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Marketing-Cloud-Intelligence Practice Questions

Question # 1
Which two statements are correct regarding LiteConnect?
A. It does not require any identification of entities, keys or any other categorization.
B. The dataset does not conform to the standard data model
C. All of the dimensions mapped within a LiteConnect data stream are considered overarching entities.
D. Data coming from LiteConnect cannot be harmonized with the rest of the workspace data via the harmonization center at a later step.


A. It does not require any identification of entities, keys or any other categorization.
B. The dataset does not conform to the standard data model

Explanation: 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.

Question # 2
A client provides the following two data streams:
Data Stream 1:



Question-

The client would like to use a VLOOKUP formula to calculate the Cost per Campaign Advertiser on January 1st 2020. Which mapping options should the client apply to obtain the expected result?

A. Option A
B. Option B
C. Option C
D. Option D


A. Option A

Explanation: To calculate Cost per Campaign Advertiser using a VLOOKUP formula, the client needs to look up the 'Cost' from Data Stream 2 based on a matching 'Media Buy Name' in Data Stream 1. Option A shows that 'Media Buy Name' is the lookup value, which is correct. The 'Campaign Advertiser' is then linked to the 'Cost' from Data Stream 2 through the VLOOKUP formula applied to the 'Media Buy Custom Attribute 01' in Data Stream 2. This setup will correctly associate the cost with the campaign advertiser.

Question # 3
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 # 4
A client wants to integrate their data within Marketing Cloud Intelligence to optimize their marketing insights and cross-channel marketing activityanalysis. Below are details regarding the different data sources and the number of data streams required for each source.



What three advantages are gained when using Patterns & Data Classification as the harmonization method for creating the Objective field?
A. Ease of Maintenance
B. Performance (Performance when loading a dashboard page)
C. Use of code
D. Scalability
E. Processing (processing time when loading relevant data streams)


A. Ease of Maintenance
B. Performance (Performance when loading a dashboard page)
D. Scalability

Explanation: Patterns & Data Classification in Marketing Cloud Intelligence offer several advantages. These include:
Ease of Maintenance (A):Patterns allow for the standardization of data harmonization processes. Once set up, they can be easily maintained and adjusted as needed, without having to manipulate each data stream individually. Performance (B):By using patterns, data is classified and standardized at ingestion, which can improve the performance of dashboard page loading because the system does not need to perform complex, on-the-fly calculations or transformations.
Scalability (D):Patterns can be applied across multiple data streams consistently, allowing them to scale with the data. This means that as the amount of data grows or as new data sources are added, the same patterns can be reused, ensuring that the data remains harmonized.
Reference: Information is based on general knowledge of marketing data harmonization and standard practices as patterns and classification rules are common features in data platforms, extrapolated to fit the context of Salesforce Marketing Cloud Intelligence.

Question # 5
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:



In order to achieve this, what steps should be taken?
A. 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.
B. 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.
C. Upload the two files and create a Parent-Child relationship between them. The Override Media Buy Hierarchy checkbox is checked in Google Analytics.
D. 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.


A. 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.

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.

Question # 6
A client wants to integrate their data within Marketing Cloud Intelligence to optimize their marketing Insights and cross-channel marketing activity analysis. Below are details regarding the different data sources and the number of data streams required for each source.



Which three advantages does a client gain from using Calculated Dimensions as the harmonization method for creating the Objective field?
A. Scalability - future data streams that will follow similar logic will be automatically harmonized.
B. Data model restrictions - Calculated Dimensions do not need to adhere to Marketing Cloud Intelligence's data model
C. Processing - creation of Calculated Dimensions will ease the processing time of the data streams it relates to
D. Ease of Maintenance - the logic is written and populated in one centralized place
E. Performance (Performance when loading a dashboard page) should be optimized as the values of calculated dimensions are stored within the database.


A. Scalability - future data streams that will follow similar logic will be automatically harmonized.
D. Ease of Maintenance - the logic is written and populated in one centralized place
E. Performance (Performance when loading a dashboard page) should be optimized as the values of calculated dimensions are stored within the database.

Explanation: Scalability: Using Calculated Dimensions allows the client to apply the same harmonization logic to future data streams, ensuring consistency and reducing the need for individual adjustments.
Ease of Maintenance: With the logic centralized in Calculated Dimensions, any adjustments or updates are applied in one place, simplifying ongoing management.
Performance: Calculated Dimensions can improve dashboard performance because their values are pre-computed and stored, reducing the need for real-time calculations when loading dashboards.

Question # 7
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 January (entire month). What is the number of opportunities in the Interest stage?
A. 1
B. 3
C. 2
D. 0


B. 3

Explanation: Based on the Opportunity file, the Opportunity Stage of 'Interest' occurs 3 times across unique Opportunity Keys. Since the pivot table is filtered to present the entire month of January and the Opportunity Stage 'Interest' is listed three times with different Opportunity Keys, the count of opportunities in the 'Interest' stage would be 3.

Question # 8
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'.

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Salesforce Marketing-Cloud-Intelligence Exam Dumps

Exam Name: Marketing Cloud Intelligence Accredited Professional
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