Data-Cloud-Consultant Exam Dumps : Salesforce Certified Data Cloud Consultant (SP25)

When ingesting customer data into Data Cloud, it is critical to use the correct data types to ensure proper segmentation and usage. Here’s how the consultant should handle the provided data points:

Propensity to Purchase :

This represents a likelihood or probability value, typically expressed as a percentage (e.g., 75%).

The appropriate data type for this field is Percent , which allows for easy interpretation and use in segmentation.

Has Active Membership :

This is a binary value indicating whether a customer has an active membership (e.g., "Yes" or "No").

The correct data type for this field is Boolean , which supports true/false values.

Work Email Address :

This is a standard email address field.

The appropriate data type is Email , which ensures proper validation and formatting.

Why Not Other Options?

A. Number, Text, URL: These data types are incorrect because "Propensity to Purchase" should be a percentage, not a generic number. Similarly, "Work Email Address" should be an email type, not a URL.

C. Number, Boolean, Text: While "Number" could work for propensity scores, it lacks the semantic meaning of a percentage. Additionally, "Text" is not suitable for email addresses.

D. Percent, Number, Email: Using "Number" for "Has Active Membership" is incorrect because it is a binary value, not a numeric one.

By selecting Percent, Boolean, Email , the consultant ensures that the data is correctly formatted and ready for segmentation and analysis.

The cause of this issue is that the new custom data model object (DMO) is not of category Profile. A category is a property of a DMO that defines its purpose and functionality in Data Cloud. There are three categories of DMOs: Profile, Event, and Other. Profile DMOs are used to store attributes of individuals or entities, such as name, email, address, etc. Event DMOs are used to store actions or interactions of individuals or entities, such as purchases, clicks, visits, etc. Other DMOs are used to store any other type of data that does not fit into the Profile or Event categories, such as products, locations, categories, etc. Only Profile DMOs can be used for creating segments in Data Cloud, as segments are based on the attributes of individuals or entities. Therefore, if the new custom DMO is not of category Profile, it will not appear in the segmentation canvas. The other options are not correct because they are not the cause of this issue. Data ingestion is not a prerequisite for creating segments, as segments can be created based on the data model schema without actual data. The new DMO does not need to have a relationship to the individual DMO, as segments can be created based on any Profile DMO, regardless of its relationship to other DMOs. Segmentation is not only supported for the Individual and Unified Individual DMOs, as segments can be created based on any Profile DMO, including custom ones. References: Create a Custom Data Model Object from an Existing Data Model Object, Create a Segment in Data Cloud, Data Model Object Category

 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. References: Segment Membership Data Model Object, Create a Calculated Insight, Create a Nested Segment