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| Section | Objectives |
|---|---|
| Salesforce Data Cloud Fundamentals | - Data Cloud value and use cases - Core concepts and architecture |
| Data Governance and Privacy | - Data privacy and compliance concepts - Security and data access controls |
| Segmentation and Activation | - Segmentation logic and audience building - Activation to Salesforce and external systems |
| Data Ingestion and Data Modeling | - Data sources and ingestion methods - Data model objects and harmonization |
| Identity Resolution and Unification | - Identity resolution rules - Unified customer profile concepts |
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NEW QUESTION # 73
What can be customized in the Data Cloud canonical model?
Answer: A
Explanation:
Explanation
You can customize the Data Cloud canonical model by adding, editing, or deleting objects, fields, and relationships. You can also modify the properties, labels, and descriptions of these components.
References:https://help.salesforce.com/s/articleView?id=sf.c360_a_data_cloud_canonical_model.htm&type=5
NEW QUESTION # 74
Which information is provided in a .csv file when activating to Amazon S3?
Answer: D
Explanation:
When activating to Amazon S3, the information that is provided in a .csv file is the activated data payload. The activated data payload is the data that is sent from Data Cloud to the activation target, which in this case is an Amazon S3 bucket1. The activated data payload contains the attributes and values of the individuals or entities that are included in the segment that is being activated2. The activated data payload can be used for various purposes, such as marketing, sales, service, or analytics3. The other options are incorrect because they are not provided in a .csv file when activating to Amazon S3. Option A is incorrect because an audit log is not provided in a .csv file, but it can be viewed in the Data Cloud UI under the Activation History tab4. Option C is incorrect because the metadata regarding the segment definition is not provided in a .csv file, but it can be viewed in the Data Cloud UI under the Segmentation tab5. Option D is incorrect because the manifest of origin sources within Data Cloud is not provided in a .csv file, but it can be viewed in the Data Cloud UI under the Data Sources tab. References: Data Activation Overview, Create and Activate Segments in Data Cloud, Data Activation Use Cases, View Activation History, Segmentation Overview, [Data Sources Overview]
NEW QUESTION # 75
Which two requirements must be met for a calculated insight to appear in the segmentation canvas?
Choose 2 answers
Answer: B,D
Explanation:
A calculated insight is a custom metric or measure that is derived from one or more data model objects or data lake objects in Data Cloud. A calculated insight can be used in segmentation to filter or group the data based on the calculated value. However, not all calculated insights can appear in the segmentation canvas. There are two requirements that must be met for a calculated insight to appear in the segmentation canvas:
The calculated insight must contain a dimension including the Individual or Unified Individual Id. A dimension is a field that can be used to categorize or group the data, such as name, gender, or location. The Individual or Unified Individual Id is a unique identifier for each individual profile in Data Cloud. The calculated insight must include this dimension to link the calculated value to the individual profile and to enable segmentation based on the individual profile attributes.
The primary key of the segmented table must be a dimension in the calculated insight. The primary key is a field that uniquely identifies each record in a table. The segmented table is the table that contains the data that is being segmented, such as the Customer or the Order table. The calculated insight must include the primary key of the segmented table as a dimension to ensure that the calculated value is associated with the correct record in the segmented table and to avoid duplication or inconsistency in the segmentation results.
Create a Calculated Insight, Use Insights in Data Cloud, Segmentation
NEW QUESTION # 76
What happens if no file name is specified in AWS S3 data stream during ingestion?
Answer: A
Explanation:
If no file name is specified in AWS S3 data stream during ingestion, the system does not fetch any file and the data stream shows an error. AWS S3 data stream is a feature that allows you to stream data from Amazon Web Services Simple Storage Service (AWS S3) to Data Cloud in near real time. You need to specify the file name or prefix of the files that you want to ingest from your S3 bucket. If you leave this field blank, the system cannot find any matching files and returns an error message. Reference: AWS S3 Data Stream
NEW QUESTION # 77
Which type of measures with aggregate functions are supported?
Answer: C
Explanation:
Numeric is the only type of measures that support aggregate functions. Measures are numeric fields that can be aggregated using functions such as SUM, AVG, MIN, MAX, or COUNT. For example, you can use a measure to calculate the total revenue or the average order value from your data. Date, Text, and Boolean are not types of measures, but types of dimensions. Dimensions are fields that can be used to group or filter data, but not to perform calculations. References: Measures andDimensions
NEW QUESTION # 78
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