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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Mapping and Modeling | 5% | - Dimensions and measures - Data structure and relationships - Source of truth configuration |
| Topic 2: Data Harmonization | 3% | - Pros and cons of harmonization approaches - Standardization methods - Cross-source alignment |
| Topic 3: Datasets and Data Views | 10% | - Filtering and segmentation - View configuration and sharing - Dataset creation and management |
| Topic 4: Data Permissions and Governance | 5% | - Data update controls - Parent-child data setups - User access and roles |
| Topic 5: Overarching Entities | 3% | - Use cases and application scenarios - Structure and configuration |
| Topic 6: Calculated Fields and Functions | 5% | - VLOOKUP and FUZZYVLOOKUP - Custom calculations - Extract and transformation formulas |
| Topic 7: Performance and Troubleshooting | 5% | - Common issues and fixes - Monitoring and optimization |
| Topic 8: Automation and Workflow | 5% | - Workflow setup - Automated distribution |
| Topic 9: Reporting and Analytics | 12% | - Attribution models - Report creation and scheduling - Insight generation and interpretation |
| Topic 10: Dashboards and Visualization | 15% | - Dashboard design and layout - Chart types and usage - KPI and metric display |
| Topic 11: Data Integration and Ingestion | 12% | - Refresh schedules and data flow - Data streams and connectors - API and file-based import |
| Topic 12: Data Quality and Cleansing | 7% | - Validation rules - Duplicate management - Error handling and correction |
| Topic 13: General Functionalities | 13% | - Navigation and interface - Core platform capabilities - Basic concepts and terminology |
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NEW QUESTION # 63
After uploading a standard file into Marketing Cloud intelligence via total Connect, you noticed that the number of rows uploaded (to the specific data stream) is NOT equal to the number of rows present in the source file. What are two resource that may cause this gap?
Answer: A,D
Explanation:
In Marketing Cloud Intelligence, discrepancies between the number of rows uploaded and the number of rows present in the source file can be caused by several factors. If all mapped measurements for a row are zero, that row may be excluded from the upload, as it does not contribute to the analytics. Additionally, if the main entity, which acts as the primary identifier for records, is not mapped, the system cannot correctly ingest the data as it lacks the necessary reference to organize and store the information.
NEW QUESTION # 64
An implementation engineer is requested to extract the first three-letter segment of the Campaign Name values.
For example:
Campaign Name: AFD@Mulop-1290
Desired outcome: AFD
Other examples:
Which formula will return the desired values?
Answer: C
Explanation:
The EXTRACT function is used to split a string based on a delimiter and return the segment at the specified position. The campaign names are structured with the segment of interest followed by an '@' sign. Therefore, the formula needs to extract the segment before the '@'.
The correct formula is: EXTRACT(csv['campaign_name']; '@', 1). This will take the 'campaign_name' field, split it at the '@' sign, and return the first segment (position 1), which is the three-letter code that is required. The other options are incorrect because they do not properly specify the delimiter and the segment position in the way needed to achieve the desired outcome.
NEW QUESTION # 65
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: A
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 # 66
What are two potential reasons for performance issues (when loading a dashboard) when using the CRM data stream type?
Answer: A,B
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 # 67
Which two statements are correct regarding the Parent-Child configuration?
Answer: A,D
Explanation:
Parent-Child configurations in Marketing Cloud Intelligence are used to link different data tables based on shared key values, allowing for the relational organization of data across various streams. While this setup enhances data analysis and reporting by maintaining logical relationships between parent and child tables, it can also introduce performance issues. The complexity increases with the number of relationships and the volume of data, potentially slowing down query processing and data manipulation. Additionally, Parent-Child configurations facilitate the sharing of dimensions and measurements across linked tables, enhancing the data's usability without duplicating it.
NEW QUESTION # 68
......
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