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| Section | Weight | Objectives |
|---|---|---|
| Performance and Troubleshooting | 5% | - Monitoring and optimization - Common issues and fixes |
| Data Harmonization | 3% | - Cross-source alignment - Standardization methods - Pros and cons of harmonization approaches |
| Dashboards and Visualization | 15% | - Chart types and usage - KPI and metric display - Dashboard design and layout |
| Reporting and Analytics | 12% | - Insight generation and interpretation - Attribution models - Report creation and scheduling |
| Calculated Fields and Functions | 5% | - Custom calculations - VLOOKUP and FUZZYVLOOKUP - Extract and transformation formulas |
| Data Integration and Ingestion | 12% | - Data streams and connectors - Refresh schedules and data flow - API and file-based import |
| General Functionalities | 13% | - Core platform capabilities - Basic concepts and terminology - Navigation and interface |
| Automation and Workflow | 5% | - Automated distribution - Workflow setup |
| Data Permissions and Governance | 5% | - User access and roles - Parent-child data setups - Data update controls |
| Data Mapping and Modeling | 5% | - Dimensions and measures - Source of truth configuration - Data structure and relationships |
| Datasets and Data Views | 10% | - Dataset creation and management - Filtering and segmentation - View configuration and sharing |
| Overarching Entities | 3% | - Structure and configuration - Use cases and application scenarios |
| Data Quality and Cleansing | 7% | - Duplicate management - Validation rules - Error handling and correction |
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NEW QUESTION # 53
What Is a disadvantage of using a Vlookup formula?
Answer: C
Explanation:
The use of VLOOKUP formulas can increase the processing time of data streams because it requires a lookup operation for each row in the data set. When large volumes of data are involved, or when multiple VLOOKUPs are used, this can significantly impact processing time due to the complexity and computational requirements of matching and retrieving the data.
NEW QUESTION # 54
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?
Answer: D
Explanation:
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.
NEW QUESTION # 55
What are two potential reasons for performance issues (when loading a dashboard) when using the CRM data stream type?
Answer: A,B
NEW QUESTION # 56
Which two statements are correct regarding variable Dimensions in marketing Cloud intelligence's data model?
Answer: C,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 # 57
The following file was uploaded into Marketing Cloud Intelligence as a generic dataset type:
The mapping is as follows:
Day - Day
Web_site_source - Main Generic Entity Attribute 01
Page Views - Generic Metric 1
*Note that 'web_site_key' and 'web_site_name' are NOT mapped.
How many rows will be stored in Marketing Cloud Intelligence after the above file is ingested?
Answer: D
Explanation:
In Marketing Cloud Intelligence, when a file is uploaded as a generic dataset type and mapped accordingly, each unique combination of the mapped fields results in a separate row in the database. The file in question has been mapped with 'Day' to 'Day', 'Web_site_source' to 'Main Generic Entity Attribute 01', and 'Page Views' to 'Generic Metric 1'. The 'web_site_key' and 'web_site_name' are not mapped and thus, won't affect the row count.
Since there are 4 unique combinations of the mapped fields in the uploaded file (each day and source combination is unique), Marketing Cloud Intelligence will store 4 rows after ingestion, corresponding to each unique combination of 'Day' and 'Web_site_source'.
NEW QUESTION # 58
......
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