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| Section | Weight | Objectives |
|---|---|---|
| Data Cloud Overview | 18% | - Business value and use cases - Architecture and ecosystem - Core concepts and terminology |
| Data Ingestion and Modeling | 20% | - Data validation and inspection - Data modeling best practices - Ingestion from various sources - Transformation and mapping |
| Identity Resolution | 14% | - Conflict resolution - Matching and reconciliation rules - Identity use cases - Unified profile creation |
| Segmentation and Insights | 18% | - Calculated vs streaming insights - Insight implementation - Segment creation and configuration - Segment membership analysis |
| Act on Data | 18% | - Process automation and triggering - Troubleshooting activations - Activation concepts and use cases - Attribute usage and management |
| Data Cloud Setup and Administration | 12% | - Data spaces and configuration - Permissions and access control - Monitoring and maintenance tools - Data streams and data bundles |
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NEW QUESTION # 145
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: B
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 # 146
Northern Trail Qutfitters wants to be able to calculate each customer's lifetime value {LTV) but also create breakdowns of the revenue sourced by website, mobile app, and retail channels.
What should a consultant use to address this use case in Data Cloud?
Answer: D
Explanation:
Metrics on metrics is a feature that allows creating new metrics based on existing metrics and applying mathematical operations on them. This can be useful for calculating complex business metrics such as LTV, ROI, or conversion rates. In this case, the consultant can use metrics on metrics to calculate the LTV of each customer by summing up the revenue generated by them across different channels. The consultant can also create breakdowns of the revenue by channel by using the channel attribute as a dimension in the metric definition. References: Metrics on Metrics, Create Metrics on Metrics
NEW QUESTION # 147
Which two characteristics describe the Customer 360 data model?
Answer: A,D
Explanation:
The Customer 360 data model is a conceptual model and a canonical model. A conceptual model is a high-level representation of the entities and relationships in a domain of interest, without specifying any implementation details. A canonical model is a common and consistent data model that can be used across different systems and applications. Reference: [Customer 360 Data Model Overview], [Conceptual Model], [Canonical Model]
NEW QUESTION # 148
The leadership team atCumulus Financial has declared that customers who have deposited more than
$250,000 in the last 5 years and who are not using advisory services, will be the central focus for all new campaigns in the next year. Which two features support this need?
Answer: B,C
Explanation:
These two features support the need to calculate each customer's lifetime value (LTV) and create breakdowns of the revenue sourced by different channels. Calculated Insight allows you to create complex calculations based on stored data, such as LTV. Segment allows you to create audiences based on different criteria, such as revenue source. References: https://help.salesforce.com/s/articleView?id=sf.c360_a_calculated_insights.
htm&type=5https://help.salesforce.com/s/articleView?id=sf.c360_a_segmentation.htm&type=5
NEW QUESTION # 149
Which functionality does Data Cloud offer to improve customer support interactions when a customer is working with an agent?
Answer: B
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 # 150
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