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Free Data-Cloud-Consultant Exam Braindumps (page: 18)

Page 17 of 44
PDF with 170 Questions

Northern Trail Outfitters is using the Marketing Cloud Starter Data Bundles to bring Marketing Cloud data into Data Cloud.
What are two of the available datasets in Marketing Cloud Starter Data Bundles? Choose 2 answers

  1. Personalization
  2. MobileConnect
  3. Loyalty Management
  4. MobilePush

Answer(s): B,D

Explanation:

The Marketing Cloud Starter Data Bundles are predefined data bundles that allow you to easily ingest data from Marketing Cloud into Data Cloud. The available datasets in Marketing Cloud Starter Data Bundles are Email, MobileConnect, and MobilePush. These datasets contain engagement events and metrics from different Marketing Cloud channels, such as email, SMS, and push notifications. By using these datasets, you can enrich your Data Cloud data model with Marketing Cloud data and create segments and activations based on your marketing campaigns and journeys. The other options are incorrect because they are not available datasets in Marketing Cloud Starter Data Bundles. Option A is incorrect because Personalization is not a dataset, but a feature of Marketing Cloud that allows you to tailor your content and messages to your audience. Option C is incorrect because Loyalty Management is not a dataset, but a product of Marketing Cloud that allows you to create and manage loyalty programs for your customers.


Reference:

Marketing Cloud Starter Data Bundles in Data Cloud, Connect Your Data Sources, Personalization in Marketing Cloud, Loyalty Management in Marketing Cloud



A customer has a custom Customer Email c object related to the standard Contact object in Salesforce CRM. This custom object stores the email address a Contact that they want to use for activation.
To which data entity is mapped?

  1. Contact
  2. Contact Point_Email
  3. Custom customer Email__c object
  4. Individual

Answer(s): B

Explanation:

The Contact Point_Email object is the data entity that represents an email address associated with an individual in Data Cloud. It is part of the Customer 360 Data Model, which is a standardized data model that defines common entities and relationships for customer data. The Contact Point_Email object can be mapped to any custom or standard object that stores email addresses in Salesforce

CRM, such as the custom Customer Email__c object. The other options are not the correct data entities to map to because:
A . The Contact object is the data entity that represents a person who is associated with an account that is a customer, partner, or competitor in Salesforce CRM. It is not the data entity that represents an email address in Data Cloud.
C . The custom Customer Email__c object is not a data entity in Data Cloud, but a custom object in Salesforce CRM. It can be mapped to a data entity in Data Cloud, such as the Contact Point_Email object, but it is not a data entity itself.
D . The Individual object is the data entity that represents a unique person in Data Cloud. It is the core entity for managing consent and privacy preferences, and it can be related to one or more contact points, such as email addresses, phone numbers, or social media handles. It is not the data entity that represents an email address in Data Cloud.


Reference:

Customer 360 Data Model:
Individual and Contact Points - Salesforce, Contact Point_Email | Object Reference for the Salesforce Platform | Salesforce Developers, [Contact | Object Reference for the Salesforce Platform | Salesforce Developers], [Individual | Object Reference for the Salesforce Platform | Salesforce Developers]



During discovery, which feature should a consultant highlight for a customer who has multiple data sources and needs to match and reconcile data about individuals into a single unified profile?

  1. Harmonization
  2. Data Cleansing
  3. Data Consolidation
  4. Identity Resolution

Answer(s): D

Explanation:

The feature that the consultant should highlight for a customer who has multiple data sources and needs to match and reconcile data about individuals into a single unified profile is D. Identity Resolution. Identity Resolution is the process of identifying, matching, and reconciling data about individuals across different data sources and creating a unified profile that represents a single view of the customer. Identity Resolution uses various methods and rules to determine the best match and reconciliation of data, such as deterministic matching, probabilistic matching, reconciliation rules,

and identity graphs. Identity Resolution enables the customer to have a complete and accurate understanding of their customers and their interactions across different channels and touchpoints.


Reference:

Salesforce Data Cloud Consultant Exam Guide, Identity Resolution



Cumulus Financial uses Data Cloud to segment banking customers and activate them for direct mail via a Cloud File Storage activation. The company also wants to analyze individuals who have been in the segment within the last 2 years.
Which Data Cloud component allows for this?

  1. Nested segments
  2. Segment exclusion
  3. Calculated insights
  4. Segment membership data model object

Answer(s): D

Explanation:

The segment membership data model object is a Data Cloud component that allows for analyzing individuals who have been in a segment within a certain time period. The segment membership data model object is a table that stores the information about which individuals belong to which segments and when they were added or removed from the segments. This object can be used to create calculated insights, such as segment size, segment duration, segment overlap, or segment retention, that can help measure the effectiveness of segmentation and activation strategies. The segment membership data model object can also be used to create nested segments or segment exclusions based on the segment membership criteria, such as segment name, segment type, or segment date range. The other options are not correct because they are not Data Cloud components that allow for analyzing individuals who have been in a segment within the last 2 years. Nested segments and segment exclusions are features that allow for creating more complex segments based on existing segments, but they do not provide the historical data about segment membership. Calculated insights are custom metrics or measures that are derived from data model objects or data lake objects, but they do not store the segment membership information by themselves.


Reference:

Segment Membership Data Model Object, Create a Calculated Insight, Create a Nested Segment






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