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Salesforce Marketing-Cloud-Intelligence Exam Syllabus Topics:

Topic	Details
Topic 1	Calculated Dimensions & Measurements: This section measures skills in using calculated objects, recognizing aggregation types, and employing these tools for tailored marketing analytics.
Topic 2	Data Model: In this domain, marketing professionals will explore data model entities, their relationships, and attributes within Marketing Cloud Intelligence.
Topic 3	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 4	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 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.
Topic 6	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 7	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 8	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 9	QA Ability: This section focuses on common QA steps for various scenarios, enabling Salesforce marketing professionals to ensure data quality and platform performance.

Salesforce Marketing Cloud Intelligence Accredited Professional Exam Sample Questions (Q59-Q64):

NEW QUESTION # 59
A client Ingested the following We into Marketing Cloud Intelligence:

The mapping of the above file can be seen below:
Date - Day
Media Buy Key - Media Buy Key
Campaign Name - Campaign Name
Campaign Group -. Campaign Custom Attribute 01
Clicks -> Clicks
Media Cost -> Media Cost
Campaign Planned Clicks -> Delivery Custom Metric 01
The client would like to have a "Campaign Planned Clicks" measurement.
This measurement should return the "Campaign Planned Clicks" value per Campaign, for example:
For Campaign Name 'Campaign AAA", the "Campaign Planned Clicks" should be 2000, rather than 6000 (the total sum by the number of Media Buy keys).
In order to create this measurement, the client considered multiple approaches. Please review the different approaches and answer the following question:

Which two options will yield a false result:

A. Option 2
B. Option 4
C. Option 3
D. Option 5
E. Option 1

Answer: D,E

Explanation:
The goal is to obtain a "Campaign Planned Clicks" value per Campaign, not accumulated by Media Buy keys.
Option 1 (SUM aggregation function) would sum all the "Campaign Planned Clicks" across Media Buy keys which would not yield the unique value per Campaign. Similarly, Option 5 (AVG aggregation function at Campaign Key level) would incorrectly average the values. Both options do not provide a way to return a singular "Campaign Planned Clicks" value for each Campaign.

NEW QUESTION # 60
A client has provided you with sample files of their data from the following data sources:
1.Google Analytics
2.Salesforce Marketing Cloud
The link between these sources is on the following two fields:
Message Send Key
A portion of: web_site_source_key
Below is the logic the client would like to have implemented in Datorama:
For 'web site medium' values containing the word "email" (in all of its forms), the section after the "_" delimiter in 'web_site_source_key' is a 4 digit number, which matches the 'Message Send Key' values from the Salesforce Marketing Cloud file. Possible examples of this can be seen in the following table:
Google Analytics:

Salesforce Marketing Cloud:

The client's objective is to visualize the mutual key values alongside measurements from both files in a table.

In order to achieve this, what steps should be taken?

A. Within both files, map the desired value to Custom Classification Key as follows Salesforce Marketing Cloud: map entire Message Key to Custom Classification Key.
Google Analytics: map the extraction logic to Custom Classification Key.
B. Create a Web Analytics Site Source custom attribute and populate it with the extraction logic. Create a Data Fusion between the newly created attribute and the Message Send Key.
C. Upload the two files and create a Parent-Child relationship between them. The Override Media Buy Hierarchy checkbox is checked in Google Analytics.
D. Create a Web Analytics Site custom attribute and populate it with the extraction logic. Create a Data Fusion between the newly created attribute and the Message Send Key.

Answer: A

Explanation:
To create a linkage between Google Analytics and Salesforce Marketing Cloud data based on the "Message Send Key" and a portion of the "web_site_source_key," both values need to be harmonized into a common key. This is done by mapping the full Message Send Key from Salesforce Marketing Cloud and the extracted part of the web_site_source_key from Google Analytics to the same Custom Classification Key. This mapping will create a common identifier that can be used to combine the data from both sources for analysis and visualization.

NEW QUESTION # 61
A client Ingested the following We into Marketing Cloud Intelligence:

The mapping of the above file can be seen below:
Date - Day
Media Buy Key - Media Buy Key
Campaign Name - Campaign Name
Campaign Group -. Campaign Custom Attribute 01
Clicks -> Clicks
Media Cost -> Media Cost
Campaign Planned Clicks -> Delivery Custom Metric 01
The client would like to have a "Campaign Planned Clicks" measurement.
This measurement should return the "Campaign Planned Clicks" value per Campaign, for example:
For Campaign Name 'Campaign AAA", the "Campaign Planned Clicks" should be 2000, rather than 6000 (the total sum by the number of Media Buy keys).
In order to create this measurement, the client considered multiple approaches. Please review the different approaches and answer the following question:

Which two options will yield a false result:

A. Option 2
B. Option 4
C. Option 3
D. Option 5
E. Option 1

Answer: D,E

Explanation:
The goal is to obtain a "Campaign Planned Clicks" value per Campaign, not accumulated by Media Buy keys. Option 1 (SUM aggregation function) would sum all the "Campaign Planned Clicks" across Media Buy keys which would not yield the unique value per Campaign. Similarly, Option 5 (AVG aggregation function at Campaign Key level) would incorrectly average the values. Both options do not provide a way to return a singular "Campaign Planned Clicks" value for each Campaign.

NEW QUESTION # 62
Which three statements describe Overarching Entities? 03m 23s

A. When needed, these entities can act as a main entity, replacing the original one.
B. Some overarching entities hold a Many-to-Many relationship with the main entity, and others hold a One-to-Many relationship with it.
C. These are mappable dimensions that are present in each and every dataset type
D. The values of these entities are stored at the workspace level, rather than the data stream level
E. Once the data streams in which Custom Classification values were mapped are deleted, their data is deleted.

Answer: A,B,D

Explanation:
Overarching Entities in Salesforce Marketing Cloud Intelligence are designed to provide a high level of data organization that spans across multiple data streams. The key points about Overarching Entities are:
* B. Relationship Types: Overarching entities can have either a Many-to-Many or One-to-Many relationship with the main entity, which allows for flexible data modeling and relationship definitions based on the nature of the data and how it should be analyzed and reported.
* C. Acting as Main Entity: They can serve as a main entity in certain situations, enabling a shift in perspective for data analysis. This can be particularly useful when there is a need to view data from a different dimension that is more aligned with business requirements.
* E. Storage Level: The values of these entities are not tied to any single data stream but are maintained at a workspace level, ensuring that they can be applied consistently across different datasets, which is critical for maintaining data integrity and ensuring that classifications are applied uniformly.

NEW QUESTION # 63
What are two potential reasons for performance issues (when loading a dashboard) when using the CRM data stream type?

A. Pacing - daily rows are being created for every lead and opportunity keys
B. The data is stored at the workspace level.
C. When a data stream type ''CRM - Leads' is created, another complementary 'CRM - Opportunity' is created automatically.
D. No mappable measurements - all measurements are calculated

Answer: A,D

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.

NEW QUESTION # 64
......

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100% PASS LATEST SALESFORCE - MARKETING-CLOUD-INTELLIGENCE UPDATED DUMPS

100% Pass Latest Salesforce - Marketing-Cloud-Intelligence Updated Dumps