Salesforce Marketing Cloud Intelligence Accredited Professional (AP-215) Exam Questions Instant Access

Question 1

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 Interest stage?

Answer : D

Since the pivot table is filtered on January 11th and the provided Opportunity file does not show any records dated January 11th, there are zero opportunities in the Interest stage for that date. Salesforce Marketing Cloud Intelligence allows users to create pivot tables and filter data based on specific criteria, such as dates. In this case, the filter would exclude all rows that do not match the specified date, resulting in a count of zero for the Interest stage. This would apply to any stage since there are no records for January 11th. Reference can be made to Salesforce Marketing Cloud Intelligence documentation on filtering and pivot tables.

Question 2

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

AParent-Child configurations can cause performances issues

BA Parent-Child cannot be configured between an Ads data stream type and a Conversion Tag one.

CParent-Child links different tables based on shared key values

DParent-Child allows sharing both dimensions and measurements

Answer : A, C

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.

Question 3

An implementation engineer is requested to extract the second position

of the Campaign Name values.

The Campaign values consist of multiple delimiter types, as can be

seen in the following example:

Campaign Name: Ad15X2w&Delux_wal90

Desired value: Delux

Which three harmonization methods will achieve the desired outcome?

ACalculated Dimensions

BPatterns

CVlookup 0

DData Fusion

EMapping formula

Answer : A, B, E

To extract specific elements from a string in Marketing Cloud Intelligence, such as the second position of a Campaign Name with multiple delimiters, several harmonization methods can be employed:

Calculated Dimensions: These allow for the creation of custom dimensions using expressions or formulas that manipulate existing data. A calculated dimension can be designed to parse and extract segments of a string based on delimiters.

Patterns: This method involves defining a pattern or regex (regular expression) that matches and isolates the desired portion of the string. Patterns are highly effective for strings with complex structures and varying delimiter types.

Mapping Formula: Similar to calculated dimensions, mapping formulas provide a way to apply a transformation or extraction rule to data fields directly within data streams, enabling targeted data extraction like the desired 'Delux' from the Campaign Name.

These methods enable the implementation engineer to accurately segment and extract the needed data from complex string fields efficiently.

Question 4

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?

AThe measurement 'Clicks' is set as a percentage.

BA mapping formula was populated, indicating not to bring Type! values.

CThe aggregation function is set as AVG

DThe aggregation function is set as LIFETIME

Answer : C

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.

Question 5

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?

AWithin 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.

BCreate 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.

CUpload the two files and create a Parent-Child relationship between them. The Override Media Buy Hierarchy checkbox is checked in Google Analytics.

DCreate 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 : A

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.

Question 6

What is the relationship between ''Media Buy Key'' and ''Campaign Key''?

AMany-to-one (one Campaign Key has many Media Buy Keys)

BMany-to-many

COne-to-many (one Media Buy Key has many Campaign Keys)

DOne-to-one

Answer : A

Typically, 'Campaign Key' is a unique identifier for a specific marketing campaign, while 'Media Buy Key' refers to the purchases of advertising space associated with that campaign. A campaign can have multiple media buys, so the relationship is many-to-one, with many media buys (Media Buy Keys) associated with a single campaign (Campaign Key).

Question 7

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 assume that the file is mapped in the OPPORTUNITIES Data Stream type with the following mapping:

''Day'' --- ''Created Date''

''Opportunity Key'' + Opportunity Key

''Opportunity Stage'' --- Opportunity Stage

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?

Answer : D

pivot table is filtered on January 11th, we refer to the Opportunity file and see that there are no records for January 11th. Thus, there would be zero opportunities in the Confirmed Interest stage on that date. The Salesforce Marketing Cloud Intelligence's pivot table feature allows for the display of counts of entities based on the filtered criteria, which in this scenario would show zero since no records exist for the filtered date. Reference: Salesforce Marketing Cloud Intelligence documentation on pivot table functionalities.