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Salesforce Marketing-Cloud-Intelligence Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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	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 5	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 6	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 7	Calculated Dimensions & Measurements: This section measures skills in using calculated objects, recognizing aggregation types, and employing these tools for tailored marketing analytics.

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Salesforce Marketing Cloud Intelligence Accredited Professional Exam Sample Questions (Q23-Q28):

NEW QUESTION # 23
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. 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. 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. 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: 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 # 24
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 Media Buy Name.
B. All fields are mapped except for the Campaign Key
C. All fields are mapped except for the Creative Name
D. All fields are mapped except for the Media Buy Key.

Answer: B

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 # 25
The following file was uploaded into Marketing Cloud Intelligence as a generic dataset type:

The mapping is as follows:
Day - Day
Web_site_source - Main Generic Entity Attribute 01
Page Views - Generic Metric 1
*Note that 'web_site_key' and 'web_site_name' are NOT mapped.
How many rows will be stored in Marketing Cloud Intelligence after the above file is ingested?

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

Answer: A

Explanation:
In Marketing Cloud Intelligence, when a file is uploaded as a generic dataset type and mapped accordingly, each unique combination of the mapped fields results in a separate row in the database. The file in question has been mapped with 'Day' to 'Day', 'Web_site_source' to 'Main Generic Entity Attribute 01', and 'Page Views' to 'Generic Metric 1'. The 'web_site_key' and 'web_site_name' are not mapped and thus, won't affect the row count.
Since there are 4 unique combinations of the mapped fields in the uploaded file (each day and source combination is unique), Marketing Cloud Intelligence will store 4 rows after ingestion, corresponding to each unique combination of 'Day' and 'Web_site_source'.

NEW QUESTION # 26
The following file was uploaded into Marketing Cloud Intelligence as a generic dataset type:

The mapping is as follows:
Day - Day
Web_site_source - Main Generic Entity Attribute 01
Page Views - Generic Metric 1
*Note that 'web_site_key' and 'web_site_name' are NOT mapped.
How many rows will be stored in Marketing Cloud Intelligence after the above file is ingested?

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

Answer: A

NEW QUESTION # 27
An implementation engineer has been asked by a client for assistance with the following problem:
The below dataset was ingested:

However, when performing QA and querying a pivot table with Campaign Category and Clicks, the value for Type' is 4.
What could be the reason for this discrepancy?

A. The measurement 'Clicks' is set as a percentage.
B. The aggregation function is set as LIFETIME
C. The aggregation function is set as AVG
D. A mapping formula was populated, indicating not to bring Type! values.

Answer: C

Explanation:
The discrepancy of 'Clicks' being reported as 4 for 'Type1' when the sum of clicks in the dataset for 'Type1' is 8 (2 on 02/02/2021 and 6 on 03/02/2021) suggests that the aggregation function used in the pivot table is set to average (AVG) rather than sum. Salesforce Marketing Cloud Intelligence allows setting different aggregation functions for metrics, and setting it to average would result in such a discrepancy when more than one entry for the same type exists. Reference: Salesforce Marketing Cloud Intelligence documentation on custom measurements and data aggregations explains how to set and understand different aggregation functions.

NEW QUESTION # 28
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