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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Reporting and Analytics | 12% | - Insight generation and interpretation - Report creation and scheduling - Attribution models |
| Topic 2: Data Quality and Cleansing | 7% | - Error handling and correction - Validation rules - Duplicate management |
| Topic 3: General Functionalities | 13% | - Core platform capabilities - Basic concepts and terminology - Navigation and interface |
| Topic 4: Datasets and Data Views | 10% | - Filtering and segmentation - View configuration and sharing - Dataset creation and management |
| Topic 5: Performance and Troubleshooting | 5% | - Common issues and fixes - Monitoring and optimization |
| Topic 6: Data Harmonization | 3% | - Pros and cons of harmonization approaches - Cross-source alignment - Standardization methods |
| Topic 7: Data Permissions and Governance | 5% | - Parent-child data setups - Data update controls - User access and roles |
| Topic 8: Data Integration and Ingestion | 12% | - API and file-based import - Refresh schedules and data flow - Data streams and connectors |
| Topic 9: Overarching Entities | 3% | - Structure and configuration - Use cases and application scenarios |
| Topic 10: Data Mapping and Modeling | 5% | - Source of truth configuration - Dimensions and measures - Data structure and relationships |
| Topic 11: Dashboards and Visualization | 15% | - Dashboard design and layout - Chart types and usage - KPI and metric display |
| Topic 12: Calculated Fields and Functions | 5% | - Extract and transformation formulas - Custom calculations - VLOOKUP and FUZZYVLOOKUP |
| Topic 13: Automation and Workflow | 5% | - Automated distribution - Workflow setup |
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NEW QUESTION # 47
An implementation engineer has been asked to perform a QA for a newly created harmonization field, Color, implemented by a client.
The source file that was ingested can be seen below:
The client performed the below standard mapping:
As a final step, the client had created the field 'Color'. As can be seen, it is extracted from the Creative Name (after the '#' sign).
For QA purposes, you have queried a pivot table, with the following fields:
* Media Buy Key
* Media Buy Name
* In View Impressions
The final pivot is presented below:
Answer: D
Explanation:
Given that the 'Color' field is extracted from the 'Creative Name' field and appears to be part of the creative-level data, the most logical method would be to create an EXTRACT formula and map it to a Creative custom attribute. This allows the 'Color' value to be associated directly with each creative entry. In Salesforce Marketing Cloud Intelligence, the EXTRACT formula can be used to parse and segment text strings within a field, and this process is used for harmonizing data by creating new dimensions or attributes based on existing data, which is what's described here. This answer is consistent with Salesforce Marketing Cloud Intelligence features that enable data transformation and harmonization through formulaic mapping, as per the official Salesforce documentation on data harmonization and transformation.
NEW QUESTION # 48
What is the relationship between "Media Buy Key" and "Campaign Key"?
Answer: C
Explanation:
Typically, 'Campaign Key' is a unique identifier for a specific marketing campaign, while 'Media Buy Key' refers to the purchases of advertising space associated with that campaign. A campaign can have multiple media buys, so the relationship is many-to-one, with many media buys (Media Buy Keys) associated with a single campaign (Campaign Key).
NEW QUESTION # 49
Which three entities and/or functions can be used in an expression when building a calculated dimension?
Answer: A,C,D
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 is requested to apply the following logic:
To apply the above logic, the engineer used only the Harmonization Center, without any mapping manipulations. What is the minimum amount of Patterns creating both 'Platform' and 'Line of Business'?"
Answer: C
Explanation:
To create both 'Platform' and 'Line of Business' fields using Patterns in the Harmonization Center without mapping manipulations, the engineer would need to create separate patterns for each data source mentioned. According to the provided images:
One pattern for LinkedIn Ads, to extract the 'Campaign Name' at position 4 for the Platform and 'Media Buy Name' at position 7 for Line of Business.
One pattern for AdRoll, to extract 'Media Buy Name' at position 3 for Platform and at position 2 for Line of Business.
One pattern for Google Analytics, which seems not required for the Platform but could apply if the Line of Business extraction is necessary, although it states N/A.
Hence, a minimum of 3 patterns would be necessary to create the fields required.
NEW QUESTION # 51
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: A
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 # 52
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