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| Section | Weight | Objectives |
|---|---|---|
| Dashboards and Visualization | 15% | - KPI and metric display - Chart types and usage - Dashboard design and layout |
| Automation and Workflow | 5% | - Workflow setup - Automated distribution |
| Data Quality and Cleansing | 7% | - Duplicate management - Error handling and correction - Validation rules |
| Overarching Entities | 3% | - Structure and configuration - Use cases and application scenarios |
| Reporting and Analytics | 12% | - Attribution models - Insight generation and interpretation - Report creation and scheduling |
| Data Integration and Ingestion | 12% | - Data streams and connectors - Refresh schedules and data flow - API and file-based import |
| Data Permissions and Governance | 5% | - Parent-child data setups - User access and roles - Data update controls |
| Performance and Troubleshooting | 5% | - Common issues and fixes - Monitoring and optimization |
| General Functionalities | 13% | - Core platform capabilities - Basic concepts and terminology - Navigation and interface |
| Data Harmonization | 3% | - Cross-source alignment - Standardization methods - Pros and cons of harmonization approaches |
| Data Mapping and Modeling | 5% | - Data structure and relationships - Dimensions and measures - Source of truth configuration |
| Datasets and Data Views | 10% | - Dataset creation and management - Filtering and segmentation - View configuration and sharing |
| Calculated Fields and Functions | 5% | - Custom calculations - Extract and transformation formulas - VLOOKUP and FUZZYVLOOKUP |
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NEW QUESTION # 31
What are two potential reasons for performance issues (when loading a dashboard) when using the CRM data stream type?
Answer: A,C
Explanation:
For performance issues when loading a dashboard using CRM data stream type:
Pacing can create performance issues because daily rows for every lead and opportunity key can result in a very large number of rows, increasing load times.
Having only calculated measurements means there are no direct, mappable values to query against, which can increase the computational load and affect performance.
NEW QUESTION # 32
Which two statements are correct regarding variable Dimensions in marketing Cloud intelligence's data model?
Answer: B,C
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 # 33
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: A,B
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 # 34
A client provides the following two data streams:
Data Stream 1:
The client would like to use a VLOOKUP formula to calculate the Cost per Campaign Advertiser on January 1st 2020.
Which mapping options should the client apply to obtain the expected result?
Answer: B
Explanation:
To calculate Cost per Campaign Advertiser using a VLOOKUP formula, the client needs to look up the 'Cost' from Data Stream 2 based on a matching 'Media Buy Name' in Data Stream 1. Option A shows that 'Media Buy Name' is the lookup value, which is correct. The 'Campaign Advertiser' is then linked to the 'Cost' from Data Stream 2 through the VLOOKUP formula applied to the 'Media Buy Custom Attribute 01' in Data Stream 2. This setup will correctly associate the cost with the campaign advertiser.
NEW QUESTION # 35
An implementation engineer has been provided with 4 different source files: 03m 48s
1. Twitter Ads ~
2. Creative Classification
3. Placement Classification
4, Campaign Category Classification
The main source is Twitter Ads (which includes various fields and KPIs), and the rest are classification files that connect to Twitter Ads and enrich different fields within it.
The connections between the files are described as follows:
1st Party Creative Classification
File structure/headers:
Creative ID - links back to Creative Key (Twitter Ads)
1st Party Placement Classification by
File structure/headers:
Answer: D
Explanation:
In Salesforce Marketing Cloud Intelligence, connections between source files and classification files are established through common keys that link data records. For this scenario:
The "1st Party Creative Classification" file has a "Creative ID" field which corresponds to the "Creative Key" in the "Twitter Ads" data. This link enables enrichment of Twitter Ads data with creative classification details.
The "1st Party Placement Classification" file will contain a "Placement ID" that connects to a corresponding field in the "Twitter Ads" data, enabling the enrichment of placement classification details.
Option A appears to accurately depict this setup where data streams for "Creative Classification" and "Placement Classification" are connected to the "Twitter Ads" data stream using the "Creative ID" and "Placement ID", respectively. This structure allows for the enhancement of the main Twitter Ads data with additional classification information.
NEW QUESTION # 36
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