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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

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 3

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 4

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 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

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

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

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

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

Topic 10

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 11

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 12

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 13

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.

Salesforce Marketing Cloud Intelligence Accredited Professional Exam Sample Questions (Q40-Q45):

NEW QUESTION # 40
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 calculated dimension was created with the formula: EXTRACT([Creative_Namel, #1)
B. An EXTRACT formula (for Color) was written and mapped to a Media Buy custom attribute.
C. An EXTRACT formula (for Color) was written and mapped to a Creative custom attribute.
D. A Harmonized dimension was created via a pattern over the Creative Name.

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 # 41
An implementation engineer has been provided with the below dataset:

*Note: CPC = Cost per Click
Formula: Cost / Clicks
Which action should an engineer take to successfully integrate CPC?

A. Populate the logic within a custom measurement. No need to change Aggregation.
B. Unmap it, as Datorama will calculate it automatically.
C. Populate the logic within a custom measurement. Set Aggregation to AVG.
D. Populate the logic within a custom measurement. Set Aggregation to SUM.

Answer: A

Explanation:
CPC (Cost per Click) is a calculated metric that should be created using a custom measurement based on the formula provided (Cost / Clicks). This calculation does not require a change in the aggregation setting because it is derived from other base metrics that are already aggregated appropriately. In Salesforce Marketing Cloud Intelligence, custom measurements are used to create new metrics from existing data points, and the system will use the underlying data's aggregation to perform the calculation. Reference: Salesforce Marketing Cloud Intelligence documentation on creating custom measurements and calculated metrics.

NEW QUESTION # 42
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 5
B. Option 2
C. Option 4
D. Option 3
E. Option 1

Answer: A,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 # 43
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 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.
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 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
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.

Answer: C

Explanation:
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.

NEW QUESTION # 44
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 January (entire month). What is the number of opportunities in the Interest stage?

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

Answer: D

Explanation:
Based on the Opportunity file, the Opportunity Stage of 'Interest' occurs 3 times across unique Opportunity Keys. Since the pivot table is filtered to present the entire month of January and the Opportunity Stage 'Interest' is listed three times with different Opportunity Keys, the count of opportunities in the 'Interest' stage would be 3.

NEW QUESTION # 45
......

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