Salesforce Data-Cloud-Consultant Exam With Confidence Using Practice Dumps

Salesforce handles Data 360 data-subject-rights requests through the Salesforce Consent API, including Data Deletion or Right to Be Forgotten requests. Current Salesforce documentation states that these requests are submitted for individual profiles; when Identity Resolution is used, requests should target the applicable source individual profiles. This makes C the required consideration. A one-hour completion guarantee is not stated as the governing behavior, so A is incorrect. Requests submitted to Data 360 are not automatically propagated as deletion commands to every connected Salesforce cloud, eliminating B. Option D is also too broad: deletion behavior depends on the data model relationships and the records covered by the request rather than an unconditional deletion of every related record. The key implementation concept is that Consent API requests are profile-centered and governed by Data 360’s data-subject-rights processing.

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The design point is to preserve source fidelity while shaping data only where Data 360 processing needs it. Here, The Tenant Specific Endpoint is auto-generated in Data 360 when setting the connector. 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.

The design point is to preserve source fidelity while shaping data only where Data 360 processing needs it. Here, To enable efficient querying on non-primary key attributes 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.