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Marketing-Cloud-Intelligence Practice Questions

Question # 1
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. 1
B. 0
C. 3
D. 4


D. 4

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.

Question # 2
Your client would like to create a new harmonization field - Exam Topic. The below table represents the harmonization logic from each source.



Which harmonization feature should an Implementation engineer use to meet the client's requirement?
A. Transformers
B. Parent Chile
C. Fusion
D. Custom Classification
E. Calculated dimensions


D. Custom Classification

Explanation: To meet the client's requirement of slicing measurements by 'Exam Topic' values, an Implementation Engineer should use Custom Classification. This feature allows different Exam IDs to be classified into their respective Exam Topics, ensuring that data from all sources can be accurately harmonized and analyzed based on these topics.

Question # 3
A client's data consists of three data streams as follows:



* The data streams should be linked together through a parent-child relationship.
* Out of the three data streams, Data Stream C is considered the source of truth for both the dimensions and measurements.

Which data stream should be set as a parent?
A. Data Stream A
B. Data Stream C
C. Any of the data streams can technically be the parent
D. Data Stream B


B. Data Stream C

Explanation: Since Data Stream C is considered the source of truth for both dimensions and measurements, it should be set as the parent data stream. This is because the parent data stream is used as the primary source for hierarchical and attribute data within a parent-child relationship setup. As the source of truth, Data Stream C will provide the foundational data upon which the other streams can be aligned and will ensure consistency and accuracy across the linked data.

Question # 4
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. 4
B. 0
C. 1
D. 2


A. 4

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'.

Question # 5
What is the relationship between "Media Buy Key" and "Creative Key?
A. One-to-many (one Media Buy ley has many Creative Key)
B. One-to-one
C. Many-to-many
D. Many-to-one (one Creative Key has many Media Buy Keys)


A. One-to-many (one Media Buy ley has many Creative Key)

Explanation: In Marketing Cloud Intelligence, the "Media Buy Key" is typically associated with the purchase details of a media campaign, such as the platform, audience, and budget. The "Creative Key" relates to the specific creative asset used within a campaign, like an image, video, or text. A single media buy can have multiple creative variations to test performance or to target different audiences, leading to a one-to-many relationship.

Question # 6
Which three statements describe Overarching Entities? 03m 23s
A. Once the data streams in which Custom Classification values were mapped are deleted, their data is deleted.
B. Some overarching entities hold a Many-to-Many relationship with the main entity, and others hold a One-to-Many relationship with it.
C. When needed, these entities can act as a main entity, replacing the original one.
D. These are mappable dimensions that are present in each and every dataset type
E. The values of these entities are stored at the workspace level, rather than the data stream level


B. Some overarching entities hold a Many-to-Many relationship with the main entity, and others hold a One-to-Many relationship with it.
C. When needed, these entities can act as a main entity, replacing the original one.
E. The values of these entities are stored at the workspace level, rather than the data stream level

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.

Question # 7
After uploading a standard file into Marketing Cloud intelligence via total Connect, you noticed that the number of rows uploaded (to the specific data stream) is NOT equal to the number of rows present in the source file. What are two resource that may cause this gap?
A. All mapped Measurements for a given row have values equal to zero
B. Main entity is not mapped
C. The source file does not contain the mediaBuy entity
D. The file does not contain any measurements (dimension only)


A. All mapped Measurements for a given row have values equal to zero
B. Main entity is not mapped

Explanation: In Marketing Cloud Intelligence, discrepancies between the number of rows uploaded and the number of rows present in the source file can be caused by several factors. If all mapped measurements for a row are zero, that row may be excluded from the upload, as it does not contribute to the analytics. Additionally, if the main entity, which acts as the primary identifier for records, is not mapped, the system cannot correctly ingest the data as it lacks the necessary reference to organize and store the information.

Question # 8
An implementation engineer is requested to extract the first three-letter segment of the Campaign Name values.
For example:

Campaign Name: AFD@Mulop-1290 Desired outcome: AFD
Other examples:



Which formula will return the desired values?
A. LEFT(EXTRACT(csy[‘campaign_name]],~',0),3)
B. EXTRACT(csv[campaign_name!;@',1)
C. EXTRACT(csv[campaign_name'],—,0)
D. EXTRACT(EXTRACT(csv['campaign_name]]/@',1),-,0)
E. LEFT(EXTRACT(csv[campaign_name'}/—',1),3)


B. EXTRACT(csv[campaign_name!;@',1)

Explanation: The EXTRACT function is used to split a string based on a delimiter and return the segment at the specified position. The campaign names are structured with the segment of interest followed by an '@' sign. Therefore, the formula needs to extract the segment before the '@'.
The correct formula is: EXTRACT(csv['campaign_name']; '@', 1). This will take the 'campaign_name' field, split it at the '@' sign, and return the first segment (position 1), which is the three-letter code that is required. The other options are incorrect because they do not properly specify the delimiter and the segment position in the way needed to achieve the desired outcome.

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Salesforce Marketing-Cloud-Intelligence Exam Dumps

Exam Name: Marketing Cloud Intelligence Accredited Professional
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