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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Segmentation and Insights | 18% | - Calculated vs streaming insights - Segment creation, configuration, and maintenance - AI-powered insights and analysis |
| Topic 2: Identity Resolution and Harmonization | 14% | - Reconciliation and result analysis - Matching rules (deterministic / probabilistic) - Identity graphs and data unification |
| Topic 3: Setup and Administration | 13% | - Monitoring, reporting, and tools (Data Explorer, APIs) - Permissions, settings, and security configuration - Data streams, data spaces, and governance |
| Topic 4: Act on Data / Activation | 18% | - Activation types and use cases - Troubleshooting activation issues and dependencies - Data actions and integration with Salesforce products |
| Topic 5: Data Ingestion and Modeling | 20% | - Ingestion methods, connectors, and transformation - Data mapping, validation, and quality best practices - Customer 360 data model and schema design |
| Topic 6: Solution Positioning / Overview | 14% | - Role in AI and predictive capabilities - Common use cases and data ethics principles - Data 360 / Data Cloud terminology and business value |
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NEW QUESTION # 106
A data architect is using Change Sets to move a new set of data configurations between two Salesforce Data
360 environments. The architect has created a data kit in the source org that includes several new data streams and identity resolution rules. Which component type should the architect select when adding these to the outbound change set to ensure the configuration is successfully transferred?
Answer: B
Explanation:
The lifecycle point is that Data 360 configuration is metadata, but it still has product-specific packaging and activation steps. Data Package Kit Definition is the right fit because Data 360 components are not fully usable just because generic deployment succeeded. The consultant must complete the Data 360-specific lifecycle step so streams, mappings, insights, and rules are correctly registered in the target environment. 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 # 107
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: A,B
NEW QUESTION # 108
Northern Trail Outfitters asks its consultant to extract the runner profiles and activity logs from its Track My Run mobile app and load them into Data Cloud. The marketing department also indicates that they need the last 90 days of historical data and want all new and updated data as it becomes available on a go-forward basis.
As best practice, which sequence of actions should the consultant use to implement this request?
Answer: C
Explanation:
Initial Data Load: For loading large volumes of historical data, such as the last 90 days of runner profiles and activity logs, bulk ingestion is the most efficient method. It allows for high-throughput data transfer.
* Bulk Ingestion: Use Salesforce Data Cloud's bulk ingestion tools to load the historical data quickly and efficiently.
Ongoing Data Synchronization: To keep the Data Cloud updated with new and modified records as they become available in the Track My Run mobile app, streaming ingestion is appropriate. It ensures near-real-time data updates.
* Streaming Ingestion: Configure streaming ingestion to continuously update the Data Cloud with new and updated data from the mobile app.
Sequence of Actions:
* Step 1: Perform bulk ingestion to import the last 90 days of historical data into Data Cloud.
* Step 2: Set up streaming ingestion to handle ongoing updates and new data as it becomes available.
Best Practice: This approach ensures that the initial large data load is handled efficiently, and ongoing updates are processed in near real-time, providing the marketing department with the most up-to-date data.
References:
* Salesforce Data Cloud Ingestion Methods
* Salesforce Bulk Data Ingestion
* Salesforce Streaming Data Ingestion
NEW QUESTION # 109
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 # 110
A Data 360 Consultant is configuring an activation for a segment created from the Unified Individual object to be sent to a Loyalty Cloud activation target. They would like to include the Loyalty Program Tier for personalization, but this field is stored in a separate Loyalty Program Member data model object (DMO) that was not used in the initial segment filters. Which feature should the consultant use during the activation setup to include this external identifier in the exported payload?
Answer: C
Explanation:
The segmentation and activation design starts with grain: who or what the audience represents, and which attributes must travel with it. Activation membership to select the Loyalty Program Member DMO and its associated fields works because Data 360 segmentation and activation must respect audience grain, relationship paths, and activation payload rules. A segment can qualify the audience, but activation determines which related attributes or contact points are actually sent downstream. 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 # 111
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