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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Act on Data | 18% | - Data actions and requirements - Activation concepts and use cases - Troubleshooting activation issues - Attributes, related attributes, and timing dependencies |
| Topic 2: Segmentation and Insights | 18% | - Calculated vs. streaming insights - Segment creation, maintenance, and membership analysis - Segmentation concepts and business scenarios |
| Topic 3: Data Ingestion and Modeling | 20% | - Transformation and data mapping capabilities - Data modeling best practices aligned with identity resolution - Ingestion from various source systems - Validation and inspection of ingested data |
| Topic 4: Identity Resolution | 14% | - Unified identity outcomes and use cases - Reconciliation rules and data unification - Matching rules and application |
| Topic 5: Data Cloud Overview | 18% | - Data ethics and responsible usage principles - Core functions, terminology, and business value - Common enterprise use cases - Architecture, dependencies, and lifecycle |
| Topic 6: Data Cloud Setup and Administration | 12% | - Data streams, data bundles, and data spaces - Permissions, permission sets, and org settings - Data exploration and diagnostics: Explorer tools, APIs - Administration tools: reports, dashboards, flows, packaging |
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NEW QUESTION # 142
Which two requirements must be met for a calculated insight to appear in the segmentation canvas?
Choose 2 answers
Answer: A,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 # 143
A customer wants to create segments of users based on their Customer Lifetime Value.
However, the source data that will be brought into Data Cloud does not include that key performance indicator (KPI).
Which sequence of steps should the consultant follow to achieve this requirement?
Answer: A
Explanation:
To create segments of users based on their Customer Lifetime Value (CLV), the sequence of steps that the consultant should follow is Ingest Data > Map Data to Data Model > Create Calculated Insight > Use in Segmentation. This is because the first step is to ingest the source data into Data Cloud using data streams1. The second step is to map the source data to the data model, which defines the structure and attributes of the data2. The third step is to create a calculated insight, which is a derived attribute that is computed based on the source or unified data3. In this case, the calculated insight would be the CLV, which can be calculated using a formula or a query based on the sales order data4. The fourth step is to use the calculated insight in segmentation, which is the process of creating groups of individuals or entities based on their attributes and behaviors. By using the CLV calculated insight, the consultant can segment the users by their predicted revenue from the lifespan of their relationship with the brand. The other options are incorrect because they do not follow the correct sequence of steps to achieve the requirement. Option B is incorrect because it is not possible to create a calculated insight before ingesting and mapping the data, as the calculated insight depends on the data model objects3. Option C is incorrect because it is not possible to create a calculated insight before mapping the data, as the calculated insight depends on the data model objects3. Option D is incorrect because it is not recommended to create a calculated insight before mapping the data, as the calculated insight may not reflect the correct data model structure and attributes3. References: Data Streams Overview, Data Model Objects Overview, Calculated Insights Overview, Calculating Customer Lifetime Value (CLV) With Salesforce, [Segmentation Overview]
NEW QUESTION # 144
Which functionality does Data Cloud offer to improve customer support interactions when a customer is working with an agent?
Answer: C
Explanation:
Customer Support in Salesforce Data Cloud: One of the key benefits of Salesforce Data Cloud is its ability to enhance customer support by providing comprehensive and real-time customer data.
Real-Time Data Integration: This functionality allows customer support agents to access the most up-to-date customer information, improving their ability to respond to customer inquiries and issues effectively.
Benefits for Customer Support:
Immediate Access: Agents have real-time access to customer interactions and data, ensuring they can provide accurate and timely support.
Contextual Information: The integrated data provides a holistic view of the customer's history and preferences, allowing for more personalized support interactions.
Use Case: When a customer contacts support, the agent can see real-time updates on recent purchases, interactions, and any ongoing issues, enabling them to resolve queries quickly and efficiently.
References:
Salesforce Data Cloud for Customer Support
Real-Time Data Integration in Salesforce
NEW QUESTION # 145
A consultant needs to package Data Cloud components from one
organization to another.
Which two Data Cloud components should the consultant include in a
data kit to achieve this goal?
Choose 2 answers
Answer: A,B
Explanation:
To package Data Cloud components from one organization to another, the consultant should include the following components in a data kit:
Data model objects: These are the custom objects that define the data model for Data Cloud, such as Individual, Segment, Activity, etc. They store the data ingested from various sources and enable the creation of unified profiles and segments1.
Identity resolution rulesets: These are the rules that determine how data from different sources are matched and merged to create unified profiles. They specify the criteria, logic, and priority for identity resolution2. References:
1: Data Model Objects in Data Cloud
2: Identity Resolution Rulesets in Data Cloud
NEW QUESTION # 146
Which consideration related to the way Data Cloud ingests CRM data is true?
Answer: C
Explanation:
The correct answer is D. The CRM Connector allows standard fields to stream into Data Cloud in real time.
This means that any changes to the standard fields in the CRM data source are reflected in Data Cloud almost instantly, without waiting for the next scheduled synchronization. This feature enables Data Cloud to have the most up-to-date and accurate CRM data for segmentation and activation1.
The other options are incorrect for the following reasons:
A). CRM data can be manually refreshed at any time by clicking the Refresh button on the data stream detail page2. This option is false.
B). The CRM Connector's synchronization times can be customized to up to 60-minute intervals, not 15- minute intervals3. This option is false.
C). Formula fields are not refreshed at regular sync intervals, but only at the next full refresh4. A full refresh is a complete data ingestion process that occurs once every 24 hours or when manually triggered. This option is false.
1: Connect and Ingest Data in Data Cloud article on Salesforce Help
2: Data Sources in Data Cloud unit on Trailhead
3: Data Cloud for Admins module on Trailhead
4: [Formula Fields in Data Cloud] unit on Trailhead
[Data Streams in Data Cloud] unit on Trailhead
NEW QUESTION # 147
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