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Salesforce Marketing-Cloud-Intelligence Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	Design Feasibility: This area evaluates the ability to identify valid and invalid solutions from solution design diagrams, ensuring effective and scalable platform designs.
Topic 3	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 4	Data Model: In this domain, marketing professionals will explore data model entities, their relationships, and attributes within Marketing Cloud Intelligence.
Topic 5	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 6	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.
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	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
Client has provided sample flies of their data from the following data sources:
Google Campaign Manager

Below are the requirements from the client and additional information:
* The sources are linked to each other by shared Media Buy names.
* In addition-to the mutual Media Buys, the sources contain campaign and site values. However, the client would like to see the campaign/site values coming from Google CM and not from Google DV360.
* The source of truth for cost is Google DV360.
As a first step, a Parent-Child relationship was created between the two files, and the following mapping was performed, within both data streams:

Please note:
* All other measurements were mapped as well to the appropriate fields.
* No other mapping manipulations or formulas were implemented.
How many records will the merged table hold?

A. 0
B. Depends on the Data Updates Permissions
C. 1
D. 2

Answer: D

Explanation:
Since the data sources are linked by shared Media Buy names and all other measurements are mapped to appropriate fields without additional manipulations, each unique Media Buy Name from Google DV360 will pair with its corresponding Media Buy Name in Google Campaign Manager. The number of records in the merged table will equal the number of unique Media Buy Names in Google DV360, provided there is a matching name in Google Campaign Manager. The sample shows 4 unique Media Buy Names in Google DV360, thus resulting in 4 records.

NEW QUESTION # 60
Your client provided the following sources:
Source 1:

Source 2:

Source 3:

As can be seen, the Product values present in sources 2 and 3 are similar and can be linked with the first extraction from 'Media Buy Name' in source1 The end goal is to achieve a final view of Product Group alongside Clicks and Sign Ups, as described below:

Which two options will meet the client's requirement and enable the desired view?

A. Harmonization Center: Patterns from sources 1 and 3 generate harmonized dimension 'Product'. Data Classification rule, using source 2, is applied on top of the harmonized dimension
B. Custom Classification: 1
Source 1: Custom Classification key will be populated with the extraction of the Media Buy Name.
Source 2: 'Product' will be mapped to Custom Classification key and 'Product Group' to a Custom Classification level. Exam Timer Source 3: 'Product will be mapped to Custom Classification key. Came
C. Overarching Entities:
Source 1: custom classification key will be populated with the extraction of the Media Buy Name.
Source 2: 'Product' will be mapped to Product field and 'Product Group' to Product Name.
Source 3: 'Product' will be mapped to Product field.
D. Parent Child:
All sources will be uploaded to the same data stream type - Ads. The setup is the following:
Source 1: Media Buy Key -- Media Buy Key, extracted product value - Media Buy Attribute.
Source 2: Product - Media Buy Key, Product Group -- Media Buy Attribute.
Source 3: Product - Media Buy Key.

Answer: A,B

Explanation:
To achieve a final view of Product Group alongside Clicks and Sign Ups, we should use:
Option A:
* Custom Classification: By using a Custom Classification key populated with the extraction of the Media Buy Name in Source 1, we can then map 'Product' in Source 2 to this key and 'Product Group' to a Custom Classification level. This will allow for grouping and analysis by Product Group, as well as enable the desired view to be created.
Option D:
* Harmonization Center: With patterns from Sources 1 and 3, we can create a harmonized dimension
'Product'. Then, by applying a Data Classification rule using Source 2, we can enhance the harmonized dimension. This allows us to align 'Product Group' with the 'Product' from Sources 1 and 3, facilitating an integrated view of Clicks and Sign Ups by Product Group.

NEW QUESTION # 61
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. Only if 5 different Patterns are created, from 5 different fields - the solution will work.
B. The Harmonized field for Exam ID is redundant. One Harmonized dimension for Exam Topic is enough for a sustainable and working solution
C. The above Patterns setup will not work for this use case.
D. The solution will work - the client will be able to view Exam Topic with Email Sends.

Answer: B

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 # 62
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. LEFT(EXTRACT(csv[campaign_name'}/-',1),3)
C. EXTRACT(csv[campaign_name'],-,0)
D. LEFT(EXTRACT(csy['campaign_name]],~',0),3)
E. EXTRACT(csv[campaign_name!;@',1)

Answer: E

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 # 63
A client would like to integrate the following two sources:
Google Campaign Manager:

IAS:

After configuring a Parent-Child relationship between the files, which query should an implementation engineer run in order to QA the setup?

A. Media Buy Type, Analyzed Impressions
B. Media Buy Type, Media Buy Name, Impressions, Analyzed Impressions
C. Media Buy Name, Impressions
D. Creative Name, Impressions, Analyzed Impressions

Answer: B

Explanation:
To QA the Parent-Child relationship setup between Google Campaign Manager and IAS data sources, it is essential to query fields that are common to both sources and that are relevant to the relationship. 'Media Buy Type' and 'Media Buy Name' are common identifiers between the two datasets. 'Impressions' from the Google Campaign Manager and 'Analyzed Impressions' from the IAS data are the metrics that should be compared to ensure they match or correlate as expected due to the Parent-Child relationship. The QA process involves checking that the data is correctly aligned and that the metrics from the parent source (Google Campaign Manager) are properly related to the metrics from the child source (IAS). References: Salesforce Marketing Cloud Intelligence documentation on data integration, Parent-Child relationships, and QA procedures for data setup.

NEW QUESTION # 64
......

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Updated and User Friendly Pass4training Marketing-Cloud-Intelligence Exam PDF Questions File

100% Pass Useful Salesforce - Marketing-Cloud-Intelligence Exams Torrent

Salesforce Marketing-Cloud-Intelligence Exam Syllabus Topics:

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