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Salesforce Marketing-Cloud-Intelligence Exam Syllabus Topics:

Topic	Details
Topic 1	Data Integration Code Ability: This section evaluates proficiency with common Marketing Cloud Intelligence functions, enabling Salesforce marketing professionals to integrate diverse data sources effectively for comprehensive marketing intelligence.
Topic 2	Overarching Entities: Salesforce marketing professionals will deepen their understanding of overarching entities, their use cases, and application, crucial for strategic data organization and analysis.
Topic 3	Design Feasibility: This area evaluates the ability to identify valid and invalid solutions from solution design diagrams, ensuring effective and scalable platform designs.
Topic 4	General Functionalities: In this topic, Salesforce marketing professionals will explore core functionalities of Marketing Cloud Intelligence. It measures understanding of platform features critical to data-driven marketing strategies and insights.
Topic 5	Data Fusion: This topic focuses on the use cases and properties of Data Fusion, equipping marketing professionals to merge datasets effectively for comprehensive marketing insights.
Topic 6	QA Ability: This section focuses on common QA steps for various scenarios, enabling Salesforce marketing professionals to ensure data quality and platform performance.
Topic 7	Vlookup: This section evaluates proficiency of marketing professionals in Vlookup statements and their properties, ensuring accurate data referencing and streamlined data manipulation for marketing intelligence tasks.
Topic 8	Harmonization Center (Patterns Data Classification Validation): Salesforce marketing professionals will learn about the Harmonization Center’s capabilities, including classification rules, validation lists, patterns, and harmonized dimensions to ensure data reliability.
Topic 9	Calculated Dimensions & Measurements: This section measures skills in using calculated objects, recognizing aggregation types, and employing these tools for tailored marketing analytics.
Topic 10	Mapping: Marketing professionals will focus on Marketing Cloud Intelligence ingestion capabilities, assessing knowledge of data mapping processes and outcomes critical to efficient data organization.
Topic 11	Harmonization Best Practices: Salesforce marketing professionals will analyze harmonization methods, properties, and their advantages and disadvantages, enhancing skills for optimizing data consistency across platforms.

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Salesforce Marketing Cloud Intelligence Accredited Professional Exam Sample Questions (Q20-Q25):

NEW QUESTION # 20
An implementation engineer has been asked to perform a QA for a newly created harmonization field, Color, implemented by a client.
The source file that was ingested can be seen below:

The client performed the below standard mapping:

As a final step, the client had created the field 'Color'. As can be seen, it is extracted from the Creative Name (after the '#' sign).
For QA purposes, you have queried a pivot table, with the following fields:
* Media Buy Key
* Media Buy Name
* In View Impressions
The final pivot is presented below:

A. An EXTRACT formula (for Color) was written and mapped to a Creative custom attribute.
B. A calculated dimension was created with the formula: EXTRACT([Creative_Namel, #1)
C. A Harmonized dimension was created via a pattern over the Creative Name.
D. An EXTRACT formula (for Color) was written and mapped to a Media Buy custom attribute.

Answer: A

Explanation:
Given that the 'Color' field is extracted from the 'Creative Name' field and appears to be part of the creative-level data, the most logical method would be to create an EXTRACT formula and map it to a Creative custom attribute. This allows the 'Color' value to be associated directly with each creative entry. In Salesforce Marketing Cloud Intelligence, the EXTRACT formula can be used to parse and segment text strings within a field, and this process is used for harmonizing data by creating new dimensions or attributes based on existing data, which is what's described here. This answer is consistent with Salesforce Marketing Cloud Intelligence features that enable data transformation and harmonization through formulaic mapping, as per the official Salesforce documentation on data harmonization and transformation.

NEW QUESTION # 21
Which three statements accurately describe the different data stream types in Marketing Cloud intelligence?

A. Each data stream type has Its own main entity
B. All data stream types consist of at least one entity
C. Each data stream type has its own set of measurements
D. All data stream types share at least one mutual measurement
E. Every data stream type includes the Medio Buy entity

Answer: A,B,C

Explanation:
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.

NEW QUESTION # 22
The following file was uploaded into Marketing Cloud Intelligence as a Generic Data Stream type:

The mapping is as follows:
Day - Day
web_site_key -> Main Generic Entity Key
web_site_name -> Main Generic Entity Name
Web_site_source -> Main Generic Entity Attribute 01
Page Views - Generic Metric 1
How many rows will be stored in Marketing Cloud Intelligence after the above file is ingested?

A. 0
B. 1
C. 2
D. 3

Answer: C

Explanation:
With the uploaded file mapped as a Generic Data Stream type, the unique identifier for a row is the combination of 'Day', 'web_site_key', 'web_site_name', and 'Web_site_source'. As 'Day' is mapped to 'Day', 'web_site_key' to 'Main Generic Entity Key', 'web_site_name' to 'Main Generic Entity Name', and 'Web_site_source' to 'Main Generic Entity Attribute 01', each unique combination of these fields will constitute a separate row.
The provided file has 4 unique combinations of 'Day', 'web_site_key', 'web_site_name', and 'Web_site_source', as each line has a unique 'web_site_key' and 'web_site_name'. Consequently, Marketing Cloud Intelligence will store 4 rows, one for each unique combination.

NEW QUESTION # 23
An implementation engineer is requested to create the harmonization field - Magician This field should come from multiple Twitter Ads data streams, and should follow the below logic:

Using the Harmonization Center, the engineer created a single Pattern for Campaign Name. What other action should the engineer take to meetthe requirements?

A. Create a second Pattern for Media Buy Name
B. Create a second Pattern for Media Buy Name and apply two Classification Rules (one for 'Messi' and another for Ronaldo') for the final Harmonized Dimension.
C. Create a second Pattern for Media Buy Name and apply a Classification Rule (with the two values) for the final Harmonized Dimension
D. Create a second Pattern for Media Buy Name and add a validation list (with the two values) for the final Harmonized Dimension.

Answer: B

Explanation:
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.

NEW QUESTION # 24
The following file was uploaded into Marketing Cloud Intelligence as a generic dataset type:

The mapping is as follows:
Day - Day
Web_site_source - Main Generic Entity Attribute 01
Page Views - Generic Metric 1
*Note that 'web_site_key' and 'web_site_name' are NOT mapped.
How many rows will be stored in Marketing Cloud Intelligence after the above file is ingested?

A. 0
B. 1
C. 2
D. 3

Answer: C

Explanation:
In Marketing Cloud Intelligence, when a file is uploaded as a generic dataset type and mapped accordingly, each unique combination of the mapped fields results in a separate row in the database. The file in question has been mapped with 'Day' to 'Day', 'Web_site_source' to 'Main Generic Entity Attribute 01', and 'Page Views' to 'Generic Metric 1'. The 'web_site_key' and 'web_site_name' are not mapped and thus, won't affect the row count.
Since there are 4 unique combinations of the mapped fields in the uploaded file (each day and source combination is unique), Marketing Cloud Intelligence will store 4 rows after ingestion, corresponding to each unique combination of 'Day' and 'Web_site_source'.

NEW QUESTION # 25
......

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