CompTIA
Checkpoint
ISC
HP
Dell
EC-Council
EXIN
Fortinet
ITIL®
Juniper
PMI
Microsoft
VMware
Avaya
Google
Salesforce
Amazon
Palo Alto Networks
Certiport
ISC2
All Vendors
  2. Home
  3. Exams
  4. Salesforce
  5. Data-Cloud-Consultant

Free Data-Cloud-Consultant Exam Braindumps (page: 21)

Page 20 of 44
PDF with 170 Questions

How does Data Cloud handle an individual's Right to be Forgotten?

  1. Deletes the records from all data source objects, and any downstream data model objects are updated at the next scheduled ingestion
  2. Deletes the specified Individual record and its Unified Individual Link record.
  3. Deletes the specified Individual and records from any data source object mapped to the Individual data model object.
  4. Deletes the specified Individual and records from any data model object/data lake object related to the Individual.

Answer(s): D

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



A healthcare client wants to make use of identity resolution, but does not want to risk unifying profiles that may share certain personally identifying information (PII).
Which matching rule criteria should a consultant recommend for the most accurate matching results?

  1. Party Identification on Patient ID
  2. Exact Last Name and Emil
  3. Email Address and Phone
  4. Fuzzy First Name, Exact Last Name, and Email

Answer(s): A

Explanation:

Identity resolution is the process of linking data from different sources into a unified profile of a customer or an individual. Identity resolution uses matching rules to compare the attributes of different records and determine if they belong to the same person. Matching rules can be based on exact or fuzzy matching of various attributes, such as name, email, phone, address, or custom identifiers. A healthcare client who wants to use identity resolution, but does not want to risk unifying profiles that may share certain personally identifying information (PII), such as name or email, should use a matching rule criteria that is based on a unique and reliable identifier that is specific to the healthcare domain. One such identifier is the patient ID, which is a unique number assigned to each patient by a healthcare provider or system. By using the party identification on patient ID as a matching rule criteria, the healthcare client can ensure that only records that have the same patient ID are matched and unified, and avoid false positives or false negatives that may occur due to common or similar names or emails. The party identification on patient ID is also a secure and compliant way of handling sensitive healthcare data, as it does not expose or share any PII that may be subject to data protection regulations or standards.


Reference:

Configure Identity Resolution Rulesets, A framework of identity resolution: evaluating identity attributes and methods



A user is not seeing suggested values from newly-modeled data when building a segment.
What is causing this issue?

  1. Value suggestion is still processing and to be available.
  2. Value suggestion requires Data Aware Specialist permissions at a minimum.
  3. Value suggestion can only work on direct attributes and not related attributes.
  4. Value suggestion will only return result for the first 50 values of a specific attribute.

Answer(s): A

Explanation:

Value suggestion is a feature that allows users to see suggested values for data model object (DMO) fields when creating segment filters. However, this feature can take up to 24 hours to process and display the values for newly-modeled data. Therefore, if a user is not seeing suggested values from newly-modeled data, it is likely that the value suggestion is still processing and will be available soon. The other options are incorrect because value suggestion does not require any specific permissions, can work on both direct and related attributes, and can return more than 50 values for a specific attribute, depending on the data type and frequency of the values.


Reference:

Use Value Suggestions in Segmentation, Data Cloud Limits and Guidelines



A consultant is building a segment to announce a new product launch for customers that have previously purchased black pants.

How should the consultant place attributes for product color and product type from the Order Product object to meet this criteria?

  1. Place the attribute for product color in one container and the attribute for product type in another container.
  2. Place an attribute for the "black" calculated insight to dynamically apply
  3. Place the attributes for product and product type as direct attributes.
  4. Place the attributes for product color and product type in a single container.

Answer(s): D

Explanation:

To create a segment based on the product color and product type from the Order Product object, the consultant should place the attributes for product color and product type in a single container. This way, the segment will include only the customers who have purchased black pants, and not those who have purchased black shirts or blue pants. A container is a grouping of attributes that defines a segment of individuals based on a logical AND operation. Placing the attributes in separate containers would result in a segment that includes customers who have purchased any black product or any pants product, which is not the desired criteria. Placing an attribute for the "black" calculated insight would not work, because calculated insights are based on aggregated data and not individual- level data. Placing the attributes as direct attributes would not work, because direct attributes are used to filter individuals based on their profile data, not their order data.


Reference:

Create a Segment in Data Cloud
Learn About Segmentation Tools
Salesforce Launches: Data Cloud Consultant Certification






Post your Comments and Discuss Salesforce Data-Cloud-Consultant exam with other Community members:

Data-Cloud-Consultant Discussions & Posts