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Salesforce Marketing-Cloud-Intelligence Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 3

CRM: This topic tests knowledge of CRM properties and their behavior within Marketing Cloud Intelligence. This knowledge is crucial for syncing customer relationship data with marketing campaigns.

Topic 4

Data Model: In this domain, marketing professionals will explore data model entities, their relationships, and attributes within Marketing Cloud Intelligence.

Topic 5

Data Fusion: This topic focuses on the use cases and properties of Data Fusion, equipping marketing professionals to merge datasets effectively for comprehensive marketing insights.

Topic 6

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 7

General Functionalities: In this topic, Salesforce marketing professionals will explore core functionalities of Marketing Cloud Intelligence. It measures understanding of platform features critical to data-driven marketing strategies and insights.

Topic 8

Calculated Dimensions & Measurements: This section measures skills in using calculated objects, recognizing aggregation types, and employing these tools for tailored marketing analytics.

Topic 9

Data Update Permissions: This area tests knowledge of permissions and settings related to data updates. It includes understanding parent-child setups and managing the "Source of Truth" for data accuracy.

Topic 10

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 11

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

Topic 12

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 13

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

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Salesforce Marketing Cloud Intelligence Accredited Professional Exam Sample Questions (Q55-Q60):

NEW QUESTION # 55
An implementation engineer is requested to extract the second position
of the Campaign Name values.
The Campaign values consist of multiple delimiter types, as can be
seen in the following example:
Campaign Name: Ad15X2w&Delux_wal90
Desired value: Delux
Which three harmonization methods will achieve the desired outcome?

A. Patterns
B. Mapping formula
C. Vlookup 0
D. Data Fusion
E. Calculated Dimensions

Answer: A,B,E

Explanation:
To extract specific elements from a string in Marketing Cloud Intelligence, such as the second position of a Campaign Name with multiple delimiters, several harmonization methods can be employed:
Calculated Dimensions: These allow for the creation of custom dimensions using expressions or formulas that manipulate existing data. A calculated dimension can be designed to parse and extract segments of a string based on delimiters.
Patterns: This method involves defining a pattern or regex (regular expression) that matches and isolates the desired portion of the string. Patterns are highly effective for strings with complex structures and varying delimiter types.
Mapping Formula: Similar to calculated dimensions, mapping formulas provide a way to apply a transformation or extraction rule to data fields directly within data streams, enabling targeted data extraction like the desired 'Delux' from the Campaign Name.
These methods enable the implementation engineer to accurately segment and extract the needed data from complex string fields efficiently.

NEW QUESTION # 56
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
A pivot table was created to present the count of opportunities in each stage. The pivot table is filtered on Jan 7th -11th.Which option reflects the stage(s) the opportunity key 123AA01 is associated with?

A. Confirmed interest
B. Confirmed Interest & Registered
C. Interest & Registered
D. interest

Answer: C

Explanation:
Filtering the pivot table on January 7th-11th, we see that the Opportunity Key 123AA01 appears on January 6th with the stage 'Interest' and then on January 10th with the stage 'Registered'. Even though the 'Interest' stage is not within the filtered dates, it is the initial stage of the opportunity, so it should be counted along with the 'Registered' stage which falls within the filter range.

NEW QUESTION # 57
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: C

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 CloudIntelligence 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 # 58
A client has integrated data from Facebook Ads, Twitter Ads, and Google Ads in Marketing Cloud Intelligence. For each data source, the data follows a naming convention as shown below:
Facebook Ads Naming Convention - Campaign Name:
Camp|D_CampName#Market_Objective#TargetAge_TargetGender
Twitter Ads Naming Convention - Media Buy Name:
Market|TargetAge|Objective|OrderID
' Google Ads Naming Convention - Media Buy Name:
Buying Type_Market_Objective
The client wants to harmonize their data on the common fields between these two platforms (i.e. Market and Objective) using the Harmonization 'Center.
In addition to the previous details, the client provides the following data sample:

Logic specification:
If a value is not present in the Validation List, return "Not Valid"
If a value is not present in the Classification File, return "Unclassified".
If the Harmonization center is used to harmonize the above data and files, what table will show the final output?

Answer: B

Explanation:
The correct table would be Option B. The harmonization process would identify the 'Market' from the campaign or media buy name based on the delimiter and position rules specified in the naming conventions.
The harmonized 'Market' would then be matched against the classification file and validation list. If a value does not match the validation list, it would return 'Not Valid', and if it's not present in the classification file, it would return 'Unclassified'. Option B is the only table showing the 'Not Valid' category which aligns with the logic specification provided.

NEW QUESTION # 59
The following file was uploaded into Marketing Cloud Intelligence as a Generic Data Stream type:

The mapping is as follows:
Day - Day
web_site_key -> Main Generic Entity Key
web_site_name -> Main Generic Entity Name
Web_site_source -> Main Generic Entity Attribute 01
Page Views - Generic Metric 1
How many rows will be stored in Marketing Cloud Intelligence after the above file is ingested?

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

Answer: C

Explanation:
With the uploaded file mapped as a Generic Data Stream type, the unique identifier for a row is the combination of 'Day', 'web_site_key', 'web_site_name', and 'Web_site_source'. As 'Day' is mapped to 'Day', 'web_site_key' to 'Main Generic Entity Key', 'web_site_name' to 'Main Generic Entity Name', and 'Web_site_source' to 'Main Generic Entity Attribute 01', each unique combination of these fields will constitute a separate row.
The provided file has 4 unique combinations of 'Day', 'web_site_key', 'web_site_name', and 'Web_site_source', as each line has a unique 'web_site_key' and 'web_site_name'. Consequently, Marketing Cloud Intelligence will store 4 rows, one for each unique combination.

NEW QUESTION # 60
......

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