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Salesforce Marketing-Cloud-Intelligence Exam Sample Questions

Question # 1

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)

Answer

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.

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Question # 2

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. The client would like to have a "Site Revenue" measurement. This measurement should return the highest revenue value per Site, for example: For Site Key ‘SK_C_2’, the "Site Revenue" should be $7.00. When aggregated by date, the "Site Revenue" measurement should return the total sum of the results of all sites. For example: For the date 1 Apr 2020, "Site Revenue" should be $11.00 (sum of Site Revenue for Site Keys ‘SK_C_1' ($4.00) and ‘SK_C_2’ ($7.00)) Which options will yield the desired result;

A. Option #1 & Option #4

B. Option #1 & Option #3

C. Option #2 & Option #3

D. Option #2 & Option #4

Answer

D. Option #2 & Option #4

Explanation:
Option #2: It suggests using the 'SUM' function to aggregate the 'Site Revenue' for each 'Site Key'. This is necessary to ensure that when aggregated by date, 'Site Revenue' should return the total sum of the highest revenue for all sites.
Option #4: It indicates changing the Aggregation Function of Revenue to 'MAX' within Data Stream C. This ensures that for a given 'Site Key', the highest revenue value is selected, which is correct for individual site revenue determination.
Combining Option #2 and Option #4 will provide the desired result:
For an individual 'Site Key', it will give the highest revenue (using MAX aggregation in Option #4). When aggregating by date across all 'Site Key's, it will sum the highest revenues (using the SUM function in Option #2).

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Question # 3

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 and apply two Classification Rules (one for ‘Messi’ and another for Ronaldo’) for the final Harmonized Dimension.

B. Create a second Pattern for Media Buy Name

C. Create a second Pattern for Media Buy Name and add a validation list (with the two values) for the final Harmonized Dimension.

D. Create a second Pattern for Media Buy Name and apply a Classification Rule (with the two values) for the final Harmonized Dimension

Answer

A. 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.

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.

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Question # 4

An implementation engineer has been provided with the below dataset: *Note: CPC = Cost per Click Formula: Cost / Clicks Which action should an engineer take to successfully integrate CPC?

A. Populate the logic within a custom measurement. No need to change Aggregation.

B. Unmap it, as Datorama will calculate it automatically.

C. Populate the logic within a custom measurement. Set Aggregation to AVG.

D. Populate the logic within a custom measurement. Set Aggregation to SUM.

Answer

A. Populate the logic within a custom measurement. No need to change Aggregation.

Explanation: CPC (Cost per Click) is a calculated metric that should be created using a custom measurement based on the formula provided (Cost / Clicks). This calculation does not require a change in the aggregation setting because it is derived from other base metrics that are already aggregated appropriately. In Salesforce Marketing Cloud Intelligence, custom measurements are used to create new metrics from existing data points, and the system will use the underlying data's aggregation to perform the calculation. References: Salesforce Marketing Cloud Intelligence documentation on creating custom measurements and calculated metrics.

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Question # 5

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.

Answer

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.

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