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Your client would like to create a new harmonization field - Exam Topic.
The below table represents the harmonization logic from each source.
As can be seen from the table there are in fact two fields that hold a certain connection: Exam ID and Exam Topic. The connection indicates that where an Exam ID is found -a single Exam Topic value is associated with it.
The Client has a requirement to be able to view measurements from all data sources sliced by Exam Topic values as seen in the following example:
Which harmonization feature should an Implementation engineer use to meet the client's requirement?
To meet the client's requirement of slicing measurements by 'Exam Topic' values, an Implementation Engineer should use Custom Classification. This feature allows different Exam IDs to be classified into their respective Exam Topics, ensuring that data from all sources can be accurately harmonized and analyzed based on these topics.
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?
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.
A client would like to integrate the following two sources:
Google Campaign Manager:
IAS:
After configuring a Parent-Child relationship between the files, which query should an implementation engineer run in order to QA the setup?
To QA the Parent-Child relationship setup between Google Campaign Manager and IAS data sources, it is essential to query fields that are common to both sources and that are relevant to the relationship. 'Media Buy Type' and 'Media Buy Name' are common identifiers between the two datasets. 'Impressions' from the Google Campaign Manager and 'Analyzed Impressions' from the IAS data are the metrics that should be compared to ensure they match or correlate as expected due to the Parent-Child relationship. The QA process involves checking that the data is correctly aligned and that the metrics from the parent source (Google Campaign Manager) are properly related to the metrics from the child source (IAS). Reference: Salesforce Marketing Cloud Intelligence documentation on data integration, Parent-Child relationships, and QA procedures for data setup.
An implementation engineer has been provided with 4 different source files: 03m 48s
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:
Creative ID --- links back to Creative Key (Twitter Ads)
1st Party Placement Classification by
File structure/headers:
A)
B)
C)
D)
In Salesforce Marketing Cloud Intelligence, connections between source files and classification files are established through common keys that link data records. For this scenario:
The '1st Party Creative Classification' file has a 'Creative ID' field which corresponds to the 'Creative Key' in the 'Twitter Ads' data. This link enables enrichment of Twitter Ads data with creative classification details.
The '1st Party Placement Classification' file will contain a 'Placement ID' that connects to a corresponding field in the 'Twitter Ads' data, enabling the enrichment of placement classification details.
Option A appears to accurately depict this setup where data streams for 'Creative Classification' and 'Placement Classification' are connected to the 'Twitter Ads' data stream using the 'Creative ID' and 'Placement ID', respectively. This structure allows for the enhancement of the main Twitter Ads data with additional classification information.
Which option will yield the desired result:?
Option 4 presents two calculated measurements for 'Group Min Cost' with 'MIN' and 'AVG' aggregations. This approach aligns with the client's need for the minimum and average media cost values. 'Group Min Cost 4 MIN' will calculate the minimum media cost across the 'Media Buy Key', while 'Group Min Cost 4 FINAL' will average these minimum costs at the 'Campaign Key' level. This will yield the desired result where minimum costs are calculated at the Media Buy Key level and then averaged at the Campaign Key level.
63 questions covering all exam domains, starting from $20
Exam domains verified against: Official Salesforce Marketing-Cloud-Intelligence exam guide, last checked September 2026.
Understand core Marketing Cloud Intelligence platform capabilities and foundational concepts. Build knowledge of how the system works and its primary features for data analysis and reporting.
Master common Marketing Cloud Intelligence functions used in data workflows. Apply these functions to solve real-world data integration challenges on the platform.
Sample question from this domain above: Q2
Learn how Marketing Cloud Intelligence ingestion capabilities work and what outcomes they produce. Understand the practical effects of different mapping approaches.
Sample question from this domain above: Q4
Master Data Update permissions and their configuration settings. Know how parent-child setups work, including the Source of Truth requirement that governs data hierarchy.
Sample question from this domain above: Q3
Learn different harmonization methods and their properties. Weigh the pros and cons of each approach for your specific use cases.
Understand vlookup statements and their general properties within the platform. Know when and how to use vlookup to enrich and match data.
Learn what Overarching Entities are and when to apply them. Identify which use cases benefit from this feature and how to implement it correctly.
Know the use cases where Data Fusion adds value. Understand its properties and when to use it to combine data from multiple sources.
Master calculated objects and their functionalities on the platform. Recognize different aggregation types and when each one applies.
Sample question from this domain above: Q5
Understand Harmonization Center capabilities including Classification Rules, Validation Lists, Patterns and Harmonized Dimensions. Use these tools to standardize and validate data quality.
Sample question from this domain above: Q1
Learn how CRM properties work within Marketing Cloud Intelligence. Understand CRM behavior and how it integrates with the platform.
Know how to perform common quality assurance steps across various platform scenarios. Apply QA techniques to validate data, configurations and solution implementations.
Understand Marketing Cloud Intelligence data model entities and their relationships. Learn data categorization including main entities, keys and attributes.
Identify invalid solutions when reviewing solution design diagrams. Recognize valid solutions that meet requirements and best practices.
Common questions about the exam itself
