path as the segment entity, and that the path does not include any many-to-many junction objects. The other options are incorrect because they do not explain why the related attributes are not available. The segment entity can be any data model object, not just profile data. The attributes are not restricted by being used in another activation. Activations can include one-to-many attributes, not just one-to-one attributes.
Reference: Related Attributes in Activation
Considerations for Selecting Related Attributes
Salesforce Launches: Data Cloud Consultant Certification
Create a Segment in Data Cloud
C
Explanation:
The correct answer is C, the desired attributes reside on different related paths. When creating an activation in Data Cloud, you can select related attributes from data model objects that are linked to the segment entity. However, not all related attributes are available for every activation. The availability of related attributes depends on the container path, which is the sequence of data model objects that connects the segment entity to the related entity. For example, if you segment on the Unified Individual entity, you can select related attributes from the Order Product entity, but only if the container path is Unified Individual > Order > Order Product. If the container path is Unified Individual > Order Line Item > Order Product, then the related attributes from Order Product are not available for activation. This is because Data Cloud only supports one-to-many relationships for related attributes, and Order Line Item is a many-to-many junction object between Order and Order Product. Therefore, you need to ensure that the desired attributes reside on the same related path as the segment entity, and that the path does not include any many-to-many junction objects. The other options are incorrect because they do not explain why the related attributes are not available. The segment entity can be any data model object, not just profile data. The attributes are not restricted by being used in another activation. Activations can include one-to-many attributes, not just one-to-one attributes.
Reference: Related Attributes in Activation
Considerations for Selecting Related Attributes
Salesforce Launches: Data Cloud Consultant Certification
Create a Segment in Data Cloud
Question #8
A user needs permissions to access Data Cloud to create, manage, and activate segments, However, the user should not be allowed to created reports or manage data sources.
Which permission set should the consultant assign?
- A . Data Cloud Marketing Specialist
- B . Data Cloud Marketing Manager
- C . Data Cloud Data Aware Specialist
- D . Data Cloud user
Correct Answer: A
A
Explanation:
To grant a user permissions to create, manage, and activate segments without allowing them to create reports or manage data sources, the consultant should assign the Data Cloud Marketing Specialist permission set. Here’s why:
Understanding the Role Requirements:
The user needs access to segment creation, management, and activation.
The user should not have permissions to create reports or manage data sources, which are higher-level administrative tasks.
Why Data Cloud Marketing Specialist?
The Data Cloud Marketing Specialist permission set provides access to segment-related functionalities, including creating, managing, and activating segments.
It excludes permissions for creating reports or managing data sources, aligning perfectly with the stated requirements.
Steps to Assign the Permission Set:
Step 1: Navigate to Setup > Users > Permission Sets in Salesforce.
Step 2: Locate and assign the Data Cloud Marketing Specialist permission set to the user.
Step 3: Verify that the user has the required permissions by testing their access in Data Cloud.
Why Not Other Options?
B. Data Cloud Marketing Manager: This permission set includes broader permissions, such as managing campaigns and audiences, which are not required for this role.
C. Data Cloud Data Aware Specialist: This role focuses on data ingestion and transformation, not segment management.
D. Data Cloud User: This is a basic permission set that provides limited access and does not include segment management capabilities.
By assigning the Data Cloud Marketing Specialist permission set, the consultant ensures that the user has the necessary permissions without overextending their access.
A
Explanation:
To grant a user permissions to create, manage, and activate segments without allowing them to create reports or manage data sources, the consultant should assign the Data Cloud Marketing Specialist permission set. Here’s why:
Understanding the Role Requirements:
The user needs access to segment creation, management, and activation.
The user should not have permissions to create reports or manage data sources, which are higher-level administrative tasks.
Why Data Cloud Marketing Specialist?
The Data Cloud Marketing Specialist permission set provides access to segment-related functionalities, including creating, managing, and activating segments.
It excludes permissions for creating reports or managing data sources, aligning perfectly with the stated requirements.
Steps to Assign the Permission Set:
Step 1: Navigate to Setup > Users > Permission Sets in Salesforce.
Step 2: Locate and assign the Data Cloud Marketing Specialist permission set to the user.
Step 3: Verify that the user has the required permissions by testing their access in Data Cloud.
Why Not Other Options?
B. Data Cloud Marketing Manager: This permission set includes broader permissions, such as managing campaigns and audiences, which are not required for this role.
C. Data Cloud Data Aware Specialist: This role focuses on data ingestion and transformation, not segment management.
D. Data Cloud User: This is a basic permission set that provides limited access and does not include segment management capabilities.
By assigning the Data Cloud Marketing Specialist permission set, the consultant ensures that the user has the necessary permissions without overextending their access.
Question #9
A consultant wants to make sure address details from customer orders are selected as best to save to the unified profile.
What should the consultant do to achieve this?
- A . Select the address details on the Contact Point Address. Change the reconciliation rules for the specific address attributes to Source Priority and move the Individual DMO to the bottom.
- B . Use the default reconciliation rules for Contact Point Address.
- C . Select the address details on the Contact Point Address. Change the reconciliation rules for the specific address attributes to Source Priority and move the Oder DMO to the top.
- D . Change the default reconciliation rules for Individual to Source Priority.
Correct Answer: C
C
Explanation:
Unified Profile: Creating a unified customer profile in Salesforce Data Cloud involves consolidating data from various sources.
Reconciliation Rules: These rules determine which data source is considered the "best" when conflicting data is encountered. Changing reconciliation rules allows prioritizing specific sources. Source Priority: Setting source priority involves defining which data source should be preferred over others for specific attributes.
Process:
Step 1: Access the Data Cloud settings for reconciliation rules.
Step 2: Select the Contact Point Address details.
Step 3: Change the reconciliation rules for address attributes to "Source Priority."
Step 4: Move the Order DMO to the top of the priority list. This ensures that address details from customer orders are prioritized and selected as the best data to save to the unified profile. Benefits:
Accuracy: Ensures the most accurate and reliable address data is used in the unified profile.
Relevance: Gives priority to the most relevant and frequently updated source (customer orders).
Reference: Salesforce Data Cloud Reconciliation Rules
Salesforce Unified Customer Profile
C
Explanation:
Unified Profile: Creating a unified customer profile in Salesforce Data Cloud involves consolidating data from various sources.
Reconciliation Rules: These rules determine which data source is considered the "best" when conflicting data is encountered. Changing reconciliation rules allows prioritizing specific sources. Source Priority: Setting source priority involves defining which data source should be preferred over others for specific attributes.
Process:
Step 1: Access the Data Cloud settings for reconciliation rules.
Step 2: Select the Contact Point Address details.
Step 3: Change the reconciliation rules for address attributes to "Source Priority."
Step 4: Move the Order DMO to the top of the priority list. This ensures that address details from customer orders are prioritized and selected as the best data to save to the unified profile. Benefits:
Accuracy: Ensures the most accurate and reliable address data is used in the unified profile.
Relevance: Gives priority to the most relevant and frequently updated source (customer orders).
Reference: Salesforce Data Cloud Reconciliation Rules
Salesforce Unified Customer Profile
Question #10
Which two dependencies need to be removed prior to disconnecting a data source? Choose 2 answers
- A . Activation target
- B . Segment
- C . Activation
- D . Data stream
Correct Answer: B, D
B, D
Explanation:
Dependencies in Data Cloud:
Before disconnecting a data source, all dependencies must be removed to prevent data integrity issues.
Reference: Salesforce Data Source Management Documentation Identifying Dependencies:
Segment: Segments using data from the source must be deleted or reassigned.
Data Stream: The data stream must be disconnected, as it directly relies on the data source.
Reference: Salesforce Segment and Data Stream Management Guide Steps to Remove Dependencies:
Remove Segments:
Navigate to the Segmentation interface in Salesforce Data Cloud. Identify and delete segments relying on the data source. Disconnect Data Stream:
Go to the Data Stream settings.
Locate and disconnect the data stream associated with the source.
Reference: Salesforce Segment Deletion and Data Stream Disconnection Tutorial Practical Application:
Example: When preparing to disconnect a legacy CRM system, ensure all segments and data streams using its data are properly removed or migrated.
Reference: Salesforce Data Source Disconnection Best Practices
B, D
Explanation:
Dependencies in Data Cloud:
Before disconnecting a data source, all dependencies must be removed to prevent data integrity issues.
Reference: Salesforce Data Source Management Documentation Identifying Dependencies:
Segment: Segments using data from the source must be deleted or reassigned.
Data Stream: The data stream must be disconnected, as it directly relies on the data source.
Reference: Salesforce Segment and Data Stream Management Guide Steps to Remove Dependencies:
Remove Segments:
Navigate to the Segmentation interface in Salesforce Data Cloud. Identify and delete segments relying on the data source. Disconnect Data Stream:
Go to the Data Stream settings.
Locate and disconnect the data stream associated with the source.
Reference: Salesforce Segment Deletion and Data Stream Disconnection Tutorial Practical Application:
Example: When preparing to disconnect a legacy CRM system, ensure all segments and data streams using its data are properly removed or migrated.
Reference: Salesforce Data Source Disconnection Best Practices