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Salesforce Marketing-Cloud-Intelligence Exam Syllabus Topics:

Topic	Details
Topic 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.
Topic 2	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 3	QA Ability: This section focuses on common QA steps for various scenarios, enabling Salesforce marketing professionals to ensure data quality and platform performance.
Topic 4	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 5	Design Feasibility: This area evaluates the ability to identify valid and invalid solutions from solution design diagrams, ensuring effective and scalable platform designs.
Topic 6	Data Model: In this domain, marketing professionals will explore data model entities, their relationships, and attributes within Marketing Cloud Intelligence.
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	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 9	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 10	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 11	Calculated Dimensions & Measurements: This section measures skills in using calculated objects, recognizing aggregation types, and employing these tools for tailored marketing analytics.
Topic 12	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.

Salesforce Marketing Cloud Intelligence Accredited Professional Exam Sample Questions (Q31-Q36):

NEW QUESTION # 31
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. Interest & Registered
B. Interest
C. Confirmed Interest & Registered
D. Registered
E. Confirmed Interest

Answer: A

Explanation:
Analyzing the Opportunity file with a filter set from January 7th to 11th, Opportunity Key '123AA01' appears under 'Interest' on January 6th and 8th, and under 'Registered' on January 10th. Therefore, during the specified date range, Opportunity Key '123AA01' is associated with both 'Interest' and 'Registered' stages. Salesforce Marketing Cloud Intelligence provides the capability to map and track opportunity stages over time, allowing for historical stage tracking and reporting. This answer aligns with the ability to use pivot tables to filter and display data by specific attributes and timeframes, as outlined in the Salesforce Marketing Cloud Intelligence documentation.

NEW QUESTION # 32
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.
How should the "Override Media Buy Hierarchies" checkbox be set in order to meet the client's requirements?

A. It should be checked in Data Stream A
B. It should not be checked in any of the three Data Streams.
C. It should be checked in Data Stream B
D. It should be checked in Data Stream C

Answer: D

Explanation:
If Data Stream C is the source of truth, the "Override Media Buy Hierarchies" checkbox should be checked for Data Stream C.
This means that the hierarchy defined within Data Stream C will take precedence over any other media buy hierarchies present in Data Streams A or B. By doing so, it enforces that the hierarchy from the source of truth (Data Stream C) is used throughout the dataset, maintaining the integrity of the hierarchical relationships as defined by the most reliable data source.

NEW QUESTION # 33
What areunstable measurements?

A. Measurements for which Aggregation Settings are set as 'Not Auto' and Granularity is set as 'Not Empty'.
B. Measurements that are set with the LIFETIME aggregation function
C. Measurements for which Aggregation Settings are set as 'Not Auto' and Granularity is set as 'None'.
D. Measurements for which Aggregation Settings are set as 'Auto' and Granularity is set as 'None'.

Answer: C

Explanation:
Unstable measurements refer to metrics that are not aggregated in a standard manner across different grains of data, which can result in inconsistent or unpredictable results when reporting across different dimensions or time frames.
* Option C describes a scenario where measurements have manual (Not Auto) aggregation settings, meaning they do not automatically adjust to theaggregation level of the report. Combined with a Granularity setting of 'None', this can lead to instability because the metric isn't bound to a specific granularity, which can cause data inconsistencies or misinterpretations when analyzed at varying levels of detail.

NEW QUESTION # 34
An implementation engineer is requested to apply the following logic:

To apply the above logic, the engineer used only the Harmonization Center, without any mapping manipulations. What is the minimum amount of Patterns creating both 'Platform' and 'Line of Business'?"

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

Answer: D

Explanation:
To create both 'Platform' and 'Line of Business' fields using Patterns in the Harmonization Center without mapping manipulations, the engineer would need to create separate patterns for each data source mentioned.
According to the provided images:
* One pattern for LinkedIn Ads, to extract the 'Campaign Name' at position 4 for the Platform and 'Media Buy Name' at position 7 for Line of Business.
* One pattern for AdRoll, to extract 'Media Buy Name' at position 3 for Platform and at position 2 for Line of Business.
* One pattern for Google Analytics, which seems not required for the Platform but could apply if the Line of Business extraction is necessary, although it states N/A.
Hence, a minimum of 3 patterns would be necessary to create the fields required.

NEW QUESTION # 35
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. An EXTRACT formula (for Color) was written and mapped to a Media Buy custom attribute.
B. A Harmonized dimension was created via a pattern over the Creative Name.
C. An EXTRACT formula (for Color) was written and mapped to a Creative custom attribute.
D. A calculated dimension was created with the formula: EXTRACT([Creative_Namel, #1)

Answer: C

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 # 36
......

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Salesforce Marketing-Cloud-Intelligence Test Objectives Pdf: Marketing Cloud Intelligence Accredited Professional Exam - PremiumVCEDump Official Pass Certify

Salesforce Marketing-Cloud-Intelligence Dumps PDF Format Is Best For Instant Preparation

Salesforce Marketing-Cloud-Intelligence Exam Syllabus Topics:

Salesforce Marketing Cloud Intelligence Accredited Professional Exam Sample Questions (Q31-Q36):

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