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Salesforce Marketing-Cloud-Intelligence Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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	Data Model: In this domain, marketing professionals will explore data model entities, their relationships, and attributes within Marketing Cloud Intelligence.
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	Design Feasibility: This area evaluates the ability to identify valid and invalid solutions from solution design diagrams, ensuring effective and scalable platform designs.
Topic 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.
Topic 8	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 9	QA Ability: This section focuses on common QA steps for various scenarios, enabling Salesforce marketing professionals to ensure data quality and platform performance.
Topic 10	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 11	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 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.
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 (Q50-Q55):

NEW QUESTION # 50
An implementation engineer is requested to integrate the following files:
File A:

File B:

The client would like to link the two files in order to view the two KPIS (Tasks Completed' and 'tasks Assignmed') alongside'Employee Name' and/or 'Squard'.
A Parent-Child configuration was set between the two.
Which two statements are correct?

A. Any one of the files can potentially be set as the Parent data stream
B. The two files cannot be Joined as they hold different measurements
C. The two files cannot be joined as they hold different dates
D. The two files were uploaded to a different Generic type
E. The join can be successful even if "empjd' isn't mapped and employee.name' is mapped to the same entity name in both data streams

Answer: A,E

Explanation:
In Marketing Cloud Intelligence, joining two files requires a common field to be mapped as the same entity. If
"employee_name" is consistently mapped across both data streams, it can serve as the basis for the join, regardless of whether "employee_id" is mapped. The choice of which file serves as the Parent stream depends on the use case and the desired reporting structure, but technically, either could serve as the Parent.

NEW QUESTION # 51
Source 3:

Via the harmonization Center, the Client has created Patterns and applied a classification rule using source 2.
While performing QA, you have spotted that the final value of clicks for Product Group Ais 10, where it should've been i5.

How can an implementation engineer fix this discrepancy?

A. Upload both source 1 and 3 to the same data stream type in order to be able to generate Patterns from them.
B. Toggle the 'Structure Compliant' OFF.
C. Leave the "Case Sensitive" checkbox in the data classification unchecked
D. Uncheck the "Case Sensitive" checkbox in the data classification

Answer: D

Explanation:
Case Sensitivity Issue:
The discrepancy in the "Clicks" value for Product Group A (10 instead of 15) likely arises from a mismatch caused by case sensitivity in the classification rules. If some data entries use different capitalization (e.g., "Product Group A" vs. "product group a"), the system might treat them as distinct entries, leading to incorrect aggregations.
Solution:
By unchecking the "Case Sensitive" checkbox, the harmonization process will treat entries with different capitalization as the same value. This ensures consistent classification and resolves discrepancies in aggregated metrics like "Clicks."

NEW QUESTION # 52
Your client would like to create a new harmonization field - Exam Topic.
The below table represents the harmonization logic from each source.

As can be seen from the table, there are in fact two fields that hold a certain connection: Exam ID and Exam Topic. The connection indicates that where an Exam ID is found - a single Exam Topic value is associated with it.
The client has a requirement to be able to view measurements from all data sources sliced by Exam Topic values, as seen in the following example:

The client suggested to create, without any mapping manipulations, several patterns via the harmonization center that will generate two Harmonized Dimensions:
Exam ID
Exam Topic
Given the above information, which statement is correct regarding the ability to implement this request with the above suggestion?

A. The above Patterns setup will not work for this use case.
B. The solution will work - the client will be able to view Exam Topic with Email Sends.
C. The Harmonized field for Exam ID is redundant. One Harmonized dimension for Exam Topic is enough for a sustainable and working solution
D. Only if 5 different Patterns are created, from 5 different fields - the solution will work.

Answer: C

Explanation:
If the harmonization logic consistently associates a single Exam Topic with each Exam ID across all data sources, then creating two harmonized dimensions may be unnecessary. One harmonized dimension for Exam Topic would suffice because it inherently carries the Exam ID's uniqueness within it. The harmonized dimension for Exam Topic would allow the client to slice the data by Exam Topic values, fulfilling the requirement.

NEW QUESTION # 53
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. Populate the logic within a custom measurement. Set Aggregation to AVG.
C. Populate the logic within a custom measurement. Set Aggregation to SUM.
D. Unmap it, as Datorama will calculate it automatically.

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 # 54
An implementation engineer is requested to extract the first three-letter segment of the Campaign Name values.
For example:
Campaign Name: AFD@Mulop-1290
Desired outcome: AFD
Other examples:

Which formula will return the desired values?

A. EXTRACT(EXTRACT(csv['campaign_name]]/@',1),-,0)
B. EXTRACT(csv[campaign_name!;@',1)
C. LEFT(EXTRACT(csy['campaign_name]],~',0),3)
D. LEFT(EXTRACT(csv[campaign_name'}/-',1),3)
E. EXTRACT(csv[campaign_name'],-,0)

Answer: B

Explanation:
The EXTRACT function is used to split a string based on a delimiter and return the segment at the specified position. The campaign names are structured with the segment of interest followed by an '@' sign. Therefore, the formula needs to extract the segment before the '@'.
The correct formula is: EXTRACT(csv['campaign_name']; '@', 1). This will take the 'campaign_name' field, split it at the '@' sign, and return the first segment (position 1), which is the three-letter code that is required. The other options are incorrect because they do not properly specify the delimiter and the segment position in the way needed to achieve the desired outcome.

NEW QUESTION # 55
......

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