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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Ingestion | 15% | - Configure data sync and refresh schedules - Set up and manage data connectors - Troubleshoot ingestion issues - Configure streaming data ingestion - Configure batch data ingestion |
| Topic 2: Data Cloud Overview and Licensing | 7% | - Describe Data Cloud setup and configuration - Explain licensing model and capacity - Describe the business value of Data Cloud |
| Topic 3: Data Cloud Monitoring and Troubleshooting | 22% | - Use Data Cloud analytics and reports - Analyze identity resolution results - Monitor data ingestion and sync status - Manage data retention and cleanup - Troubleshoot data quality issues - Debug common configuration errors |
| Topic 4: Data Sharing and Security | 8% | - Understand data governance requirements - Implement row-level security - Manage user access and permissions - Configure data sharing settings |
| Topic 5: 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 6: Identity Resolution and Data Unification | 17% | - Understand unified records creation - Configure contact and account matching - Manage data overlap and deduplication - Configure identity resolution rules - Configure match rules and strategies |
| Topic 7: Data Actions | 8% | - Set up segmentation and target audiences - Create and manage data bundles - Configure data actions for activation - Configure data sync to external systems |
| Topic 8: Calculated Insights | 8% | - Define metrics and measurements - Create and configure calculated insights - Publish insights to Data Cloud - Use formulas and functions in calculations |
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NEW QUESTION # 13
A Data 360 Consultant noticed a recent increase in a customer ' s consolidation rate. Which change to the identity resolution ruleset typically causes this result?
Answer: C
Explanation:
The identity logic is about linking source profiles safely, then choosing the best surviving values for the unified profile. Modifying a ruleset that increases the number of matched profiles 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 # 14
A customer notices that their consolidation rate is low across their account unification. They have mapped Account to the Individual and Contact Point Email DMOs.
What should they do to increase their consolidation rate?
Answer: C
Explanation:
Consolidation Rate: The consolidation rate in Salesforce Data Cloud refers to the effectiveness of unifying records into a single profile. A low consolidation rate indicates that many records are not being successfully unified.
Matching Rules: Matching rules are critical in the identity resolution process. They define the criteria for identifying and merging duplicate records.
Solution:
* Increase Matching Rules: Adding more matching rules improves the system's ability to identify duplicate records. This includes matching on additional fields or using more sophisticated matching algorithms.
* Steps:
* Access the Identity Resolution settings in Data Cloud.
* Review the current matching rules.
* Add new rules that consider more fields such as phone number, address, or other unique identifiers.
Benefits:
* Improved Unification: Higher accuracy in matching and merging records, leading to a higher consolidation rate.
* Comprehensive Profiles: Enhanced customer profiles with consolidated data from multiple sources.
References:
* Salesforce Data Cloud Identity Resolution
* Salesforce Help: Matching Rules
NEW QUESTION # 15
The recruiting team at Cumulus Financial wants to identify which candidates have browsed the jobs page on its website at least twice within the last 24 hours. They want the information about these candidates to be available for segmentation in Data Cloud and the candidates added to their recruiting system.
Which feature should a consultant recommend to achieve this goal?
Answer: D
Explanation:
A streaming insight is a feature that allows users to create and monitor real-time metrics from streaming data sources, such as web and mobile events. A streaming insight can also trigger data actions, such as sending notifications, creating records, or updating fields, based on the metric values and conditions. Therefore, a streaming insight is the best feature to achieve the goal of identifying candidates who have browsed the jobs page on the website at least twice within the last 24 hours, and adding them to the recruiting system. The other options are incorrect because:
A streaming data transform is a feature that allows users to transform and enrich streaming data using SQL expressions, such as filtering, joining, aggregating, or calculating values. However, a streaming data transform does not provide the ability to monitor metrics or trigger data actions based on conditions.
A calculated insight is a feature that allows users to define and calculate multidimensional metrics from data using SQL expressions, such as LTV, CSAT, or average order value. However, a calculated insight is not suitable for real-time data analysis, as it runs on a scheduled basis and does not support data actions.
A batch data transform is a feature that allows users to create and schedule complex data transformations using a visual editor, such as joining, aggregating, filtering, or appending data. However, a batch data transform is not suitable for real-time data analysis, as it runs on a scheduled basis and does not support data actions. References: Streaming Insights, Create a Streaming Insight, Use Insights in Data Cloud, Learn About Data Cloud Insights, Data Cloud Insights Using SQL, Streaming Data Transforms, Get Started with Batch Data Transforms in Data Cloud, Transformations for Batch Data Transforms, Batch Data Transforms in Data Cloud: Quick Look, Salesforce Data Cloud: AI CDP.
NEW QUESTION # 16
Northern Trail Outfitters uploads new customer data to an Amazon S3 bucket on a daily basis to be ingested in Data 360. Based on this, a calculated insight is created that shows the total spend per customer in the last
30 days. In which sequence should each process be run to ensure that freshly imported data is ready and available to use for any segment?
Answer: C
Explanation:
The design point is to preserve source fidelity while shaping data only where Data 360 processing needs it.
Here, Refresh Data Stream > Identity Resolution > Calculated Insight fits because it changes the shape, keying, or refresh behavior at the Data 360 layer instead of forcing the source system to carry an analytics- specific design. In production, this keeps the upstream application simpler and gives the data team a repeatable way to prepare records for mapping, identity resolution, insights, or segmentation. 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 # 17
Which data model subject area defines the revenue or quantity for an opportunity by product family?
Answer: D
Explanation:
The Sales Order subject area defines the details of an order placed by a customer for one or more products or services. It includes information such as the order date, status, amount, quantity, currency, payment method, and delivery method. The Sales Order subject area also allows you to track the revenue or quantity for an opportunity by product family, which is a grouping of products that share common characteristics or features.
For example, you can use the Sales Order Line Item DMO to associate each product in an order with its product family, and then use the Sales Order Revenue DMO to calculate the total revenue or quantity for each product family in an opportunity. References: Sales Order Subject Area, Sales Order Revenue DMO Reference
NEW QUESTION # 18
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