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| Section | Weight | Objectives |
|---|---|---|
| Automation and Workflow | 5% | - Automated distribution - Workflow setup |
| Overarching Entities | 3% | - Use cases and application scenarios - Structure and configuration |
| General Functionalities | 13% | - Basic concepts and terminology - Core platform capabilities - Navigation and interface |
| Calculated Fields and Functions | 5% | - Custom calculations - VLOOKUP and FUZZYVLOOKUP - Extract and transformation formulas |
| Data Quality and Cleansing | 7% | - Validation rules - Duplicate management - Error handling and correction |
| Performance and Troubleshooting | 5% | - Monitoring and optimization - Common issues and fixes |
| Data Harmonization | 3% | - Standardization methods - Pros and cons of harmonization approaches - Cross-source alignment |
| Data Permissions and Governance | 5% | - User access and roles - Data update controls - Parent-child data setups |
| Reporting and Analytics | 12% | - Insight generation and interpretation - Report creation and scheduling - Attribution models |
| Dashboards and Visualization | 15% | - Dashboard design and layout - KPI and metric display - Chart types and usage |
| Data Integration and Ingestion | 12% | - Refresh schedules and data flow - API and file-based import - Data streams and connectors |
| Data Mapping and Modeling | 5% | - Dimensions and measures - Source of truth configuration - Data structure and relationships |
| Datasets and Data Views | 10% | - View configuration and sharing - Dataset creation and management - Filtering and segmentation |
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NEW QUESTION # 48
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 # 49
Which three entities and/or functions can be used in an expression when building a calculated dimension?
Answer: A,C,E
Explanation:
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.
NEW QUESTION # 50
An implementation engineer has been asked by a client for assistance with the following problem:
The below dataset was ingested:
However, when performing QA and querying a pivot table with Campaign Category and Clicks, the value for Type' is 4.
What could be the reason for this discrepancy?
Answer: A
Explanation:
The discrepancy of 'Clicks' being reported as 4 for 'Type1' when the sum of clicks in the dataset for 'Type1' is 8 (2 on 02/02/2021 and 6 on 03/02/2021) suggests that the aggregation function used in the pivot table is set to average (AVG) rather than sum. Salesforce Marketing Cloud Intelligence allows setting different aggregation functions for metrics, and setting it to average would result in such a discrepancy when more than one entry for the same type exists. Reference: Salesforce Marketing Cloud Intelligence documentation on custom measurements and data aggregations explains how to set and understand different aggregation functions.
NEW QUESTION # 51
Your client has provided sample files of their data from the following data sources:
Google Campaign Manager
Below are the requirements from the client and additional information:
* The sources are linked to each other by shared Media Buy names.
* In addition to the mutual Media Buys, the sources contain campaign and site values. However, the client would like to see the campaign/site values coming from Google CM and not from Google DV360.
* The source of truth for cost is Google DV360
Which action(s) are needed to take place in order to meet the client's requirement and set Google DV360 as the source of truth for Cost?
Answer: D
Explanation:
To set Google DV360 as the source of truth for cost:
The cost data from Google DV360 should be prioritized, which means ensuring that the 'Cost' field in Google Campaign Manager is not mapped or is mapped with less priority compared to Google DV360.
Given that DV360 is to be the source of truth, you do not want competing cost data from Campaign Manager. Unmapping 'Cost' in Google Campaign Manager prevents conflicting data between the two sources and upholds the integrity of the cost data coming from Google DV360.
NEW QUESTION # 52
An implementation engineer is requested to integrate 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 Assignmed') alongside'Employee Name' and/or 'Squard'.
A Parent-Child configuration was set between the two.
Which two statements are correct?
Answer: C,D
Explanation:
In Marketing Cloud Intelligence, joining two files requires a common field to be mapped as the same entity. If "employee_name" is consistently mapped across both data streams, it can serve as the basis for the join, regardless of whether "employee_id" is mapped. The choice of which file serves as the Parent stream depends on the use case and the desired reporting structure, but technically, either could serve as the Parent.
NEW QUESTION # 53
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