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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Identity Resolution and Data Unification | 17% | - Manage data overlap and deduplication - Configure match rules and strategies - Understand unified records creation - Configure contact and account matching - Configure identity resolution rules |
| Topic 2: Data Actions | 8% | - Configure data sync to external systems - Create and manage data bundles - Configure data actions for activation - Set up segmentation and target audiences |
| Topic 3: Data Sharing and Security | 8% | - Manage user access and permissions - Configure data sharing settings - Understand data governance requirements - Implement row-level security |
| Topic 4: Data Cloud Overview and Licensing | 7% | - Describe the business value of Data Cloud - Describe Data Cloud setup and configuration - Explain licensing model and capacity |
| Topic 5: Data Ingestion | 15% | - Configure batch data ingestion - Troubleshoot ingestion issues - Configure streaming data ingestion - Set up and manage data connectors - Configure data sync and refresh schedules |
| Topic 6: Calculated Insights | 8% | - Publish insights to Data Cloud - Create and configure calculated insights - Use formulas and functions in calculations - Define metrics and measurements |
| Topic 7: Data Model Setup and Configuration | 15% | - Describe Harmonized Data Model concepts - Set up and configure Data Lake objects - Configure custom objects, fields, and relationships - Map source data to harmonized data model - Understand data types and field mapping |
| Topic 8: Data Cloud Monitoring and Troubleshooting | 22% | - Troubleshoot data quality issues - Use Data Cloud analytics and reports - Analyze identity resolution results - Debug common configuration errors - Manage data retention and cleanup - Monitor data ingestion and sync status |
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NEW QUESTION # 53
Which two common use cases can be addressed with Data Cloud?
Choose 2 answers
Answer: A,D
Explanation:
recovery.
Explanation:
Data Cloud is a data platform that can help customers connect, prepare, harmonize, unify, query, analyze, and act on their data across various Salesforce and external sources. Some of the common use cases that can be addressed with Data Cloud are:
Understand and act upon customer data to drive more relevant experiences. Data Cloud can help customers gain a 360-degree view of their customers by unifying data from different sources and resolving identities across channels. Data Cloud can also help customers segment their audiences, create personalized experiences, and activate data in any channel using insights and AI.
Harmonize data from multiple sources with a standardized and extendable data model. Data Cloud can help customers transform and cleanse their data before using it, and map it to a common data model that can be extended and customized. Data Cloud can also help customers create calculated insights and related attributes to enrich their data and optimize identity resolution.
The other two options are not common use cases for Data Cloud. Data Cloud does not provide data governance or backup and disaster recovery features, as these are typically handled by other Salesforce or external solutions.
Reference:
Learn How Data Cloud Works
About Salesforce Data Cloud
Discover Use Cases for the Platform
Understand Common Data Analysis Use Cases
NEW QUESTION # 54
An organization wants to enable users with the ability to identify and select text attributes from a picklist of options.
Which Data Cloud feature should help with this use case?
Answer: C
Explanation:
Value suggestion is a Data Cloud feature that allows users to see and select the possible values for a text field when creating segment filters. Value suggestion can be enabled or disabled for each data model object (DMO) field in the DMO record home. Value suggestion can help users to identify and select text attributes from a picklist of options, without having to type or remember the exact values. Value suggestion can also reduce errors and improve data quality by ensuring consistent and valid values for the segment filters. References: Use Value Suggestions in Segmentation, Considerations for Selecting Related Attributes
NEW QUESTION # 55
A Data Cloud consultant is evaluating the initial phase of the Data Cloud lifecycle for a company.
Which action is essential to effectively begin the Data Cloud lifecycle?
Answer: D
Explanation:
* Data Cloud Lifecycle: The initial phase of the Salesforce Data Cloud lifecycle is critical for setting the foundation for successful data integration and utilization.
* Identifying Use Cases:
Importance: Defining clear use cases helps in understanding the business objectives and how Data Cloud can address them.
Required Data Sources: Identifying the necessary data sources ensures that relevant data is ingested into Data Cloud.
Data Quality: Assessing data quality is essential for accurate and reliable data analysis and insights.
* Actions:
Step 1: Engage with stakeholders to define specific use cases for Data Cloud.
Step 2: Identify and catalog the required data sources for these use cases.
Step 3: Evaluate the quality of data from these sources to ensure they meet the standards for effective data analysis.
* Reference:
Salesforce Data Cloud Implementation Guide
Salesforce Data Cloud Lifecycle
NEW QUESTION # 56
Northern Trail Outfitters (NTD) creates a calculated insight to compute recency, frequency, monetary {RFM) scores on its unified individuals. NTO then creates a segment based on these scores that it activates to a Marketing Cloud activation target.
Which two actions are required when configuring the activation?
Choose 2 answers
Answer: B,C
Explanation:
To configure an activation to a Marketing Cloud activation target, you need to choose a segment and select contact points. Choosing a segment allows you to specify which unified individuals you want to activate. Selecting contact points allows you to map the attributes from the segment to the fields in the Marketing Cloud data extension. You do not need to add additional attributes or add the calculated insight in the activation, as these are already part of the segment definition. Reference: Create a Marketing Cloud Activation Target; Types of Data Targets in Data Cloud
NEW QUESTION # 57
Which consideration related to the way Data Cloud ingests CRM data is true?
Answer: D
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.
References:
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
5: [Data Streams in Data Cloud] unit on Trailhead
NEW QUESTION # 58
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