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NEW QUESTION # 69
A client wants to bring in loyalty data from a custom object in Salesforce CRM that contains a point balance for accrued hotel points and airline points within the same record. The client wants to split these point systems into two separate records for better tracking and processing.
What should a consultant recommend in this scenario?
Answer: B
Explanation:
Batch transforms are a feature that allows creating new data lake objects based on existing data lake objects and applying transformations on them. This can be useful for splitting, merging, or reshaping data to fit the data model or business requirements. In this case, the consultant can use batch transforms to create a second data lake object that contains only the airline points from the original loyalty data object. The original object can be modified to contain only the hotel points. This way, the client can have two separate records for each point system and track and process them accordingly. Reference: Batch Transforms, Create a Batch Transform
NEW QUESTION # 70
How does Data Cloud handle an individual's Right to be Forgotten?
Answer: A
Explanation:
Data Cloud handles an individual's Right to be Forgotten by deleting the specified Individual and records from any data model object/data lake object related to the Individual. This means that Data Cloud removes all the data associated with the individual from the data space, including the data from the source objects, the unified individual profile, and any related objects. Data Cloud also deletes the Unified Individual Link record that links the individual to the source records. Data Cloud uses the Consent API to process the Right to be Forgotten requests, which are reprocessed at 30, 60, and 90 days to ensure a full deletion.
The other options are not correct descriptions of how Data Cloud handles an individual's Right to be Forgotten. Data Cloud does not delete the records from all data source objects, as this would affect the data integrity and availability of the source systems. Data Cloud also does not delete only the specified Individual record and its Unified Individual Link record, as this would leave the source records and the related records intact. Data Cloud also does not delete only the specified Individual and records from any data source object mapped to the Individual data model object, as this would leave the related records intact.
Reference:
Requesting Data Deletion or Right to Be Forgotten
Data Deletion for Data Cloud
Use the Consent API with Data Cloud
Data and Identity in Data Cloud
NEW QUESTION # 71
A Data 360 Consultant at Northern Trail Outfitters is attempting to ingest a field from the Contact object in Salesforce CRM that contains both yyyy-mm-dd and yyyy-mm-dd hh:mm:ss values. The target field is set to Date datatype. Which statement is true in this situation?
Answer: D
Explanation:
The design point is to preserve source fidelity while shaping data only where Data 360 processing needs it.
Here, The target field will only hold the date part and ignore the time part. 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 # 72
A consultant wants to ensure that every segment managed by multiple brand teams adheres to the same set of exclusion criteria, that are updated on a monthly basis.
What is the most efficient option to allow for this capability?
Answer: C
Explanation:
Explanation
The most efficient option to allow for this capability is to create a reusable container block with common criteria. A container block is a segment component that can bereused across multiple segments. A container block can contain any combination of filters, nested segments, and exclusion criteria. A consultant can create a container block with the exclusion criteria that apply to all the segments managed by multiple brand teams, and then add the container block to each segment. This way, the consultant can update the exclusion criteria in one place and have them reflected in all the segments that use the container block.
The other options are not the most efficient options to allow for this capability. Creating, publishing, and deploying a data kit is a way to share data and segments across different data spaces, but it does not allow for updating the exclusion criteria on a monthly basis. Creating a nested segment is a way to combine segments using logical operators, but it does not allow for excluding individuals based on specific criteria. Creating a segment and copying it for each brand is a way to create multiple segments with the same exclusion criteria, but it does not allow for updating the exclusion criteria in one place.
References:
* Create a Container Block
* Create a Segment in Data Cloud
* Create and Publish a Data Kit
* Create a Nested Segment
NEW QUESTION # 73
What does the Ignore Empty Value option do in identity resolution?
Answer: A
Explanation:
The Ignore Empty Value option in identity resolution allows customers to ignore empty fields when running reconciliation rules. Reconciliation rules are used to determine the final value of an attribute for a unified individual profile, based on the values from different sources. The Ignore Empty Value option can be set to true or false for each attribute in a reconciliation rule. If set to true, the reconciliation rule will skip any source that has an empty value for that attribute and move on to the next source in the priority order. If set to false, the reconciliation rule will consider any source that has an empty value for that attribute as a valid source and use it to populate the attribute value for the unified individual profile.
The other options are not correct descriptions of what the Ignore Empty Value option does in identity resolution. The Ignore Empty Value option does not affect the custom match rules or the standard match rules, which are used to identify and link individuals across different sources based on their attributes. The Ignore Empty Value option also does not ignore individual object records with empty fields when running identity resolution rules, as identity resolution rules operate on the attribute level, not the record level.
References:
* Data Cloud Identity Resolution Reconciliation Rule Input
* Configure Identity Resolution Rulesets
* Data and Identity in Data Cloud
NEW QUESTION # 74
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