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Salesforce Marketing-Cloud-Intelligence Exam Syllabus Topics:

Topic
Details

Topic 1

Data Integration Code Ability: This section evaluates proficiency with common Marketing Cloud Intelligence functions, enabling Salesforce marketing professionals to integrate diverse data sources effectively for comprehensive marketing intelligence.

Topic 2

Mapping: Marketing professionals will focus on Marketing Cloud Intelligence ingestion capabilities, assessing knowledge of data mapping processes and outcomes critical to efficient data organization.

Topic 3

Vlookup: This section evaluates proficiency of marketing professionals in Vlookup statements and their properties, ensuring accurate data referencing and streamlined data manipulation for marketing intelligence tasks.

Topic 4

Harmonization Center (Patterns
Data Classification
Validation): Salesforce marketing professionals will learn about the Harmonization Center’s capabilities, including classification rules, validation lists, patterns, and harmonized dimensions to ensure data reliability.

Topic 5

Overarching Entities: Salesforce marketing professionals will deepen their understanding of overarching entities, their use cases, and application, crucial for strategic data organization and analysis.

Topic 6

Harmonization Best Practices: Salesforce marketing professionals will analyze harmonization methods, properties, and their advantages and disadvantages, enhancing skills for optimizing data consistency across platforms.

Topic 7

QA Ability: This section focuses on common QA steps for various scenarios, enabling Salesforce marketing professionals to ensure data quality and platform performance.

Topic 8

Design Feasibility: This area evaluates the ability to identify valid and invalid solutions from solution design diagrams, ensuring effective and scalable platform designs.

Salesforce Marketing Cloud Intelligence Accredited Professional Exam Sample Questions (Q24-Q29):

NEW QUESTION # 24
A client created a new KPI: CPS (Cost per Sign-up).
The new KIP is mapped within the data stream mapping, and is populated with the following logic: (Media Cost) / Sign-ups) As can be seen in the table below, CPS was created twice and was set with two different aggregations:

From looking at the table, what are the aggregation settings for each one of the newly created KPIs?

Answer: B

Explanation:
The KPI CPS (Cost per Sign-up) would be calculated by dividing the 'Media Cost' by 'Sign-ups'. The table indicates that CPS is set with two different aggregations. In option C, CPS #1 is set to 'AUTO', which allows the system to decide the best aggregation method based on the context. CPS #2 is set to 'SUM', which indicates that the individual costs per sign-up are summed up across multiple records to provide a total cost per sign-up.

NEW QUESTION # 25
An implementation engineer has been asked to perform a QA for a newly created harmonization field, Color, implemented by a client.
The source file that was ingested can be seen below:

The client performed the below standard mapping:

As a final step, the client had created the field 'Color'. As can be seen, it is extracted from the Creative Name (after the '#' sign).
For QA purposes, you have queried a pivot table, with the following fields:
* Media Buy Key
* Media Buy Name
* In View Impressions
The final pivot is presented below:

A. A Harmonized dimension was created via a pattern over the Creative Name.
B. An EXTRACT formula (for Color) was written and mapped to a Creative custom attribute.
C. An EXTRACT formula (for Color) was written and mapped to a Media Buy custom attribute.
D. A calculated dimension was created with the formula: EXTRACT([Creative_Namel, #1)

Answer: B

Explanation:
Given that the 'Color' field is extracted from the 'Creative Name' field and appears to be part of the creative-level data, the most logical method would be to create an EXTRACT formula and map it to a Creative custom attribute. This allows the 'Color' value to be associated directly with each creative entry. In Salesforce Marketing Cloud Intelligence, the EXTRACT formula can be used to parse and segment text strings within a field, and this process is used for harmonizing data by creating new dimensions or attributes based on existing data, which is what's described here. This answer is consistent with Salesforce Marketing Cloud Intelligence features that enable data transformation and harmonization through formulaic mapping, as per the official Salesforce documentation on data harmonization and transformation.

NEW QUESTION # 26
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 Jan 7th - 10th. How many different stages are presented in the table?

A. 0
B. 1
C. 2
D. 3

Answer: A

Explanation:
Based on the Opportunity file and considering the filter dates from January 7th to 10th, the different stages presented are 'Interest', 'Confirmed Interest', and 'Registered'. This makes a total of 3 different stages that would be presented in the pivot table. Salesforce Marketing Cloud Intelligence allows for the creation of pivot tables that can display counts of entities across different dimensions, in this case, Opportunity Stages. Reference to Salesforce Marketing Cloud Intelligence documentation that covers data mapping and pivot table creation would support this conclusion.

NEW QUESTION # 27
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 assume that the file is mapped in the OPPORTUNITIES Data Stream type with the following mapping:
"Day" - "Created Date"
"Opportunity Key" + Opportunity Key
"Opportunity Stage" - Opportunity Stage
A pivot table was created to present the count of opportunities in each stage. The pivot table is filtered on Jan
11th. What is the number of'opportunitiesin the Confirmed Interest stage?

A. 0
B. 1
C. 2
D. 3

Answer: C

Explanation:
pivot table is filtered on January 11th, we refer to the Opportunity file and see that there are no records for January 11th. Thus, there would be zero opportunities in the Confirmed Interest stage on that date. The Salesforce Marketing Cloud Intelligence's pivot table feature allows for the display of counts of entities based on the filtered criteria, which in this scenario would show zero since no records exist for the filtered date.
Reference: Salesforce Marketing Cloud Intelligence documentation on pivot table functionalities.

NEW QUESTION # 28
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 #2 & Option #4
C. Option #1 & Option #3
D. Option #2 & Option #3

Answer: B

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).

NEW QUESTION # 29
......

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Jack Cook Jack Cook

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