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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	Data Model: In this domain, marketing professionals will explore data model entities, their relationships, and attributes within Marketing Cloud Intelligence.
Topic 4	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 5	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 6	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 7	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 8	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 9	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 10	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 11	QA Ability: This section focuses on common QA steps for various scenarios, enabling Salesforce marketing professionals to ensure data quality and platform performance.
Topic 12	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 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 (Q16-Q21):

NEW QUESTION # 16
A client's data consists of three data sources - Facebook Ads, LinkedIn Ads and Google Campaign Manager.
Notes:
* The client is planning on adding an additional 100 Facebook Ads data streams and 50 more LinkedIn Ads data streams.
* The final volume of data in the workspace will be 5M rows
* Each data source has a naming convention and it can be assumed that any additional profile (i.e. Data Stream) from one of these sources will follow the same naming convention.
The client provided the following sample files:
Facebook Ads:

The client would like to create a new harmonization field named "Market," which will only be coming from Facebook Ads and LinkedIn Ads. The logic for
"Market" is the following:
IF Media Buy Type is equal to "TypeB" or "TypeC" or "TypeD"
Return 'Europe'
ELSE
Return 'Rest Of The World'
In order to create the harmonization field Market, the client considers using either Mapping Formula, Calculated Dimension, VLOOKUP or Patterns.
Considering maintenance and scalability, which option is recommended?

A. Mapping Formulas
B. Calculated Dimension
C. vLookuP
D. Patterns

Answer: D

Explanation:
Patterns are the best approach in this scenario because:
Scalability: Patterns are highly scalable and can easily handle the addition of 100 more Facebook Ads and 50 more LinkedIn Ads streams. You can define pattern-matching rules that automatically apply to new data streams based on the naming conventions.
Flexibility and Maintenance: Patterns allow you to maintain and adjust logic easily. Since the logic for determining "Market" is based on a defined naming convention (e.g., Media Buy Type), Patterns can handle these rules effectively without requiring manual updates or static tables.
Efficient Harmonization: Patterns automatically classify data based on defined rules, reducing the need for ongoing manual maintenance compared to approaches like VLOOKUP or Mapping Formulas, which might require frequent updates as data changes.
Why not other options?
Mapping Formulas: While Mapping Formulas work well for static mappings, they are not as scalable or maintainable when the dataset grows or changes frequently.
Calculated Dimension: This option is valid for simple logic but is less maintainable for large-scale datasets, especially when new data streams are added.
VLOOKUP: This method is manual and not scalable. It would require you to update lookup tables for each new data stream, which is inefficient given the expected growth of the data.

NEW QUESTION # 17
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. 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.
B. 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.
C. 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
D. 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

Answer: C,D

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 # 18
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:

Which harmonization feature should an Implementation engineer use to meet the client's requirement?

A. Parent Chile
B. Custom Classification
C. Transformers
D. Fusion
E. Calculated dimensions

Answer: B

Explanation:
To meet the client's requirement of slicing measurements by 'Exam Topic' values, an Implementation Engineer should use Custom Classification. This feature allows different Exam IDs to be classified into their respective Exam Topics, ensuring that data from all sources can be accurately harmonized and analyzed based on these topics.

NEW QUESTION # 19
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 1
B. Option 5
C. Option 3
D. Option 2
E. Option 4

Answer: A,B

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 # 20
A client wants to integrate their data within Marketing Cloud Intelligence to optimize their marketing insights and cross-channel marketing activityanalysis. Below are details regarding the different data sources and the number of data streams required for each source.

What three advantages are gained when using Patterns & Data Classification as the harmonization method for creating the Objective field?

A. Performance (Performance when loading a dashboard page)
B. Ease of Maintenance
C. Processing (processing time when loading relevant data streams)
D. Scalability
E. Use of code

Answer: A,B,D

Explanation:
Patterns & Data Classification in Marketing Cloud Intelligence offer several advantages. These include:
* Ease of Maintenance (A):Patterns allow for the standardization of data harmonization processes. Once set up, they can be easily maintained and adjusted as needed, without having to manipulate each data stream individually.
* Performance (B):By using patterns, data is classified and standardized at ingestion, which can improve the performance of dashboard page loading because the system does not need to perform complex, on-the-fly calculations or transformations.
* Scalability (D):Patterns can be applied across multiple data streams consistently, allowing them to scale with the data. This means that as the amount of data grows or as new data sources are added, the same patterns can be reused, ensuring that the data remains harmonized.

NEW QUESTION # 21
......

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