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

Question 1

Ina workspace that contains one hundred data streams and a lot of data, what is the biggest downside of using calculated dimensions?

APerformance

BEase of setup

CEase of maintenance

DScalability

Answer : A

In a workspace with a high number of data streams, such as one hundred, the biggest downside of using calculated dimensions is the performance impact. Calculated dimensions require computational resources to dynamically compute values based on existing data. This can lead to increased load times and slower performance, especially in environments with large amounts of data or complex calculations. This performance degradation is due to the extra processing power needed every time the data is accessed or refreshed, impacting the overall efficiency of data retrieval and analysis operations.

Question 2

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 3

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

''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 7th - 10th. How many different stages are presented in the table?

Answer : C

Based on the Opportunity file and considering the filter dates from January 7th to 10th, the different stages presented are 'Interest', 'Confirmed Interest', and 'Registered'. This makes a total of 3 different stages that would be presented in the pivot table. Salesforce Marketing Cloud Intelligence allows for the creation of pivot tables that can display counts of entities across different dimensions, in this case, Opportunity Stages. Reference to Salesforce Marketing Cloud Intelligence documentation that covers data mapping and pivot table creation would support this conclusion.

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

An implementation engineer is requested to apply the following logic:

To apply the above logic, the engineer used only the Harmonization Center, without any mapping manipulations. What is the minimum amount of Patterns creating both 'Platform' and 'Line of Business'?"

Answer : B

To create both 'Platform' and 'Line of Business' fields using Patterns in the Harmonization Center without mapping manipulations, the engineer would need to create separate patterns for each data source mentioned. According to the provided images:

One pattern for LinkedIn Ads, to extract the 'Campaign Name' at position 4 for the Platform and 'Media Buy Name' at position 7 for Line of Business.

One pattern for AdRoll, to extract 'Media Buy Name' at position 3 for Platform and at position 2 for Line of Business.

One pattern for Google Analytics, which seems not required for the Platform but could apply if the Line of Business extraction is necessary, although it states N/A.

Hence, a minimum of 3 patterns would be necessary to create the fields required.

Question 6

A client has integrated data from Facebook Ads, Twitter Ads, and Google Ads in Marketing Cloud Intelligence. For each data source, the data

follows a naming convention as shown below:

Facebook Ads Naming Convention - Campaign Name:

Camp|D_CampName#Market_Objective#TargetAge_TargetGender

Twitter Ads Naming Convention - Media Buy Name:

Market|TargetAge|Objective|OrderID

' 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.

In addition to the previous details, the client provides the following data sample:

Logic specification:

If a value is not present in the Validation List, return ''Not Valid''

If a value is not present in the Classification File, return ''Unclassified''.

If the Harmonization center is used to harmonize the above data and files, what table will show the final output?

AOption A

BOption B

COption C

DOption D

Answer : B

The correct table would be Option B. The harmonization process would identify the 'Market' from the campaign or media buy name based on the delimiter and position rules specified in the naming conventions. The harmonized 'Market' would then be matched against the classification file and validation list. If a value does not match the validation list, it would return 'Not Valid', and if it's not present in the classification file, it would return 'Unclassified'. Option B is the only table showing the 'Not Valid' category which aligns with the logic specification provided.

Question 7

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?

APopulate the logic within a custom measurement. No need to change Aggregation.

BUnmap it, as Datorama will calculate it automatically.

CPopulate the logic within a custom measurement. Set Aggregation to AVG.

DPopulate the logic within a custom measurement. Set Aggregation to SUM.

Answer : A

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.

Salesforce Marketing Cloud Intelligence Accredited Professional (AP-215) Marketing Cloud Intelligence Accredited Professional Exam Practice Test