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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	Calculated Dimensions & Measurements: This section measures skills in using calculated objects, recognizing aggregation types, and employing these tools for tailored marketing analytics.
Topic 3	CRM: This topic tests knowledge of CRM properties and their behavior within Marketing Cloud Intelligence. This knowledge is crucial for syncing customer relationship data with marketing campaigns.
Topic 4	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 5	Design Feasibility: This area evaluates the ability to identify valid and invalid solutions from solution design diagrams, ensuring effective and scalable platform designs.
Topic 6	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 7	Data Update Permissions: This area tests knowledge of permissions and settings related to data updates. It includes understanding parent-child setups and managing the "Source of Truth" for data accuracy.
Topic 8	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 9	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 10	QA Ability: This section focuses on common QA steps for various scenarios, enabling Salesforce marketing professionals to ensure data quality and platform performance.
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.
Topic 12	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.

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Salesforce Marketing Cloud Intelligence Accredited Professional Exam Sample Questions (Q50-Q55):

NEW QUESTION # 50
A client has provided you with sample files of their data from the following data sources:
1.Google Analytics
2.Salesforce Marketing Cloud
The link between these sources is on the following two fields:
Message Send Key
A portion of: web_site_source_key
Below is the logic the client would like to have implemented in Datorama:
For 'web site medium' values containing the word "email" (in all of its forms), the section after the "_" delimiter in 'web_site_source_key' is a 4 digit number, which matches the 'Message Send Key' values from the Salesforce Marketing Cloud file. Possible examples of this can be seen in the following table:
Google Analytics:

Salesforce Marketing Cloud:

The client's objective is to visualize the mutual key values alongside measurements from both files in a table.

In order to achieve this, what steps should be taken?

A. Create a Web Analytics Site Source custom attribute and populate it with the extraction logic. Create a Data Fusion between the newly created attribute and the Message Send Key.
B. Upload the two files and create a Parent-Child relationship between them. The Override Media Buy Hierarchy checkbox is checked in Google Analytics.
C. Create a Web Analytics Site custom attribute and populate it with the extraction logic. Create a Data Fusion between the newly created attribute and the Message Send Key.
D. Within both files, map the desired value to Custom Classification Key as follows Salesforce Marketing Cloud: map entire Message Key to Custom Classification Key.
Google Analytics: map the extraction logic to Custom Classification Key.

Answer: D

Explanation:
To create a linkage between Google Analytics and Salesforce Marketing Cloud data based on the "Message Send Key" and a portion of the "web_site_source_key," both values need to be harmonized into a common key. This is done by mapping the full Message Send Key from Salesforce Marketing Cloud and the extracted part of the web_site_source_key from Google Analytics to the same Custom Classification Key. This mapping will create a common identifier that can be used to combine the data from both sources for analysis and visualization.

NEW QUESTION # 51
Which three statements describe Overarching Entities? 03m 23s

A. When needed, these entities can act as a main entity, replacing the original one.
B. The values of these entities are stored at the workspace level, rather than the data stream level
C. Once the data streams in which Custom Classification values were mapped are deleted, their data is deleted.
D. Some overarching entities hold a Many-to-Many relationship with the main entity, and others hold a One-to-Many relationship with it.
E. These are mappable dimensions that are present in each and every dataset type

Answer: A,B,D

Explanation:
Overarching Entities in Salesforce Marketing Cloud Intelligence are designed to provide a high level of data organization that spans across multiple data streams. The key points about Overarching Entities are:
* B. Relationship Types: Overarching entities can have either a Many-to-Many or One-to-Many relationship with the main entity, which allows for flexible data modeling and relationship definitions based on the nature of the data and how it should be analyzed and reported.
* C. Acting as Main Entity: They can serve as a main entity in certain situations, enabling a shift in perspective for data analysis. This can be particularly useful when there is a need to view data from a different dimension that is more aligned with business requirements.
* E. Storage Level: The values of these entities are not tied to any single data stream but are maintained at a workspace level, ensuring that they can be applied consistently across different datasets, which is critical for maintaining data integrity and ensuring that classifications are applied uniformly.

NEW QUESTION # 52
What Is a disadvantage of using a Vlookup formula?

A. Can return values only from the same data stream type
B. Could extend processing time of data streams.
C. It cannot be used more than once from the same data stream.
D. It allows classifying data only on a basis of mutual entity keys.

Answer: B

Explanation:
The use of VLOOKUP formulas can increase the processing time of data streams because it requires a lookup operation for each row in the data set. When large volumes of data are involved, or when multiple VLOOKUPs are used, this can significantly impact processing time due to the complexity and computational requirements of matching and retrieving the data.

NEW QUESTION # 53
Your client is interested in ingesting the below file:

The client decided to upload the file to a new generic data stream type and map 'Date' to 'Day' and 'Number of Topics' to a generic custom metric.
In regards to the fields 'Meeting Code' and 'Meeting Name', your client is debating several options.
Which two options would you recommend in order to avoid data loss?

A. 'Meeting Code' will be mapped to 'Main Generic Entity Key'.
'Meeting Name' will be mapped to 'Generic Entity 2 Key'.
B. 'Meeting Code' will be mapped to 'Main Generic Entity Attribute 1'.
'Meeting Name' will be mapped to 'Main Generic Entity Attribute 2'.
C. Concatenation of both 'Meeting Code' and 'Meeting Name' will be mapped to 'Main Generic Entity Key'.
'Meeting Code' will be mapped to 'Main Generic Entity Attribute 1'.
D. 'Meeting Code' will be mapped to 'Main Generic Entity custom attribute'.
'Meeting Name' will be mapped to 'Generic Entity Key'
E. 'Meeting Code' will be mapped to 'Main Generic Entity Key'.
'Meeting Name' will be mapped to 'Main Generic Entity custom attribute'.

Answer: C,E

Explanation:
'Meeting Name' will be mapped to 'Main Generic Entity Attribute 2'.
Explanation:
To avoid data loss and ensure each meeting is uniquely identified and its details are preserved, two mappings are recommended:
Option A:
'Meeting Code' should be mapped to the 'Main Generic Entity Key' to uniquely identify each meeting.
'Meeting Name' should be mapped to a 'Main Generic Entity custom attribute' to store additional information about the meeting.
Option E:
Concatenation of 'Meeting Code' and 'Meeting Name' should be mapped to 'Main Generic Entity Key'. This ensures a unique identifier for each meeting is created combining both pieces of information, preventing any mix-ups between meetings with similar codes or names.
Additionally, mapping 'Meeting Code' and 'Meeting Name' to their respective 'Main Generic Entity Attribute' fields will allow for more detailed filtering and reporting capabilities within Marketing Cloud Intelligence.

NEW QUESTION # 54
An implementation engineer is requested to integrate the following files:
File A:

File B:

The client would like to link the two files in order to view the two KPIS (Tasks Completed' and 'tasks Assignmed') alongside'Employee Name' and/or 'Squard'.
A Parent-Child configuration was set between the two.
Which two statements are correct?

A. The two files cannot be Joined as they hold different measurements
B. The two files cannot be joined as they hold different dates
C. Any one of the files can potentially be set as the Parent data stream
D. The two files were uploaded to a different Generic type
E. The join can be successful even if "empjd' isn't mapped and employee.name' is mapped to the same entity name in both data streams

Answer: C,E

Explanation:
In Marketing Cloud Intelligence, joining two files requires a common field to be mapped as the same entity. If "employee_name" is consistently mapped across both data streams, it can serve as the basis for the join, regardless of whether "employee_id" is mapped. The choice of which file serves as the Parent stream depends on the use case and the desired reporting structure, but technically, either could serve as the Parent.

NEW QUESTION # 55
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