EXAM MARKETING-CLOUD-INTELLIGENCE SCORE, MARKETING-CLOUD-INTELLIGENCE VALID EXAM PREP

Exam Marketing-Cloud-Intelligence Score, Marketing-Cloud-Intelligence Valid Exam Prep

Exam Marketing-Cloud-Intelligence Score, Marketing-Cloud-Intelligence Valid Exam Prep

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Salesforce Marketing-Cloud-Intelligence Exam Syllabus Topics:

TopicDetails
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
  • 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 3
  • 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 4
  • 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 5
  • Calculated Dimensions & Measurements: This section measures skills in using calculated objects, recognizing aggregation types, and employing these tools for tailored marketing analytics.
Topic 6
  • 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 7
  • 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 8
  • 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 9
  • Design Feasibility: This area evaluates the ability to identify valid and invalid solutions from solution design diagrams, ensuring effective and scalable platform designs.
Topic 10
  • 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 11
  • 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 12
  • Mapping: Marketing professionals will focus on Marketing Cloud Intelligence ingestion capabilities, assessing knowledge of data mapping processes and outcomes critical to efficient data organization.

Salesforce Marketing Cloud Intelligence Accredited Professional Exam Sample Questions (Q11-Q16):

NEW QUESTION # 11
A client's data consists of three data sources - Facebook Ads, LinkedIn Ads and Google Campaign Manager.
Notes:
* The client is planning on adding an additional 100 Facebook Ads data streams and 50 more LinkedIn Ads data streams.
* The final volume of data in the workspace will be 5M rows
* Each data source has a naming convention and it can be assumed that any additional profile (i.e. Data Stream) from one of these sources will follow the same naming convention.
The client provided the following sample files:
Facebook Ads:


The client would like to create a new harmonization field named "Market," which will only be coming from Facebook Ads and LinkedIn Ads. The logic for
"Market" is the following:
IF Media Buy Type is equal to "TypeB" or "TypeC" or "TypeD"
Return 'Europe'
ELSE
Return 'Rest Of The World'
In order to create the harmonization field Market, the client considers using either Mapping Formula, Calculated Dimension, VLOOKUP or Patterns.
Considering maintenance and scalability, which option is recommended?

  • A. Patterns
  • B. Calculated Dimension
  • C. vLookuP
  • D. Mapping Formulas

Answer: A

Explanation:
Patterns are the best approach in this scenario because:
Scalability: Patterns are highly scalable and can easily handle the addition of 100 more Facebook Ads and 50 more LinkedIn Ads streams. You can define pattern-matching rules that automatically apply to new data streams based on the naming conventions.
Flexibility and Maintenance: Patterns allow you to maintain and adjust logic easily. Since the logic for determining "Market" is based on a defined naming convention (e.g., Media Buy Type), Patterns can handle these rules effectively without requiring manual updates or static tables.
Efficient Harmonization: Patterns automatically classify data based on defined rules, reducing the need for ongoing manual maintenance compared to approaches like VLOOKUP or Mapping Formulas, which might require frequent updates as data changes.
Why not other options?
Mapping Formulas: While Mapping Formulas work well for static mappings, they are not as scalable or maintainable when the dataset grows or changes frequently.
Calculated Dimension: This option is valid for simple logic but is less maintainable for large-scale datasets, especially when new data streams are added.
VLOOKUP: This method is manual and not scalable. It would require you to update lookup tables for each new data stream, which is inefficient given the expected growth of the data.


NEW QUESTION # 12
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: D

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 # 13
Client has provided sample flies of their data from the following data sources:
Google Campaign Manager

Below are the requirements from the client and additional information:
* The sources are linked to each other by shared Media Buy names.
* In addition-to the mutual Media Buys, the sources contain campaign and site values. However, the client would like to see the campaign/site values coming from Google CM and not from Google DV360.
* The source of truth for cost is Google DV360.
As a first step, a Parent-Child relationship was created between the two files, and the following mapping was performed, within both data streams:

Please note:
* All other measurements were mapped as well to the appropriate fields.
* No other mapping manipulations or formulas were implemented.
How many records will the merged table hold?

  • A. 0
  • B. Depends on the Data Updates Permissions
  • C. 1
  • D. 2

Answer: C

Explanation:
Since the data sources are linked by shared Media Buy names and all other measurements are mapped to appropriate fields without additional manipulations, each unique Media Buy Name from Google DV360 will pair with its corresponding Media Buy Name in Google Campaign Manager. The number of records in the merged table will equal the number of unique Media Buy Names in Google DV360, provided there is a matching name in Google Campaign Manager. The sample shows 4 unique Media Buy Names in Google DV360, thus resulting in 4 records.


NEW QUESTION # 14
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: D

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 # 15
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. Calculated Dimensions
  • B. Patterns
  • C. Mapping formula
  • D. Data Fusion
  • E. Vlookup 0

Answer: A,B,C

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 # 16
......

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