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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Act on Data / Activation | 18% | - Activation types and use cases - Data actions and integration with Salesforce products - Troubleshooting activation issues and dependencies |
| Topic 2: Setup and Administration | 13% | - Permissions, settings, and security configuration - Data streams, data spaces, and governance - Monitoring, reporting, and tools (Data Explorer, APIs) |
| Topic 3: Solution Positioning / Overview | 14% | - Data 360 / Data Cloud terminology and business value - Role in AI and predictive capabilities - Common use cases and data ethics principles |
| Topic 4: Segmentation and Insights | 18% | - Segment creation, configuration, and maintenance - AI-powered insights and analysis - Calculated vs streaming insights |
| Topic 5: Identity Resolution and Harmonization | 14% | - Identity graphs and data unification - Reconciliation and result analysis - Matching rules (deterministic / probabilistic) |
| Topic 6: Data Ingestion and Modeling | 20% | - Ingestion methods, connectors, and transformation - Customer 360 data model and schema design - Data mapping, validation, and quality best practices |
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NEW QUESTION # 28
A Data 360 Consultant has been asked to help a customer implement Data 360 to improve their customer experience. They have identified four potential use cases. Which scenario represents the most appropriate initial use case based on Salesforce implementation best practices?
Answer: A
Explanation:
The core Data 360 principle is harmonization: bring data from multiple systems into a governed model that business teams can use consistently. Consolidating three systems (Sales, Service, and Marketing Cloud) to provide a " Single View of the Customer " for high-tier support agents is the strongest answer because Data
360 is designed to unify, harmonize, and activate customer and business data across systems. The platform is not merely a dashboard, archive, or point solution. 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.
NEW QUESTION # 29
How does Data Cloud ensure high availability and fault tolerance for customer data?
Answer: D
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.
NEW QUESTION # 30
A company stores customer data in Marketing Cloud and uses the Marketing Cloud Connector to ingest data into Data 360. Where does a request for Data Deletion or Right to Be Forgotten get submitted?
Answer: D
Explanation:
The governance lens is least privilege, purpose limitation, and honoring privacy operations against the individual profile. Through Consent API supports the governance requirement because Data 360 implementations should minimize unnecessary access, avoid over-collection, and process deletion or consent requests at the profile level where Salesforce expects them. The safest design is explicit, auditable, and limited to the business purpose. 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 # 31
A consultant is troubleshooting a segment error.
Which error message is solved by using calculated insights Instead of nested segments?
Answer: B
Explanation:
* Segment Errors in Data Cloud: Segments in Salesforce Data Cloud can encounter errors due to various reasons, including complexity and nested segments.
* Calculated Insights vs. Nested Segments:
Complex Segments: If a segment is too complex due to extensive nesting or numerous conditions, it can lead to errors.
Simplification with Calculated Insights: Using calculated insights can simplify segment creation by pre-computing and storing complex logic or aggregations, which can then be referenced directly in the segment.
* Solution:
Step 1: Identify the segment causing the "Segment is too complex" error.
Step 2: Break down complex logic into calculated insights.
Step 3: Use these calculated insights in segment definitions to reduce complexity.
* Reference:
Salesforce Data Cloud Calculated Insights
Salesforce Data Cloud Segment Creation
NEW QUESTION # 32
A consultant wants to build a new audience in Data Cloud.
Which three criteria can the consultant include when building a segment?
Choose 3 answers
Answer: A,B,D
Explanation:
Explanation
A segment is a subset of individuals who meet certain criteria based on their attributes and behaviors. A consultant can use different types of criteria when building a segment in Data Cloud, such as:
* Direct attributes: These are attributes that describe the characteristics of an individual, such as name, email, gender, age, etc. These attributes are stored in the Profile data model object (DMO) and can be used to filter individuals based on their profile data.
* Calculated Insights: These are insights that perform calculations on data in a data space and store the results in a data extension. These insights can be used to segment individuals based on metrics or scores derived from their data, such as customer lifetime value, churn risk, loyalty tier, etc.
* Related attributes: These are attributes that describe the relationships of an individual with other DMOs,
* such as Email, Engagement, Order, Product, etc. These attributes can be used to segment individuals based on their interactions or transactions with different entities, such as email opens, clicks, purchases, etc.
The other two options are not valid criteria for building a segment in Data Cloud. Data stream attributes are attributes that describe the streaming data that is ingested into Data Cloud from various sources, such as Marketing Cloud, Commerce Cloud, Service Cloud, etc. These attributes are not directly available for segmentation, but they can be transformed and stored in data extensions using streaming data transforms.
Streaming insights are insights that analyze streaming data in real time and trigger actions based on predefined conditions. These insights are not used for segmentation, but for activation and personalization. References: Create a Segment in Data Cloud, Use Insights in Data Cloud, Data Cloud Data Model
NEW QUESTION # 33
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