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Salesforce Marketing-Cloud-Intelligence Exam Sample Questions

Question # 1

A client's data consists of three data streams as follows: Data Stream A:

A. Update Attributes

B. Inherit Attributes and Hierarchies

C. Update Attributes and Hierarchies

D. It doesn't matter. As long as Data stream A is set as a Parent', the rest of the Data Updates Permissions are irrelevant.

Answer

B. Inherit Attributes and Hierarchies

Explanation: For the client's data consisting of three data streams, setting Data Stream A as the Parent allows for inheriting attributes and hierarchies from it to the child data streams. This ensures consistency across the data streams, making it possible to analyze the data collectively, using the structure and attributes defined in the Parent data stream.

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Question # 2

Your client provided the following sources: Source 1: As can be seen, the Product values present in sources 2 and 3 are similar and can be linked with the first extraction from ‘Media Buy Name’ in source1 The end goal is to achieve a final view of Product Group alongside Clicks and Sign Ups, as described below: Which two options will meet the client’s requirement and enable the desired view?

A. Custom Classification: 1 Source 1: Custom Classification key will be populated with the extraction of the Media Buy Name. Source 2: ‘Product’ will be mapped to Custom Classification key and ‘Product Group’ to a Custom Classification level. Exam Timer Source 3: ‘Product will be mapped to Custom Classification key. Came

B. Overarching Entities: Source 1: custom classification key will be populated with the extraction of the Media Buy Name. Source 2: ‘Product’ will be mapped to Product field and ‘Product Group’ to Product Name. Source 3: ‘Product’ will be mapped to Product field.

C. Parent Child: All sources will be uploaded to the same data stream type - Ads. The setup is the following: Source 1: Media Buy Key —- Media Buy Key, extracted product value — Media Buy Attribute. Source 2: Product — Media Buy Key, Product Group —- Media Buy Attribute. Source 3: Product — Media Buy Key.

D. Harmonization Center: Patterns from sources 1 and 3 generate harmonized dimension ‘Product’. Data Classification rule, using source 2, is applied on top of the harmonized dimension

Answer

A. Custom Classification: 1
Source 1: Custom Classification key will be populated with the extraction of the Media Buy Name.
Source 2: ‘Product’ will be mapped to Custom Classification key and ‘Product Group’ to a Custom Classification level. Exam Timer
Source 3: ‘Product will be mapped to Custom Classification key. Came
D. Harmonization Center:
Patterns from sources 1 and 3 generate harmonized dimension ‘Product’. Data Classification rule, using source 2, is applied on top of the harmonized dimension

Explanation: To achieve a final view of Product Group alongside Clicks and Sign Ups, we should use:
Option A:
Custom Classification: By using a Custom Classification key populated with the extraction of the Media Buy Name in Source 1, we can then map 'Product' in Source 2 to this key and 'Product Group' to a Custom Classification level. This will allow for grouping and analysis by Product Group, as well as enable the desired view to be created.
Option D:
Harmonization Center: With patterns from Sources 1 and 3, we can create a harmonized dimension 'Product'. Then, by applying a Data Classification rule using Source 2, we can enhance the harmonized dimension. This allows us to align 'Product Group' with the 'Product' from Sources 1 and 3, facilitating an integrated view of Clicks and Sign Ups by Product Group.

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Question # 3

Which three entities and/or functions can be used in an expression when building a calculated dimension?

A. The VLOOKUP function

B. Mapped dimensions

C. The EXTRACT function

D. Calculated dimensions

E. Mapped measurements

Answer

B. Mapped dimensions
C. The EXTRACT function
E. Mapped measurements

Explanation: In the context of Marketing Cloud Intelligence, when building a calculated dimension, you can typically use:
B. Mapped dimensions: These are dimensions that have been brought into Marketing Cloud Intelligence through the data integration process and have been mapped to a known schema or model.
C. The EXTRACT function: This function can be used to dynamically create dimensions by extracting values from a mapped dimension or measurement.
E. Mapped measurements: Similar to mapped dimensions, these are quantitative data points that have been integrated into the platform and can be referenced in calculations.
Calculated dimensions (D) and the VLOOKUP function (A) are not typically used within the expression for a calculated dimension. Calculated dimensions are usually an output, not an input, and VLOOKUP is a function typically used to enrich or connect data, not within the definition of a calculated dimension itself.

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Question # 4

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

Answer

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.

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Question # 5

What are two potential reasons for performance issues (when loading a dashboard) when using the CRM data stream type?

A. When a data stream type ''CRM - Leads' is created, another complementary 'CRM - Opportunity' is created automatically.

B. Pacing - daily rows are being created for every lead and opportunity keys

C. No mappable measurements - all measurements are calculated

D. The data is stored at the workspace level.

Answer

B. Pacing - daily rows are being created for every lead and opportunity keys
C. No mappable measurements - all measurements are calculated

Explanation: For performance issues when loading a dashboard using CRM data stream type:
Pacing can create performance issues because daily rows for every lead and opportunity key can result in a very large number of rows, increasing load times. Having only calculated measurements means there are no direct, mappable values to query against, which can increase the computational load and affect performance.

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