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Cumulus Financial uses calculated insights to compute the total banking value per branch for its high net worth customers. In the
calculated insight, "banking value" is a metric, "branch" is a dimension, and "high net worth" is a filter.
What can be included as an attribute in activation?
According to the Salesforce Data Cloud documentation, an attribute is a dimension or a measure that can be used in activation. A dimension is a categorical variable that can be used to group or filter data, such as branch, region, or product. A measure is a numerical variable that can be used to calculate metrics, such as revenue, profit, or count. A filter is a condition that can be applied to limit the data that is used in a calculated insight, such as high net worth, age range, or gender. In this question, the calculated insight uses ''banking value'' as a metric, which is a measure, and ''branch'' as a dimension. Therefore, only ''branch'' can be included as an attribute in activation, since it is a dimension. The other options are either measures or filters, which are not attributes.
Cloud Kicks wants to be able to build a segment of customers who have visited its website within the previous 7 days.
Which filter operator on the Engagement Date field fits this use case?
Which data stream category should be assigned to use the data for time-based operations in segmentation and calculated insights?
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.
A customer has a Master Customer table from their CRM to ingest into Data Cloud. The
table contains a name and primary email address, along with other personally Identifiable
information (Pll).
How should the fields be mapped to support identity resolution?
To support identity resolution in Data Cloud, the fields from the Master Customer table should be mapped to the standard data model objects that are designed for this purpose. The Individual object is used to store the name and other personally identifiable information (PII) of a customer, while the Contact Phone Email object is used to store the primary email address and other contact information of a customer. These objects are linked by a relationship field that indicates the contact information belongs to the individual. By mapping the fields to these objects, Data Cloud can use the identity resolution rules to match and reconcile the profiles from different sources based on the name and email address fields. The other options are not recommended because they either create a new custom object that is not part of the standard data model, or map all fields to the Customer object that is not intended for identity resolution, or map all fields to the Individual object that does not have a standard email address field.
Northern Trail Outfitters (NTD) creates a calculated insight to compute recency, frequency,
monetary {RFM) scores on its unified individuals. NTO then creates a segment based on these scores
that it activates to a Marketing Cloud activation target.
Which two actions are required when configuring the activation?
Choose 2 answers
To configure an activation to a Marketing Cloud activation target, you need to choose a segment and select contact points. Choosing a segment allows you to specify which unified individuals you want to activate. Selecting contact points allows you to map the attributes from the segment to the fields in the Marketing Cloud data extension. You do not need to add additional attributes or add the calculated insight in the activation, as these are already part of the segment definition.
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