Salesforce AP-215 Prüfung Übungen und Antworten

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Salesforce AP-215 Exam Syllabus Topics:

Section	Objectives
Data Harmonization and Data Model	- Dimensions and Measures 1. Creating and managing measures (KPIs) 2. Creating and managing dimensions 3. Calculated measurements - Data Classification 1. Hierarchy management 2. Attribute classification - Parent-Child Data Streams 1. Data Update Permissions 2. Linking data streams 3. Override Media Buy Hierarchies
Data Integration and Configuration	- Data Ingestion 1. File upload and API data sources 2. Scheduling and refresh settings - Data Stream Types 1. Configuring data source properties 2. Understanding different data stream types - Data Mapping 1. Data model configuration 2. Mapping fields from source files
Data Visualization and Dashboards	- Dashboard Design 1. KPI widgets and chart types 2. Building dashboards - Filters and Drill-Downs 1. Global and local filters 2. Drill-down configuration - Calculated Measurements 1. Creating custom calculations 2. Formulas and functions
Reports and Activation	- Data Export 1. Integration with external tools 2. Exporting data and insights - Report Types 1. Standard and custom reports 2. Report scheduling
Marketing Intelligence Platform Fundamentals	- Administration and Security 1. Workspace and account settings 2. User management and permissions - Platform Overview 1. Marketing Cloud Intelligence (formerly Datorama) overview 2. Key features and capabilities

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AP-215 Trainingsmaterialien: Marketing Cloud Intelligence Accredited Professional & AP-215 Lernmittel & Salesforce AP-215 Quiz

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Salesforce Marketing Cloud Intelligence Accredited Professional AP-215 Prüfungsfragen mit Lösungen (Q46-Q51):

46. Frage
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

Antwort: A,E

Begründung:
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.

47. Frage
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. 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.
C. 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.
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.

Antwort: B

Begründung:
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.

48. Frage
Which two statements are correct regarding LiteConnect?

A. It does not require any identification of entities, keys or any other categorization.
B. Data coming from LiteConnect cannot be harmonized with the rest of the workspace data via the harmonization center at a later step.
C. The dataset does not conform to the standard data model
D. All of the dimensions mapped within a LiteConnect data stream are considered overarching entities.

Antwort: A,C

Begründung:
LiteConnect is a feature in Salesforce Marketing Cloud Intelligence that allows users to bring external data into the platform quickly and easily. Here are the correct statements regarding LiteConnect:
A . LiteConnect allows for a quick setup by not requiring detailed identification of entities, keys, or categorization. Users can upload files without having to conform to the standard data model, which speeds up the process of data integration.
B . With LiteConnect, datasets are uploaded in their native format and do not conform to the standard data model of Marketing Cloud Intelligence. This means that the original structure of the dataset is maintained, and there is no need for extensive transformation or mapping upon the initial data import.
For C and D: While LiteConnect datasets might not conform to the standard data model initially, there are capabilities within Marketing Cloud Intelligence to further categorize and harmonize this data if needed. Therefore, C is not entirely correct, and D is incorrect because harmonization can indeed occur at a later step.

49. Frage
A client's data consists of three data streams as follows:
Data Stream A:

* The data streams should be linked together through a parent-child relationship.
* Out of the three data streams, Data Stream C is considered the source of truth for both the dimensions and measurements.
How should the "Override Media Buy Hierarchies" checkbox be set in order to meet the client's requirements?

A. It should be checked in Data Stream C
B. It should be checked in Data Stream B
C. It should be checked in Data Stream A
D. It should not be checked in any of the three Data Streams.

Antwort: A

Begründung:
If Data Stream C is the source of truth, the "Override Media Buy Hierarchies" checkbox should be checked for Data Stream C.
This means that the hierarchy defined within Data Stream C will take precedence over any other media buy hierarchies present in Data Streams A or B. By doing so, it enforces that the hierarchy from the source of truth (Data Stream C) is used throughout the dataset, maintaining the integrity of the hierarchical relationships as defined by the most reliable data source.

50. Frage
An implementation engineer is requested to create the harmonization field - Magician This field should come from multiple Twitter Ads data streams, and should follow the below logic:

Using the Harmonization Center, the engineer created a single Pattern for Campaign Name. What other action should the engineer take to meet the requirements?

A. Create a second Pattern for Media Buy Name
B. Create a second Pattern for Media Buy Name and add a validation list (with the two values) for the final Harmonized Dimension.
C. Create a second Pattern for Media Buy Name and apply two Classification Rules (one for 'Messi' and another for Ronaldo') for the final Harmonized Dimension.
D. Create a second Pattern for Media Buy Name and apply a Classification Rule (with the two values) for the final Harmonized Dimension

Antwort: C

Begründung:
For the field 'Magician', the engineer is required to follow a logic that extracts a value from 'Campaign Name' and checks against a validation list for specific values ('Messi' or 'Ronaldo'). If those values are not found, it should instead extract from 'Media Buy Name'. To accomplish this, the engineer should:
Use the created Pattern for 'Campaign Name'.
Create a second Pattern for 'Media Buy Name' to capture the fallback values.
Apply two Classification Rules to the Harmonized Dimension: one for the value 'Messi' and another for 'Ronaldo'. This is to check the extracted 'Campaign Name' against these specific values.
These steps ensure that the 'Magician' field will be populated with the correct values from the respective data streams following the specified logic.

51. Frage
......

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