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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Calculated Fields and Functions | 5% | - Extract and transformation formulas - Custom calculations - VLOOKUP and FUZZYVLOOKUP |
| Topic 2: Automation and Workflow | 5% | - Workflow setup - Automated distribution |
| Topic 3: General Functionalities | 13% | - Core platform capabilities - Navigation and interface - Basic concepts and terminology |
| Topic 4: Performance and Troubleshooting | 5% | - Common issues and fixes - Monitoring and optimization |
| Topic 5: Datasets and Data Views | 10% | - Dataset creation and management - Filtering and segmentation - View configuration and sharing |
| Topic 6: Reporting and Analytics | 12% | - Attribution models - Report creation and scheduling - Insight generation and interpretation |
| Topic 7: Dashboards and Visualization | 15% | - Chart types and usage - KPI and metric display - Dashboard design and layout |
| Topic 8: Data Quality and Cleansing | 7% | - Error handling and correction - Duplicate management - Validation rules |
| Topic 9: Data Integration and Ingestion | 12% | - Refresh schedules and data flow - API and file-based import - Data streams and connectors |
| Topic 10: Data Harmonization | 3% | - Cross-source alignment - Standardization methods - Pros and cons of harmonization approaches |
| Topic 11: Overarching Entities | 3% | - Structure and configuration - Use cases and application scenarios |
| Topic 12: Data Mapping and Modeling | 5% | - Source of truth configuration - Data structure and relationships - Dimensions and measures |
| Topic 13: Data Permissions and Governance | 5% | - User access and roles - Parent-child data setups - Data update controls |
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NEW QUESTION # 14
Which three statements accurately describe the different data stream types in Marketing Cloud intelligence?
Answer: A,B,C
Explanation:
In Marketing Cloud Intelligence, data stream types are templates that define how data should be structured within the system. Each data stream type:
B . Includes at least one entity, which is a fundamental component of the data stream and represents a collection of related data points.
D . Has its own main entity, which is the primary focus of that particular data stream type and serves as the central point of reference for the associated data.
E . Contains its own unique set of measurements that are specific to the type of data being captured within that stream. These measurements represent quantitative data that can be analyzed within the context of the main entity and other dimensions present in the data stream.
A is incorrect because not every data stream type includes the Media Buy entity-this is specific to certain types of advertising data streams. C is incorrect because not all data stream types share at least one mutual measurement; measurements are typically unique to the data stream's focus and purpose.
NEW QUESTION # 15
A client has integrated the following files:
File A:
File B:
The client would like to link the two files in order to view the two KPIs ('Tasks Completed' and 'Tasks Assigned) alongside 'Employee Name' and/or
'Squad'.
The client set the following properties:
+ File A is set as the Parent data stream
* Both files were uploaded to a generic data stream type.
* Override Media Buy Hierarchies is checked for file A.
* The 'Data Updates Permissions' set for file B is 'Update Attributes and Hierarchy'.
When filtering on the entire date range (1-30/8), and querying employee ID, Name and Squad with the two measurements - what will the result look like?
Answer: C
Explanation:
In Marketing Cloud Intelligence, when linking two data streams, the parent data stream (File A) provides the main structure. Since 'Override Media Buy Hierarchies' is checked for File A, the hierarchies from File B will be aligned with File A. Given 'Data Updates Permissions' set for file B as 'Update Attributes and Hierarchy', this means that attributes and hierarchy will be updated in the parent file based on the child file (File B), but the child file's metrics won't be associated with the parent file's date.
Hence, when filtering on the entire date range (1-30/8), the resulting view will align with the structure of the parent data stream, showing the KPIs ('Tasks Completed' from File A and 'Tasks Assigned' from File B) alongside the employee names and squads from the respective files. Since the employee IDs align, the data can be linked properly. However, since the dates do not align (File A data is from 01/08/2019 and File B from 15/08/2019), only attributes from File B will be updated without date association.
The result will look like Option C, where the employee names are corrected based on File B's data, the squads are added from File B, and the tasks_completed and tasks_assigned are displayed from their respective files. The tasks_assigned from File B are shown without date association as File B's date doesn't match with File A's.
NEW QUESTION # 16
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 Confirmed Interest stage?
Answer: D
Explanation:
Based on the data provided and the date filter set to January 11th, there are no records for 'Confirmed Interest' on that specific date in the Opportunity file. Thus, the number of opportunities in the 'Confirmed Interest' stage for January 11th would be zero (0). In Salesforce Marketing Cloud Intelligence, when creating pivot tables, the data is aggregated based on the selected filters. If no records meet the filter criteria, the result for that category would be zero. The answer is supported by best practices in data analysis and reporting within Salesforce Marketing Cloud Intelligence, where date filters are applied to segment and analyze data.
NEW QUESTION # 17
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?
Answer: B
Explanation:
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.
NEW QUESTION # 18
Which two statements are correct regarding variable Dimensions in marketing Cloud intelligence's data model?
Answer: B,D
Explanation:
Variable dimensions in Marketing Cloud Intelligence's data model are flexible and can be associated with multiple entities, forming a many-to-many relationship. These dimensions are configured and stored at the workspace level, allowing for customization and alignment with specific reporting needs and analytics practices.
NEW QUESTION # 19
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