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Marketing-Cloud-Intelligence Prüfungsunterlagen & Marketing-Cloud-Intelligence Originale Fragen

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Salesforce Marketing-Cloud-Intelligence Prüfungsplan:

Thema
Einzelheiten

Thema 1

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.

Thema 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.

Thema 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.

Thema 4

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

Thema 5

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

Thema 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.

Thema 7

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.

Thema 8

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.

Thema 9

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

Thema 10

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.

Thema 11

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

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Salesforce Marketing Cloud Intelligence Accredited Professional Exam Marketing-Cloud-Intelligence Prüfungsfragen mit Lösungen (Q38-Q43):

38. Frage
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 'opportunities in the Confirmed Interest stage?

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

Antwort: D

Begründung:
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.

39. Frage
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)

Antwort: B

Begründung:
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.

40. Frage
An Implementation engineer is requested to create anew harmonization field 'Offer'and apply the following logic:

The implementation engineer to use the Harmonization Center. Which of the below actions can help implement the new dimension 'Offer?

A. Two separate patterns (filtered by Linkedin or AdRoll sources)
Within Google Analytics' mapping: A formula that reflects the logic above will be populated within a Campaign custom attribute.
Another pattern to be created for the newly campaign attribute (filtered by Google Analytics source).
A total of 3 patterns
B. Two separate patterns (filtered by Linkedin or AdRoll sources).
Another single pattern for Campaign Name (filtered by Google Analytics source).
A total of 3 patterns.
C. Two separate patterns (filtered by Linkedln or AdRoll sources).Another single pattern for Web Analytics Site Source (filtered by Google Analytics source), extracting all three positions A total of 3 patterns.
D. Two separate patterns (filtered by Linkedin or AdRoll sources)
Within Google Analytics' mapping A formula that reflects the logic above will be populated within a Web Analytics Site custom attribute Another pattern to be created for the newly Web Analytics Site custom attribute (filtered by Google Analytics source).
A total of 3 patterns.

Antwort: A

Begründung:
To implement the new harmonization field 'Offer', the implementation engineer would create two separate harmonization patterns for LinkedIn and AdRoll sources, extracting the 'Campaign Name' using the specified delimiter and position. Then, within Google Analytics' mapping, a custom attribute for the 'Campaign' would be created to apply the formula logic based on the source. This allows for the harmonization of campaign data across different platforms, ensuring consistency in the reporting and analysis within Marketing Cloud Intelligence. The total patterns required would be three, one for each data source involved.

41. Frage
Your client is interested in ingesting the below file:

The client decided to upload the file to a new generic data stream type and map 'Date' to 'Day' and 'Number of Topics' to a generic custom metric.
In regards to the fields 'Meeting Code' and 'Meeting Name', your client is debating several options.
Which two options would you recommend in order to avoid data loss?

A. 'Meeting Code' will be mapped to 'Main Generic Entity Attribute 1'.
'Meeting Name' will be mapped to 'Main Generic Entity Attribute 2'.
B. 'Meeting Code' will be mapped to 'Main Generic Entity Key'.
'Meeting Name' will be mapped to 'Main Generic Entity custom attribute'.
C. Concatenation of both 'Meeting Code' and 'Meeting Name' will be mapped to 'Main Generic Entity Key'.
'Meeting Code' will be mapped to 'Main Generic Entity Attribute 1'.
'Meeting Name' will be mapped to 'Main Generic Entity Attribute 2'.
D. 'Meeting Code' will be mapped to 'Main Generic Entity Key'.
'Meeting Name' will be mapped to 'Generic Entity 2 Key'.
E. 'Meeting Code' will be mapped to 'Main Generic Entity custom attribute'.
'Meeting Name' will be mapped to 'Generic Entity Key'

Antwort: B,C

Begründung:
To avoid data loss and ensure each meeting is uniquely identified and its details are preserved, two mappings are recommended:
Option A:
* 'Meeting Code' should be mapped to the 'Main Generic Entity Key' to uniquely identify each meeting.
* 'Meeting Name' should be mapped to a 'Main Generic Entity custom attribute' to store additional information about the meeting.
Option E:
* Concatenation of 'Meeting Code' and 'Meeting Name' should be mapped to 'Main Generic Entity Key'.
This ensures a unique identifier for each meeting is created combining both pieces of information, preventing any mix-ups between meetings with similar codes or names.
* Additionally, mapping 'Meeting Code' and 'Meeting Name' to their respective 'Main Generic Entity Attribute' fields will allow for more detailed filtering and reporting capabilities within Marketing Cloud Intelligence.

42. Frage
A client wants to integrate their data within Marketing Cloud Intelligence to optimize their marketing insights and cross-channel marketing activity analysis. 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. Performance (Performance when loading a dashboard page)
B. Ease of Maintenance
C. Processing (processing time when loading relevant data streams)
D. Scalability
E. Use of code

Antwort: A,B,D

Begründung:
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.

43. Frage
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Marketing-Cloud-Intelligence Originale Fragen: https://www.zertpruefung.ch/Marketing-Cloud-Intelligence_exam.html

Liam Stewart Liam Stewart

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