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Salesforce AP-215 Exam Syllabus Topics:

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

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Salesforce Marketing Cloud Intelligence Accredited Professional AP-215 Prüfungsfragen mit Lösungen (Q37-Q42):

37. Frage
An implementation engineer has been provided with the below dataset:

*Note: CPC = Cost per Click
Formula: Cost / Clicks
Which action should an engineer take to successfully integrate CPC?

A. Populate the logic within a custom measurement. Set Aggregation to AVG.
B. Unmap it, as Datorama will calculate it automatically.
C. Populate the logic within a custom measurement. Set Aggregation to SUM.
D. Populate the logic within a custom measurement. No need to change Aggregation.

Antwort: D

Begründung:
CPC (Cost per Click) is a calculated metric that should be created using a custom measurement based on the formula provided (Cost / Clicks). This calculation does not require a change in the aggregation setting because it is derived from other base metrics that are already aggregated appropriately. In Salesforce Marketing Cloud Intelligence, custom measurements are used to create new metrics from existing data points, and the system will use the underlying data's aggregation to perform the calculation. Reference: Salesforce Marketing Cloud Intelligence documentation on creating custom measurements and calculated metrics.

38. Frage
A client has integrated data from Facebook Ads. Twitter ads, and Google ads in marketing Cloud intelligence. For each data source, the source, the data follows a naming convensions as ...
Facebook Ads Naming Convention - Campaign Name:
CampID_CampName#Market_Object#object#targetAge_TargetGender
Twitter Ads Naming Convention- Media Buy Name
MarketTargeAgeObjectiveOrderID
Google ads Naming Convention-Media Buy Name:
Buying_type_Market_Objective
The client wants to harmonize their data on the common fields between these two platforms (i.e. Market and Objective) using the Harmonization Center. Given the above information, which statement is correct regarding the ability to implement this request?
wet Me - Given the above information, which statement i 's Correct regarding the ability to implement this request?

A. This is not possible as the naming conventions are in different fields (Campaign Name and Placement Name)
B. The client will be able to do this and it will require building three patterns.
C. The client Wi-Fi be able to harmonize only Google Ads and Twitter Ads, as Facebook Ads naming convention contains mufti delimiters.
D. it is not possible to do this, as the naming conventions are different

Antwort: B

Begründung:
Despite the different naming conventions, harmonization is possible using patterns in the Harmonization Center. By extracting the 'Market' and 'Objective' components from the naming conventions of each platform, three separate patterns would be created to map these common fields consistently across the data from Facebook Ads, Twitter Ads, and Google Ads.

39. Frage
The following file was uploaded into Marketing Cloud Intelligence as a Generic Data Stream type:

The mapping is as follows:
Day - Day
web_site_key -> Main Generic Entity Key
web_site_name -> Main Generic Entity Name
Web_site_source -> Main Generic Entity Attribute 01
Page Views - Generic Metric 1
How many rows will be stored in Marketing Cloud Intelligence after the above file is ingested?

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

Antwort: C

Begründung:
With the uploaded file mapped as a Generic Data Stream type, the unique identifier for a row is the combination of 'Day', 'web_site_key', 'web_site_name', and 'Web_site_source'. As 'Day' is mapped to 'Day', 'web_site_key' to 'Main Generic Entity Key', 'web_site_name' to 'Main Generic Entity Name', and 'Web_site_source' to 'Main Generic Entity Attribute 01', each unique combination of these fields will constitute a separate row.
The provided file has 4 unique combinations of 'Day', 'web_site_key', 'web_site_name', and 'Web_site_source', as each line has a unique 'web_site_key' and 'web_site_name'. Consequently, Marketing Cloud Intelligence will store 4 rows, one for each unique combination.

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

Antwort: E

Begründung:
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.

41. Frage
Which two statements are correct regarding the Parent-Child configuration?

A. Parent-Child configurations can cause performances issues
B. Parent-Child links different tables based on shared key values
C. Parent-Child allows sharing both dimensions and measurements
D. A Parent-Child cannot be configured between an Ads data stream type and a Conversion Tag one.

Antwort: A,B

Begründung:
Parent-Child configurations in Marketing Cloud Intelligence are used to link different data tables based on shared key values, allowing for the relational organization of data across various streams. While this setup enhances data analysis and reporting by maintaining logical relationships between parent and child tables, it can also introduce performance issues. The complexity increases with the number of relationships and the volume of data, potentially slowing down query processing and data manipulation. Additionally, Parent-Child configurations facilitate the sharing of dimensions and measurements across linked tables, enhancing the data's usability without duplicating it.

42. Frage
......

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