Marketing-Cloud-Intelligence Exam Dumps : Marketing Cloud Intelligence Accredited Professional (AP-215)

For the field 'Magician', the engineer is required to follow a logic that extracts a value from 'Campaign Name' and checks against a validation list for specific values ('Messi' or 'Ronaldo'). If those values are not found, it should instead extract from 'Media Buy Name'. To accomplish this, the engineer should:

Use the created Pattern for 'Campaign Name'.

Create a second Pattern for 'Media Buy Name' to capture the fallback values.

Apply two Classification Rules to the Harmonized Dimension: one for the value 'Messi' and another for 'Ronaldo'. This is to check the extracted 'Campaign Name' against these specific values.

These steps ensure that the 'Magician' field will be populated with the correct values from the respective data streams following the specified logic.

To create both 'Platform' and 'Line of Business' fields using Patterns in the Harmonization Center without mapping manipulations, the engineer would need to create separate patterns for each data source mentioned. According to the provided images:

One pattern for LinkedIn Ads, to extract the 'Campaign Name' at position 4 for the Platform and 'Media Buy Name' at position 7 for Line of Business.

One pattern for AdRoll, to extract 'Media Buy Name' at position 3 for Platform and at position 2 for Line of Business.

One pattern for Google Analytics, which seems not required for the Platform but could apply if the Line of Business extraction is necessary, although it states N/A.

Hence, a minimum of 3 patterns would be necessary to create the fields required.

In Marketing Cloud Intelligence, data stream types are templates that define how data should be structured within the system. Each data stream type:

B. Includes at least one entity, which is a fundamental component of the data stream and represents a collection of related data points.

D. Has its own main entity, which is the primary focus of that particular data stream type and serves as the central point of reference for the associated data.

E. Contains its own unique set of measurements that are specific to the type of data being captured within that stream. These measurements represent quantitative data that can be analyzed within the context of the main entity and other dimensions present in the data stream.

A is incorrect because not every data stream type includes the Media Buy entity—this is specific to certain types of advertising data streams. C is incorrect because not all data stream types share at least one mutual measurement; measurements are typically unique to the data stream's focus and purpose.