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| Section | Weight | Objectives |
|---|---|---|
| Performance and Troubleshooting | 5% | - Common issues and fixes - Monitoring and optimization |
| General Functionalities | 13% | - Core platform capabilities - Navigation and interface - Basic concepts and terminology |
| Dashboards and Visualization | 15% | - KPI and metric display - Chart types and usage - Dashboard design and layout |
| Reporting and Analytics | 12% | - Report creation and scheduling - Attribution models - Insight generation and interpretation |
| Datasets and Data Views | 10% | - Dataset creation and management - View configuration and sharing - Filtering and segmentation |
| Overarching Entities | 3% | - Structure and configuration - Use cases and application scenarios |
| Data Mapping and Modeling | 5% | - Dimensions and measures - Source of truth configuration - Data structure and relationships |
| Data Harmonization | 3% | - Cross-source alignment - Pros and cons of harmonization approaches - Standardization methods |
| Automation and Workflow | 5% | - Workflow setup - Automated distribution |
| Calculated Fields and Functions | 5% | - VLOOKUP and FUZZYVLOOKUP - Extract and transformation formulas - Custom calculations |
| Data Quality and Cleansing | 7% | - Duplicate management - Error handling and correction - Validation rules |
| Data Permissions and Governance | 5% | - Parent-child data setups - Data update controls - User access and roles |
| Data Integration and Ingestion | 12% | - Data streams and connectors - Refresh schedules and data flow - API and file-based import |
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질문 # 47
Which Marketing Cloud Intelligence field is considered an attribute and not a "variable"?
정답：C
설명：
In Marketing Cloud Intelligence, attributes refer to characteristics of the data that describe the environment or context but do not change within the scope of the data being analyzed. 'Device Category' is typically an attribute as it describes a characteristic of the device used and doesn't vary within a given session or user interaction. In contrast, variables are typically metrics or dimensions that can change value or be measured.
질문 # 48
A client created a new KPI: CPS (Cost per Sign-up).
The new KIP is mapped within the data stream mapping, and is populated with the following logic: (Media Cost) / Sign-ups) As can be seen in the table below, CPS was created twice and was set with two different aggregations:
From looking at the table, what are the aggregation settings for each one of the newly created KPIs?
정답：D
설명：
The KPI CPS (Cost per Sign-up) would be calculated by dividing the 'Media Cost' by 'Sign-ups'. The table indicates that CPS is set with two different aggregations. In option C, CPS #1 is set to 'AUTO', which allows the system to decide the best aggregation method based on the context. CPS #2 is set to 'SUM', which indicates that the individual costs per sign-up are summed up across multiple records to provide a total cost per sign-up.
질문 # 49
Which three statements describe Overarching Entities? 03m 23s
정답：A,B,E
설명：
Overarching Entities in Salesforce Marketing Cloud Intelligence are designed to provide a high level of data organization that spans across multiple data streams. The key points about Overarching Entities are:
B . Relationship Types: Overarching entities can have either a Many-to-Many or One-to-Many relationship with the main entity, which allows for flexible data modeling and relationship definitions based on the nature of the data and how it should be analyzed and reported.
C . Acting as Main Entity: They can serve as a main entity in certain situations, enabling a shift in perspective for data analysis. This can be particularly useful when there is a need to view data from a different dimension that is more aligned with business requirements.
E . Storage Level: The values of these entities are not tied to any single data stream but are maintained at a workspace level, ensuring that they can be applied consistently across different datasets, which is critical for maintaining data integrity and ensuring that classifications are applied uniformly.
질문 # 50
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?
정답：B
설명：
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.
질문 # 51
Which three entities and/or functions can be used in an expression when building a calculated dimension?
정답：C,D,E
설명：
Calculated dimensions (D) and the VLOOKUP function (A) are not typically used within the expression for a calculated dimension. Calculated dimensions are usually an output, not an input, and VLOOKUP is a function typically used to enrich or connect data, not within the definition of a calculated dimension itself.
Explanation:
In the context of Marketing Cloud Intelligence, when building a calculated dimension, you can typically use:
B). Mapped dimensions: These are dimensions that have been brought into Marketing Cloud Intelligence through the data integration process and have been mapped to a known schema or model.
C). The EXTRACT function: This function can be used to dynamically create dimensions by extracting values from a mapped dimension or measurement.
E). Mapped measurements: Similar to mapped dimensions, these are quantitative data points that have been integrated into the platform and can be referenced in calculations.
질문 # 52
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