Salesforce Marketing-Cloud-Intelligence Exam Dumps

In Salesforce Marketing Cloud Intelligence, connections between source files and classification files are established through common keys that link data records. For this scenario:

The '1st Party Creative Classification' file has a 'Creative ID' field which corresponds to the 'Creative Key' in the 'Twitter Ads' data. This link enables enrichment of Twitter Ads data with creative classification details.

The '1st Party Placement Classification' file will contain a 'Placement ID' that connects to a corresponding field in the 'Twitter Ads' data, enabling the enrichment of placement classification details.

Option A appears to accurately depict this setup where data streams for 'Creative Classification' and 'Placement Classification' are connected to the 'Twitter Ads' data stream using the 'Creative ID' and 'Placement ID', respectively. This structure allows for the enhancement of the main Twitter Ads data with additional classification information.

The discrepancy of 'Clicks' being reported as 4 for 'Type1' when the sum of clicks in the dataset for 'Type1' is 8 (2 on 02/02/2021 and 6 on 03/02/2021) suggests that the aggregation function used in the pivot table is set to average (AVG) rather than sum. Salesforce Marketing Cloud Intelligence allows setting different aggregation functions for metrics, and setting it to average would result in such a discrepancy when more than one entry for the same type exists. Reference: Salesforce Marketing Cloud Intelligence documentation on custom measurements and data aggregations explains how to set and understand different aggregation functions.

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.

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.

Case Sensitivity Issue:

The discrepancy in the 'Clicks' value for Product Group A (10 instead of 15) likely arises from a mismatch caused by case sensitivity in the classification rules. If some data entries use different capitalization (e.g., 'Product Group A' vs. 'product group a'), the system might treat them as distinct entries, leading to incorrect aggregations.

Solution:

By unchecking the 'Case Sensitive' checkbox, the harmonization process will treat entries with different capitalization as the same value. This ensures consistent classification and resolves discrepancies in aggregated metrics like 'Clicks.'