PASS GUARANTEED 2025 UPDATED MARKETING-CLOUD-INTELLIGENCE: BRAIN DUMP MARKETING CLOUD INTELLIGENCE ACCREDITED PROFESSIONAL EXAM FREE

Pass Guaranteed 2025 Updated Marketing-Cloud-Intelligence: Brain Dump Marketing Cloud Intelligence Accredited Professional Exam Free

Pass Guaranteed 2025 Updated Marketing-Cloud-Intelligence: Brain Dump Marketing Cloud Intelligence Accredited Professional Exam Free

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Salesforce Marketing-Cloud-Intelligence Exam Syllabus Topics:

TopicDetails
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
  • 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
  • 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
  • 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 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
  • 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 7
  • Calculated Dimensions & Measurements: This section measures skills in using calculated objects, recognizing aggregation types, and employing these tools for tailored marketing analytics.
Topic 8
  • Data Model: In this domain, marketing professionals will explore data model entities, their relationships, and attributes within Marketing Cloud Intelligence.

Salesforce Marketing Cloud Intelligence Accredited Professional Exam Sample Questions (Q17-Q22):

NEW QUESTION # 17
An implementation engineer has been asked to perform QA for a standard file ingestion, done by the client.
The source file that was ingested can be seen below:

The number of rows added to this data stream is 3. What could have led to this discrepancy?

  • A. All fields are mapped except for the Creative Name
  • B. All fields are mapped except for the Media Buy Key.
  • C. All fields are mapped except for the Media Buy Name.
  • D. All fields are mapped except for the Campaign Key

Answer: D

Explanation:
The source file shows data related to media buys, including a 'Media Buy Key', 'Media Buy Name', 'Campaign Key', and 'Site Key', among other fields. If only three rows were added, and the discrepancy is due to a missing field, it's likely that 'Campaign Key' is the field not mapped, because it is crucial for linking related records in the data stream. Without the 'Campaign Key', the system cannot associate the media buy data with specific campaigns, leading to a potential loss of data rows during ingestion.


NEW QUESTION # 18
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" - Main Generic Entity Attribute
"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 Jan 11th. What is the number of 'opportunities in the Confirmed Interest stage?

  • A. 0
  • B. 1
  • C. 2
  • D. 3

Answer: D

Explanation:
Based on the data provided and the date filter set to January 11th, there are no records for 'Confirmed Interest' on that specific date in the Opportunity file. Thus, the number of opportunities in the 'Confirmed Interest' stage for January 11th would be zero (0). In Salesforce Marketing Cloud Intelligence, when creating pivot tables, the data is aggregated based on the selected filters. If no records meet the filter criteria, the result for that category would be zero. The answer is supported by best practices in data analysis and reporting within Salesforce Marketing Cloud Intelligence, where date filters are applied to segment and analyze data.


NEW QUESTION # 19
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
  • B. Confirmed Interest & Registered
  • C. Confirmed interest
  • D. Interest & Registered

Answer: D

Explanation:
Filtering the pivot table on January 7th-11th, we see that the Opportunity Key 123AA01 appears on January
6th with the stage 'Interest' and then on January 10th with the stage 'Registered'. Even though the 'Interest' stage is not within the filtered dates, it is the initial stage of the opportunity, so it should be counted along with the 'Registered' stage which falls within the filter range.


NEW QUESTION # 20
A client has provided you with sample files of their data from the following data sources:
1.Google Analytics
2.Salesforce Marketing Cloud
The link between these sources is on the following two fields:
Message Send Key
A portion of: web_site_source_key
Below is the logic the client would like to have implemented in Datorama:
For 'web site medium' values containing the word "email" (in all of its forms), the section after the "_" delimiter in 'web_site_source_key' is a 4 digit number, which matches the 'Message Send Key' values from the Salesforce Marketing Cloud file. Possible examples of this can be seen in the following table:
Google Analytics:

Salesforce Marketing Cloud:

The client's objective is to visualize the mutual key values alongside measurements from both files in a table.

In order to achieve this, what steps should be taken?

  • A. Upload the two files and create a Parent-Child relationship between them. The Override Media Buy Hierarchy checkbox is checked in Google Analytics.
  • B. Create a Web Analytics Site custom attribute and populate it with the extraction logic. Create a Data Fusion between the newly created attribute and the Message Send Key.
  • C. Within both files, map the desired value to Custom Classification Key as follows Salesforce Marketing Cloud: map entire Message Key to Custom Classification Key.
    Google Analytics: map the extraction logic to Custom Classification Key.
  • D. Create a Web Analytics Site Source custom attribute and populate it with the extraction logic. Create a Data Fusion between the newly created attribute and the Message Send Key.

Answer: C

Explanation:
To create a linkage between Google Analytics and Salesforce Marketing Cloud data based on the "Message Send Key" and a portion of the "web_site_source_key," both values need to be harmonized into a common key. This is done by mapping the full Message Send Key from Salesforce Marketing Cloud and the extracted part of the web_site_source_key from Google Analytics to the same Custom Classification Key. This mapping will create a common identifier that can be used to combine the data from both sources for analysis and visualization.


NEW QUESTION # 21
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. 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.
  • B. 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
  • C. 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
  • D. 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.

Answer: B,C

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 # 22
......

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