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| Section | Weight | Objectives |
|---|---|---|
| Identity Resolution and Data Unification | 17% | - Configure match rules and strategies - Configure contact and account matching - Understand unified records creation - Manage data overlap and deduplication - Configure identity resolution rules |
| Data Cloud Monitoring and Troubleshooting | 22% | - Manage data retention and cleanup - Use Data Cloud analytics and reports - Debug common configuration errors - Monitor data ingestion and sync status - Troubleshoot data quality issues - Analyze identity resolution results |
| Data Sharing and Security | 8% | - Understand data governance requirements - Manage user access and permissions - Implement row-level security - Configure data sharing settings |
| Data Ingestion | 15% | - Set up and manage data connectors - Configure data sync and refresh schedules - Configure batch data ingestion - Configure streaming data ingestion - Troubleshoot ingestion issues |
| Data Model Setup and Configuration | 15% | - Understand data types and field mapping - Set up and configure Data Lake objects - Map source data to harmonized data model - Describe Harmonized Data Model concepts - Configure custom objects, fields, and relationships |
| 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 |
| Calculated Insights | 8% | - Create and configure calculated insights - Publish insights to Data Cloud - Use formulas and functions in calculations - Define metrics and measurements |
| Data Cloud Overview and Licensing | 7% | - Explain licensing model and capacity - Describe the business value of Data Cloud - Describe Data Cloud setup and configuration |
>> Data-Cloud-Consultant認定テキスト <<
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質問 # 114
A Data 360 Consultant needs to build a model on Einstein Studio to predict the time- to- close of an opportunity brought into Data 360 from CRM. Based on the supported model types, which statement is true?
正解：A
解説：
The AI pattern works when the model is grounded in appropriate Data 360 data and its outputs can be operationalized safely. The consultant should use a Regression model because the target outcome is a numeric measure. fits because predictions or generative experiences are only useful when the data is representative, governed, and connected to Salesforce execution patterns such as scoring jobs, Flow, or grounded retrieval.
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.
質問 # 115
Cumulus Financial wants to be able to track the daily transaction volume of each of its customers in real time and send out anotification as soon as it detects volume outside a customer's normal range.
What should a consultant do to accommodate this request?
正解：B
解説：
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
質問 # 116
A data science team has developed a custom propensity- to- churn model in Amazon SageMaker. The company wants to use this model to score its Unified Profiles in Data 360 and use those scores for a high- priority retention segment. The Data 360 Consultant needs to ensure the data is not duplicated or moved out of Data 360 during this process. Which feature should the consultant use to integrate this model?
正解：B
解説：
The identity logic is about linking source profiles safely, then choosing the best surviving values for the unified profile. Einstein Studio Bring Your Own Model (BYOM) 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.
質問 # 117
Cumulus Financial (CF) wants to target loyal and engaged customers. When a platinum- tier customer visits their investment pages more than three times in a 24- hour period, CF wants to immediately send an email that offers a private consultation. What should a Data 360 Consultant recommend for this business requirement?
正解：A
解説：
The core Data 360 principle is harmonization: bring data from multiple systems into a governed model that business teams can use consistently. Create a streaming insight with a data action to Marketing Cloud Engagement. 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. 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.
質問 # 118
A customer has a requirement to be able to view the last time each segment was published within their Data Cloud org.
Which two features should the consultant recommend to best address this requirement?
Choose 2 answers
正解：C、D
解説：
Explanation
A customer who wants to view the last time each segment was published within their Data Cloud org can use the dashboard and report features to achieve this requirement. A dashboard is a visual representation of data that can show key metrics, trends, and comparisons. A report is a tabular or matrix view of data that can show details, summaries, and calculations. Both dashboard and report features allow the user to create, customize, and share data views based on their needs and preferences. To view the last time each segment was published, the user can create a dashboard or a report that shows the segment name, the publish date, and the publish status fields from the segment object. The user can also filter, sort, group, or chart the data by these fields to get more insights and analysis. The user can also schedule, refresh, or export the dashboard or report data as needed. References: Dashboards, Reports
質問 # 119
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