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Marketing-Cloud-Intelligence Practice Questions

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


B. EXTRACT(csv[campaign_name!;@',1)

Explanation: 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.

Question # 2
A client wants to integrate their data within Marketing Cloud Intelligence to optimize their marketing insights and cross-channel marketing activity analysis. Below are details regarding the different data sources and the number of data streams required for each source.



When harmonizing the Objective field from within the data stream mapping, which advantage is gained?
A. Scalability
B. Ease of Setup
C. Performance (Performance when loading a dashboard page)
D. Ease of Maintenance


D. Ease of Maintenance

Explanation: By harmonizing the Objective field within data stream mapping, an organization can benefit from:
Ease of Maintenance: Harmonization allows for consistent naming conventions across different data sources and streams. This means when business logic or naming conventions change, updates can be made in one place and consistently applied across all data streams. It also reduces the complexity of managing multiple streams and ensures data consistency, which is vital for accurate reporting and analysis.

Question # 3
Your client is interested in ingested the below file to a new generic data stream type:



The field ‘Meeting Code’ was mapped to the main entity key. ‘How should the ‘Room Number’ be mapped?
A. An attribute of ‘Meeting Code’
B. A custom metric and set aggregation to AUTO
C. A custom metric and set aggregation to SUM
D. A separate entity key


A. An attribute of ‘Meeting Code’

Explanation: In Marketing Cloud Intelligence, when a field is mapped to the main entity key, other related fields should be mapped as attributes of that key if they provide additional descriptors or details. Since 'Room Number' is related to 'Meeting Code', it would be an attribute of the' Meeting Code' entity, providing additional context to the meetings without serving as a metric or a separate entity key.

Question # 4
A client Ingested the following We into Marketing Cloud Intelligence:



The mapping of the above file can be seen below:
Date — Day
Media Buy Key — Media Buy Key
Campaign Name — Campaign Name
Campaign Group -. Campaign Custom Attribute 01
Clicks —> Clicks
Media Cost —> Media Cost
Campaign Planned Clicks —> Delivery Custom Metric 01
The client would like to have a "Campaign Planned Clicks" measurement.
This measurement should return the "Campaign Planned Clicks" value per Campaign, for example:
For Campaign Name 'Campaign AAA", the "Campaign Planned Clicks" should be 2000, rather than 6000 (the total sum by the number of Media Buy keys).
In order to create this measurement, the client considered multiple approaches. Please review the different approaches and answer the following question:



Which two options will yield a false result:
A. Option 2
B. Option 5
C. Option 3
D. Option 4
E. Option 1


B. Option 5
E. Option 1

Explanation: The goal is to obtain a "Campaign Planned Clicks" value per Campaign, not accumulated by Media Buy keys. Option 1 (SUM aggregation function) would sum all the "Campaign Planned Clicks" across Media Buy keys which would not yield the unique value per Campaign. Similarly, Option 5 (AVG aggregation function at Campaign Key level) would incorrectly average the values. Both options do not provide a way to return a singular "Campaign Planned Clicks" value for each Campaign.

Question # 5
Animplementation engineer has been provided with 4 different source files: 03m 16s

1. Twitter Ads
2. Creative Classification
3. Placement Classification
4, Campaign Category Classification

The main source is Twitter Ads (which includes various fields and KPIs), and the rest are classification files that connect to Twitter Ads and enrich different fields within it.
The connections between the files are described as follows: 1st Party Creative Classification
File structure/headers:



Category — links back to Campaign Category (Twitter Ads)
Which proposed solution meets the client's requirements for the above use case?

A. Option A
B. Option B
C. Option C
D. Option D


A. Option A

Explanation: For the given use case, where the Twitter Ads data stream needs to be enriched with classifications from three other sources, the correct implementation would involve creating links between the various fields across these files.
Option A is correct because it shows the correct usage of the fields from the classification files: "Creative ID" in the Creative Classification file is linked to the "Creative Key" in the Twitter Ads data, allowing for enrichment with creative details.
"Placement ID" in the Placement Classification file is linked to a corresponding field in the Twitter Ads data, allowing for placement details to be added. "Category" in the Campaign Category Classification file is linked back to "Campaign Category" in the Twitter Ads data, thus enriching the campaign data with the correct categories.
This configuration correctly uses VLOOKUP to enrich the Twitter Ads data stream with additional details from the classification files, aligning with best practices for data integration and enrichment in Marketing Cloud Intelligence.

Question # 6
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
A pivot table was created to present the count of opportunities in each stage. The pivot table is filtered on Jan 7th - 11th. Which option reflects the stage(s) the Opportunity key 123AA01 is associated with?
A. Registered
B. Interest & Registered
C. Confirmed Interest & Registered
D. Interest
E. Confirmed Interest


B. Interest & Registered

Explanation: Analyzing the Opportunity file with a filter set from January 7th to 11th, Opportunity Key '123AA01' appears under 'Interest' on January 6th and 8th, and under 'Registered' on January 10th. Therefore, during the specified date range, Opportunity Key '123AA01' is associated with both 'Interest' and 'Registered' stages. Salesforce Marketing Cloud Intelligence provides the capability to map and track opportunity stages over time, allowing for historical stage tracking and reporting. This answer aligns with the ability to use pivot tables to filter and display data by specific attributes and timeframes, as outlined in the Salesforce Marketing Cloud Intelligence documentation.

Question # 7
An implementation engineer has been asked to perform a QA for a newly created harmonization field, Color, implemented by a client.
The source file that was ingested can be seen below:

A. A Harmonized dimension was created via a pattern over the Creative Name.
B. A calculated dimension was created with the formula: EXTRACT([Creative_Namel, #1)
C. An EXTRACT formula (for Color) was written and mapped to a Media Buy custom attribute.
D. An EXTRACT formula (for Color) was written and mapped to a Creative custom attribute.


D. An EXTRACT formula (for Color) was written and mapped to a Creative custom attribute.

Explanation: Given that the 'Color' field is extracted from the 'Creative Name' field and appears to be part of the creative-level data, the most logical method would be to create an EXTRACT formula and map it to a Creative custom attribute. This allows the 'Color' value to be associated directly with each creative entry. In Salesforce Marketing Cloud Intelligence, the EXTRACT formula can be used to parse and segment text strings within a field, and this process is used for harmonizing data by creating new dimensions or attributes based on existing data, which is what's described here. This answer is consistent with Salesforce Marketing Cloud Intelligence features that enable data transformation and harmonization through formulaic mapping, as per the official Salesforce documentation on data harmonization and transformation.

Question # 8
A client's data consists of three data streams as follows:



* 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.
* Data Stream C was set as a ‘Parent’, and the ‘Override Media Buy Hierarchy’ checkbox is checked

What should the Data Updates Permissions be set to for Data Stream B?
A. There is no difference, all permissions will have a similar effect given the scenario.
B. Update Attributes and Hierarchies
C. Update Attributes
D. Inherit Attributes and Hierarchies


B. Update Attributes and Hierarchies

Explanation: With Data Stream C set as the 'Parent' and 'Override Media Buy Hierarchy' checked:
The appropriate setting for Data Stream B would be 'Update Attributes and Hierarchies'. This setting will ensure that the hierarchy and attributes from the parent data stream (C) are updated based on the child data stream (B) without overwriting the measurement data that the parent is the source of truth for.
The 'Override Media Buy Hierarchy' option checked indicates that the hierarchy of the parent is to be considered as the main one, but the attributes and hierarchy can still be updated from the child data stream, which aligns with option B.

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