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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Cloud Monitoring and Troubleshooting | 22% | - Use Data Cloud analytics and reports - Monitor data ingestion and sync status - Manage data retention and cleanup - Debug common configuration errors - Troubleshoot data quality issues - Analyze identity resolution results |
| Topic 2: Data Model Setup and Configuration | 15% | - Configure custom objects, fields, and relationships - Describe Harmonized Data Model concepts - Map source data to harmonized data model - Set up and configure Data Lake objects - Understand data types and field mapping |
| Topic 3: Data Sharing and Security | 8% | - Manage user access and permissions - Implement row-level security - Configure data sharing settings - Understand data governance requirements |
| Topic 4: Identity Resolution and Data Unification | 17% | - Configure match rules and strategies - Configure identity resolution rules - Configure contact and account matching - Manage data overlap and deduplication - Understand unified records creation |
| Topic 5: Calculated Insights | 8% | - Define metrics and measurements - Publish insights to Data Cloud - Use formulas and functions in calculations - Create and configure calculated insights |
| Topic 6: 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 7: Data Actions | 8% | - Create and manage data bundles - Set up segmentation and target audiences - Configure data actions for activation - Configure data sync to external systems |
| Topic 8: Data Ingestion | 15% | - Set up and manage data connectors - Configure streaming data ingestion - Configure data sync and refresh schedules - Troubleshoot ingestion issues - Configure batch data ingestion |
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NEW QUESTION # 92
During discovery, which feature should a consultant highlight for a customer who has multiple data sources and needs to match and reconcile data about individuals into a single unified profile?
Answer: B
Explanation:
Identity resolution is the feature that allows Data Cloud to match and reconcile data about individuals from multiple data sources into a single unified profile. Identity resolution uses rulesets to define how source profiles are matched and consolidated based on common attributes, such as name, email, phone, or party identifier. Identity resolution enables Data Cloud to create a 360-degree view of each customer across different data sources and systems12. The other options are not the best features to highlight for this customer need because:
* A. Data cleansing is the process of detecting and correcting errors or inconsistencies in data, such as duplicates, missing values, or invalid formats. Data cleansing can improve the quality and accuracy of data, but it does not match or reconcile data across different data sources3.
* B. Harmonization is the process of standardizing and transforming data from different sources into a common format and structure. Harmonization can enable data integration and interoperability, but it does not match or reconcile data across different data sources4.
* C. Data consolidation is the process of combining data from different sources into a single data set or system. Data consolidation can reduce data redundancy and complexity, but it does not match or reconcile data across different data sources5. References: 1: Data and Identity in Data Cloud | Salesforce Trailhead, 2: Data Cloud Identiy Resolution | Salesforce AI Research, 3: [Data Cleansing - Salesforce], 4: [Harmonization - Salesforce], 5: [Data Consolidation - Salesforce]
NEW QUESTION # 93
How does Data Cloud ensure high availability and fault tolerance for customer data?
Answer: A
Explanation:
* Ensuring High Availability and Fault Tolerance:
High availability refers to systems that are continuously operational and accessible, while fault tolerance is the ability to continue functioning in the event of a failure.
Reference:
* Data Distribution Across Multiple Regions and Data Centers:
Salesforce Data Cloud ensures high availability by replicating data across multiple geographic regions and data centers. This distribution mitigates risks associated with localized failures.
If one data center goes down, data and services can continue to be served from another location, ensuring uninterrupted service.
* Benefits of Regional Data Distribution:
Redundancy: Having multiple copies of data across regions provides redundancy, which is critical for disaster recovery.
Load Balancing: Traffic can be distributed across data centers to optimize performance and reduce latency.
Regulatory Compliance: Storing data in different regions helps meet local data residency requirements.
* Implementation in Salesforce Data Cloud:
Salesforce utilizes a robust architecture involving data replication and failover mechanisms to maintain data integrity and availability.
This architecture ensures that even in the event of a regional outage, customer data remains secure and accessible.
NEW QUESTION # 94
A Data 360 Consultant has set up an identity resolution ruleset for their client ' s Data 360 implementation and now wants to confirm the results. Which two features should the consultant use to validate the data on a unified profile?
Answer: B
Explanation:
The identity logic is about linking source profiles safely, then choosing the best surviving values for the unified profile. Profile ExplorerandQuery Editor is appropriate because identity resolution needs reliable match inputs, qualified identifiers, and controlled reconciliation. It is not just deduplication; it is a rules-driven process that connects source records into a trusted unified profile. The distractors fall short because they either move the problem into the wrong system, add needless duplication, ignore Data 360 object relationships, or rely on a feature built for a different lifecycle stage. In a real implementation, those choices usually create brittle pipelines, stale data, security exposure, or segments that look correct on paper but fail when activated. Thinking like an architect, the selected option places the logic where Data 360 can govern it and reuse it reliably. This is the nuance exam questions often test: the platform capability must match both the technical layer and the business timing requirement, not just sound related to data.
NEW QUESTION # 95
A consultant needs to create a data graph based on several DLOs,
Which step should the consultant take to make this work?
Answer: D
NEW QUESTION # 96
A retailer wants to unify profiles using Loyalty ID which is different than the unique ID of their customers.
Which object should the consultant use in identity resolution to perform exact match rules on the Loyalty ID?
Answer: D
Explanation:
The Party Identification object is the correct object to use in identity resolution to perform exact match rules on the Loyalty ID. The Party Identification object is a child object of the Individual object that stores different types of identifiers for an individual, such as email, phone, loyalty ID, social media handle, etc. Each identifier has a type, a value, and a source. The consultant can use the Party Identification object to create a match rule that compares the Loyalty ID type and value across different sources and links the corresponding individuals.
The other options are not correct objects to use in identity resolution to perform exact match rules on the Loyalty ID. The Loyalty Identification object does not exist in Data Cloud. The Individual object is the parent object that represents a unified profile of an individual, but it does not store the Loyalty ID directly. The Contact Identification object is a child object of the Contact object that stores identifiers for a contact, such as email, phone, etc., but it does not store the Loyalty ID.
Data Modeling Requirements for Identity Resolution
Identity Resolution in a Data Space
Configure Identity Resolution Rulesets
Map Required Objects
Data and Identity in Data Cloud
NEW QUESTION # 97
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