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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Identity Resolution | 14% | - Conflict resolution - Unified profile creation - Matching and reconciliation rules - Identity use cases |
| Topic 2: Data Cloud Overview | 18% | - Architecture and ecosystem - Core concepts and terminology - Business value and use cases |
| Topic 3: Data Cloud Setup and Administration | 12% | - Data spaces and configuration - Permissions and access control - Data streams and data bundles - Monitoring and maintenance tools |
| Topic 4: Act on Data | 18% | - Activation concepts and use cases - Process automation and triggering - Troubleshooting activations - Attribute usage and management |
| Topic 5: Segmentation and Insights | 18% | - Insight implementation - Calculated vs streaming insights - Segment membership analysis - Segment creation and configuration |
| Topic 6: Data Ingestion and Modeling | 20% | - Data modeling best practices - Data validation and inspection - Ingestion from various sources - Transformation and mapping |
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NEW QUESTION # 66
Cumulus Financial wants to be able to track the daily transaction volume of each of its customers in real time and send out a notification as soon as it detects volume outside a customer's normal range.
What should a consultant do to accommodate this request?
Answer: B
Explanation:
A streaming insight is a type of insight that analyzes streaming data in real time and triggers actions based on predefined conditions. A data action is a type of action that executes a flow, a data action target, or a data action script when an insight is triggered. By using a streaming insight paired with a data action, a consultant can accommodate Cumulus Financial's request to track the daily transaction volume of each customer and send out a notification when the volume is outside the normal range. A calculated insight is a type of insight that performs calculations on data in a data space and stores the results in a data extension. A streaming data transform is a type of data transform that applies transformations to streaming data in real time and stores the results in a data extension. A flow is a type of automation that executes a series of actions when triggered by an event, a schedule, or another flow. None of these options can achieve the same functionality as a streaming insight paired with a data action. References: Use Insights in Data Cloud Unit, Streaming Insights and Data Actions Use Cases, Streaming Insights and Data Actions Limits and Behaviors
NEW QUESTION # 67
A bank collects customer data for its loan applicants and high net worth customers. A customer can be both a load applicant and a high net worth customer, resulting in duplicate data.
How should a consultant ingest and map this data in Data Cloud?
Answer: A
Explanation:
To handle duplicate data for customers who are both loan applicants and high net worth individuals, the consultant should ingest the data into two separate Data Lake Objects (DLOs) and map them to the Individual and Contact Point Email Data Model Objects (DMOs). Here's why and how this works:
Understanding the Problem :
Customers may exist in both datasets (loan applicants and high net worth individuals), leading to potential duplication.
To avoid redundancy while maintaining data integrity, the data must be ingested and mapped carefully.
Why Two DLOs?
By ingesting the data into two DLOs, you can maintain separation between the two datasets while still leveraging shared attributes (e.g., email addresses).
Mapping both DLOs to the Individual and Contact Point Email DMOs ensures that identity resolution can consolidate duplicate records based on shared identifiers like email.
Steps to Implement This Solution :
Step 1: Create two DLOs-one for loan applicants and another for high net worth customers.
Step 2: Map both DLOs to the Individual DMO to consolidate customer profiles.
Step 3: Map the email fields from both DLOs to the Contact Point Email DMO to enable identity resolution based on email addresses.
Step 4: Configure identity resolution rules to merge duplicate records based on shared attributes like email.
Why Not Other Options?
A). Use a data transform to consolidate the data into one DLO: Consolidating into a single DLO before mapping would lose the distinction between the two datasets and make it harder to manage updates or changes.
C). Ingest the data into two DLOs and then map to two custom DMOs: Creating custom DMOs is unnecessary complexity when the standard Individual and Contact Point Email DMOs can handle this scenario.
D). Ingest the data into one DLO and then map to one custom DMO: Using a single DLO would result in data loss or confusion, as the distinction between loan applicants and high net worth customers would be lost.
By using two DLOs and mapping them to the standard DMOs, the consultant ensures clean data ingestion and effective identity resolution.
NEW QUESTION # 68
Which three Data Model objects do Reconciliation Rules operate across?
Answer: A,C,D
Explanation:
These are the three Data Model objectsthat Reconciliation Rules operate across. Reconciliation rules tell Data Cloud how to prioritize values for unified individual profiles when there is conflicting information in the data model. For example, if two profiles have different email addresses, reconciliation rules can determine which one to use for the unified profile. Reconciliation rules can operate on the Individual object, which represents a person or entity, the Party Identification object, which represents an identifier such as a social media handle or a loyalty card number, and the Contact Point Email object, which represents an email address.
References: Identity Resolution Reconciliation Rules
NEW QUESTION # 69
Cloud Kicks wants to be able to build a segment of customers who have visited its website within the previous 7 days.
Which filter operator on the Engagement Date field fits this use case?
Answer: C
Explanation:
The filter operator Last Number of Days allows you to filter on date fields using a relative date range that specifies the number of days before today. For example, you can use this operator to filter on customers who have visited your website in the last 7 days, or the last 30 days, or any number of days you want. This operator is useful for creating dynamic segments that update automatically based on the current date12. References:
Relative Date Filter Reference
Create Filtered Segments
NEW QUESTION # 70
Which permission setting should an administrator check if the custom CRM object is not available in New Data Stream configuration?
Answer: A
Explanation:
This permission setting should be checked if the custom CRM object is not available in New Data Stream configuration. The user who connects the Salesforce CRM connector must have View All permission for the custom object in the source CRM org. Reference: https://help.salesforce.com/s/articleView?id=sf.c360_a_data_cloud_salesforce_crm.htm&type=5
NEW QUESTION # 71
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