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Marketing-Cloud-Intelligence Practice Questions

Question # 1
What Is a disadvantage of using a Vlookup formula?
A. Can return values only from the same data stream type
B. It cannot be used more than once from the same data stream.
C. Could extend processing time of data streams.
D. It allows classifying data only on a basis of mutual entity keys.


C. Could extend processing time of data streams.

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.

Question # 2
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 # 3
A client’s data consists of three data streams as follows:
Data Stream A:



* 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.

Assuming the data was ingested properly and the Parent Child was created correctly according to the client's requirements, what is the total Impressions value for Campaign Key ‘CK_3’?
A. N-A
B. 100
C. 300
D. 150


B. 100

Explanation: Assuming that Data Stream A is set correctly with parent-child relationships: To find the total impressions for Campaign Key 'CK_3', you would look in Data Stream A, since it contains the 'Impressions' metric. As per the provided data, Campaign Key ‘CK_3’ has 100 impressions.

Question # 4
An implementation engineer has been asked by a client for assistance with the following problem: The below dataset was ingested:



However, when performing QA and querying a pivot table with Campaign Category and Clicks, the value for Type’ is 4.
What could be the reason for this discrepancy?
A. The measurement ‘Clicks’ is set as a percentage.
B. A mapping formula was populated, indicating not to bring Type! values.
C. The aggregation function is set as AVG
D. The aggregation function is set as LIFETIME


C. The aggregation function is set as AVG

Explanation: 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. References: Salesforce Marketing Cloud Intelligence documentation on custom measurements and data aggregations explains how to set and understand different aggregation functions.

Question # 5
A client provides the following three files:



Which proposed solution would cause a false connection between the two files?
A. Custom classification
B. Data Classification
C. VLOOKUP in Data Stream B. Vlookup will return “Day” and “Installs”
D. VLOOKUP in Data Stream C. Vlookup will return "MB Name”


C. VLOOKUP in Data Stream B. Vlookup will return “Day” and “Installs”

Explanation: With File A uploaded using the Ads data stream type, the client wishes to create a view incorporating data from Files B & C. A false connection would occur if VLOOKUP in Data Stream B is used incorrectly to return “Day” and “Installs”. In this scenario, VLOOKUP might inaccurately link data based on MB Name between File B and File A or File C, which do not have a "Day" field to correctly join on. Moreover, "Installs" data in File B doesn't exist, soVLOOKUP cannot correctly return this information. The correct method would be to use the "Media Buy New Name" to link File B and File C since they both have this field, ensuring accurate connection and avoiding data mismatches or false connections.

Question # 6
Ina workspace that contains one hundred data streams and a lot of data, what is the biggest downside of using calculated dimensions?
A. Performance
B. Ease of setup
C. Ease of maintenance
D. Scalability


A. Performance

Explanation: In a workspace with a high number of data streams, such as one hundred, the biggest downside of using calculated dimensions is the performance impact. Calculated dimensions require computational resources to dynamically compute values based on existing data. This can lead to increased load times and slower performance, especially in environments with large amounts of data or complex calculations. This performance degradation is due to the extra processing power needed every time the data is accessed or refreshed, impacting the overall efficiency of data retrieval and analysis operations.

Question # 7
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 # 8
A client would like to integrate the following two sources:
Google Campaign Manager:



After configuring a Parent-Child relationship between the files, which query should an implementation engineer run in order to QA the setup?
A. Media Buy Type, Media Buy Name, Impressions, Analyzed Impressions
B. Creative Name, Impressions, Analyzed Impressions
C. Media Buy Name, Impressions
D. Media Buy Type, Analyzed Impressions


A. Media Buy Type, Media Buy Name, Impressions, Analyzed Impressions

Explanation: To QA the Parent-Child relationship setup between Google Campaign Manager and IAS data sources, it is essential to query fields that are common to both sources and that are relevant to the relationship. 'Media Buy Type' and 'Media Buy Name' are common identifiers between the two datasets. 'Impressions' from the Google Campaign Manager and 'Analyzed Impressions' from the IAS data are the metrics that should be compared to ensure they match or correlate as expected due to the Parent-Child relationship. The QA process involves checking that the data is correctly aligned and that the metrics from the parent source (Google Campaign Manager) are properly related to the metrics from the child source (IAS). References: Salesforce Marketing Cloud Intelligence documentation on data integration, Parent-Child relationships, and QA procedures for data setup.

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Salesforce Marketing-Cloud-Intelligence Exam Dumps

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