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The questions for Marketing-Cloud-Intelligence were last updated on Feb 8, 2025.
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What are unstable measurements?
Unstable measurements refer to metrics that are not aggregated in a standard manner across different grains of data, which can result in inconsistent or unpredictable results when reporting across different dimensions or time frames.
Option C describes a scenario where measurements have manual (Not Auto) aggregation settings, meaning they do not automatically adjust to the aggregation level of the report. Combined with a Granularity setting of 'None', this can lead to instability because the metric isn't bound to a specific granularity, which can cause data inconsistencies or misinterpretations when analyzed at varying levels of detail.
Your client would like to create a new harmonization field - Exam Topic.
The below table represents the harmonization logic from each source.
As can be seen from the table, there are in fact two fields that hold a certain connection: Exam ID and Exam Topic. The connection indicates that
where an Exam ID is found - a single Exam Topic value is associated with it.
The client has a requirement to be able to view measurements from all data sources sliced by Exam Topic values, as seen in the following
example:
The client suggested to create, without any mapping manipulations, several patterns via the harmonization center that will generate two
Harmonized Dimensions:
Exam ID
Exam Topic
Given the above information, which statement is correct regarding the ability to implement this request with the above suggestion?
If the harmonization logic consistently associates a single Exam Topic with each Exam ID across all data sources, then creating two harmonized dimensions may be unnecessary. One harmonized dimension for Exam Topic would suffice because it inherently carries the Exam ID's uniqueness within it. The harmonized dimension for Exam Topic would allow the client to slice the data by Exam Topic values, fulfilling the requirement.
A technical architect is provided with the logic and Opportunity file shown below:
The opportunity status logic is as follows:
For the opportunity stages ''Interest'', ''Confirmed Interest'' and ''Registered'', the status should be ''Open''.
For the opportunity stage ''Closed'', the opportunity status should be closed.
Otherwise, return null for the opportunity status.
Given the above file and logic and assuming that the file is mapped in a GENERIC data stream type with the following mapping:
''Day'' --- Standard ''Day'' field
''Opportunity Key'' > Main Generic Entity Key
''Opportunity Stage'' --- Generic Entity Key 2
''Opportunity Count'' --- Generic Custom Metric
A pivot table was created to present the count of opportunities in each stage. The pivot table is filtered on Jan 7th - 10th. How many different stages are presented in the table?
Based on the Opportunity file and considering the filter dates from January 7th to 10th, the different stages presented are 'Interest', 'Confirmed Interest', and 'Registered'. This makes a total of 3 different stages that would be presented in the pivot table. Salesforce Marketing Cloud Intelligence allows for the creation of pivot tables that can display counts of entities across different dimensions, in this case, Opportunity Stages. Reference to Salesforce Marketing Cloud Intelligence documentation that covers data mapping and pivot table creation would support this conclusion.
Which two statements are correct regarding LiteConnect?
LiteConnect is a feature in Salesforce Marketing Cloud Intelligence that allows users to bring external data into the platform quickly and easily. Here are the correct statements regarding LiteConnect:
A . LiteConnect allows for a quick setup by not requiring detailed identification of entities, keys, or categorization. Users can upload files without having to conform to the standard data model, which speeds up the process of data integration.
B . With LiteConnect, datasets are uploaded in their native format and do not conform to the standard data model of Marketing Cloud Intelligence. This means that the original structure of the dataset is maintained, and there is no need for extensive transformation or mapping upon the initial data import.
For C and D: While LiteConnect datasets might not conform to the standard data model initially, there are capabilities within Marketing Cloud Intelligence to further categorize and harmonize this data if needed. Therefore, C is not entirely correct, and D is incorrect because harmonization can indeed occur at a later step.
A client's data consists of three data sources - Facebook Ads, LinkedIn Ads and Google Campaign Manager.
Notes:
* The client is planning on adding an additional 100 Facebook Ads data streams and 50 more LinkedIn Ads data streams.
* The final volume of data in the workspace will be 5M rows
* Each data source has a naming convention and it can be assumed that any additional profile (i.e. Data Stream) from one of these sources will follow the same naming convention.
The client provided the following sample files:
Facebook Ads:
The client would like to create a new harmonization field named "Market," which will only be coming from Facebook Ads and LinkedIn Ads. The logic for
"Market" is the following:
IF Media Buy Type is equal to "TypeB" or "TypeC" or "TypeD"
Return 'Europe'
ELSE
Return 'Rest Of The World'
In order to create the harmonization field Market, the client considers using either Mapping Formula, Calculated Dimension, VLOOKUP or Patterns.
Considering maintenance and scalability, which option is recommended?
Patterns are the best approach in this scenario because:
Scalability: Patterns are highly scalable and can easily handle the addition of 100 more Facebook Ads and 50 more LinkedIn Ads streams. You can define pattern-matching rules that automatically apply to new data streams based on the naming conventions.
Flexibility and Maintenance: Patterns allow you to maintain and adjust logic easily. Since the logic for determining 'Market' is based on a defined naming convention (e.g., Media Buy Type), Patterns can handle these rules effectively without requiring manual updates or static tables.
Efficient Harmonization: Patterns automatically classify data based on defined rules, reducing the need for ongoing manual maintenance compared to approaches like VLOOKUP or Mapping Formulas, which might require frequent updates as data changes.
Why not other options?
Mapping Formulas: While Mapping Formulas work well for static mappings, they are not as scalable or maintainable when the dataset grows or changes frequently.
Calculated Dimension: This option is valid for simple logic but is less maintainable for large-scale datasets, especially when new data streams are added.
VLOOKUP: This method is manual and not scalable. It would require you to update lookup tables for each new data stream, which is inefficient given the expected growth of the data.
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