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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Harmonization | 3% | - Cross-source alignment - Standardization methods - Pros and cons of harmonization approaches |
| Topic 2: Dashboards and Visualization | 15% | - Dashboard design and layout - Chart types and usage - KPI and metric display |
| Topic 3: Automation and Workflow | 5% | - Workflow setup - Automated distribution |
| Topic 4: Calculated Fields and Functions | 5% | - Custom calculations - Extract and transformation formulas - VLOOKUP and FUZZYVLOOKUP |
| Topic 5: Performance and Troubleshooting | 5% | - Monitoring and optimization - Common issues and fixes |
| Topic 6: Data Integration and Ingestion | 12% | - Data streams and connectors - API and file-based import - Refresh schedules and data flow |
| Topic 7: Datasets and Data Views | 10% | - View configuration and sharing - Filtering and segmentation - Dataset creation and management |
| Topic 8: Data Permissions and Governance | 5% | - Parent-child data setups - Data update controls - User access and roles |
| Topic 9: General Functionalities | 13% | - Navigation and interface - Core platform capabilities - Basic concepts and terminology |
| Topic 10: Data Quality and Cleansing | 7% | - Validation rules - Error handling and correction - Duplicate management |
| Topic 11: Overarching Entities | 3% | - Use cases and application scenarios - Structure and configuration |
| Topic 12: Reporting and Analytics | 12% | - Attribution models - Insight generation and interpretation - Report creation and scheduling |
| Topic 13: Data Mapping and Modeling | 5% | - Source of truth configuration - Data structure and relationships - Dimensions and measures |
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NEW QUESTION # 53
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: A
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 # 54
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: B
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 # 55
Which option will yield the desired result:?
Answer: B
Explanation:
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.
NEW QUESTION # 56
Ina workspace that contains one hundred data streams and a lot of data, what is the biggest downside of using calculated dimensions?
Answer: B
Explanation:
In a workspace with a high number of data streams, such as one hundred, the biggest downside of using calculated dimensions is the performance impact. Calculated dimensions require computational resources to dynamically compute values based on existing data. This can lead to increased load times and slower performance, especially in environments with large amounts of data or complex calculations. This performance degradation is due to the extra processing power needed every time the data is accessed or refreshed, impacting the overall efficiency of data retrieval and analysis operations.
NEW QUESTION # 57
A client's data consists of three data streams as follows:
* The data streams should be linked together through a parent-child relationship.
* Out of the three data streams, Data Stream C is considered the source of truth for both the dimensions and measurements.
Which data stream should be set as a parent?
Answer: A
Explanation:
Since Data Stream C is considered the source of truth for both dimensions and measurements, it should be set as the parent data stream. This is because the parent data stream is used as the primary source for hierarchical and attribute data within a parent-child relationship setup. As the source of truth, Data Stream C will provide the foundational data upon which the other streams can be aligned and will ensure consistency and accuracy across the linked data.
NEW QUESTION # 58
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