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Salesforce Data-Cloud-Consultant Exam Sample Questions

Question # 1

Cloud Kicks received a Request to be Forgotten by a customer. In which two ways should a consultant use Data Cloud to honor this request? Choose 2 answers

A. Delete the data from the incoming data stream and perform a full refresh.

B. Add the Individual ID to a headerless file and use the delete from file functionality.

C. Use Data Explorer to locate and manually remove the Individual.

D. Use the Consent API to suppress processing and delete the Individual and related records from source data streams.

Answer

B. Add the Individual ID to a headerless file and use the delete from file functionality.
D. Use the Consent API to suppress processing and delete the Individual and related records from source data streams.

Explanation:
To honor a Request to be Forgotten by a customer, a consultant should use Data Cloud in two ways:
Add the Individual ID to a headerless file and use the delete from file functionality. This option allows the consultant to delete multiple Individuals from Data Cloud by uploading a CSV file with their IDs1. The deletion process is asynchronous and can take up to 24 hours to complete1.
Use the Consent API to suppress processing and delete the Individual and related records from source data streams. This option allows the consultant to submit a Data Deletion request for an Individual profile in Data Cloud using the Consent API2. A Data Deletion request deletes the specified Individual entity and any entities where a relationship has been defined between that entity’s identifying attribute and the Individual ID attribute2. The deletion process is reprocessed at 30, 60, and 90 days to ensure a full deletion2. The other options are not correct because:
Deleting the data from the incoming data stream and performing a full refresh will not delete the existing data in Data Cloud, only the new data from the source system3.
Using Data Explorer to locate and manually remove the Individual will not delete the related records from the source data streams, only the Individual entity in Data Cloud. References:
Delete Individuals from Data Cloud Requesting Data Deletion or Right to Be Forgotten Data Refresh for Data Cloud [Data Explorer]

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Question # 2

A consultant is planning the ingestion of a data stream that has profile information including a mobile phone number. To ensure that the phone number can be used for future SMS campaigns, they need to confirm the phone number field is in the proper E164 Phone Number format. However, the phone numbers in the file appear to be in varying formats. What is the most efficient way to guarantee that the various phone number formats are standardized?

A. Create a formula field to standardize the format.

B. Edit and update the data in the source system prior to sending to Data Cloud.

C. Assign the PhoneNumber field type when creating the data stream.

D. Create a calculated insight after ingestion.

Answer

C. Assign the PhoneNumber field type when creating the data stream.

Explanation: The most efficient way to guarantee that the various phone number formats are standardized is to assign the PhoneNumber field type when creating the data stream. The PhoneNumber field type is a special field type that automatically converts phone numbers into the E164 format, which is the international standard for phone numbers. The E164 format consists of a plus sign (+), the country code, and the national number. 

For example, +1-202-555-1234 is the E164 format for a US phone number. By using the PhoneNumber field type, the consultant can ensure that the phone numbers are consistent and can be used for future SMS campaigns. The other options are either more time- consuming, require manual intervention, or do not address the formatting issue. 

References: Data Stream Field Types, E164 Phone Number Format, Salesforce Data Cloud Exam Questions

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Question # 3

Which two requirements must be met for a calculated insight to appear in the segmentation canvas? Choose 2 answers

A. The metrics of the calculated insights must only contain numeric values.

B. The primary key of the segmented table must be a metric in the calculated insight.

C. The calculated insight must contain a dimension including the Individual or Unified Individual Id.

D. The primary key of the segmented table must be a dimension in the calculated insight.

Answer

C. The calculated insight must contain a dimension including the Individual or Unified Individual Id.
D. The primary key of the segmented table must be a dimension in the calculated insight.

Explanation:

A calculated insight is a custom metric or measure that is derived from one or more data model objects or data lake objects in Data Cloud. A calculated insight can be used in segmentation to filter or group the data based on the calculated value. However, not all calculated insights can appear in the segmentation canvas. There are two requirements that must be met for a calculated insight to appear in the segmentation canvas:

The calculated insight must contain a dimension including the Individual or Unified Individual Id. A dimension is a field that can be used to categorize or group the data, such as name, gender, or location. The Individual or Unified Individual Id is a unique identifier for each individual profile in Data Cloud. The calculated insight must include this dimension to link the calculated value to the individual profile and to enable segmentation based on the individual profile attributes. The primary key of the segmented table must be a dimension in the calculated insight. The primary key is a field that uniquely identifies each record in a table. The segmented table is the table that contains the data that is being segmented, such as the Customer or the Order table. The calculated insight must include the primary key of the segmented table as a dimension to ensure that the calculated value is associated with the correct record in the segmented table and to avoid duplication or inconsistency in the segmentation results.

References: Create a Calculated Insight, Use Insights in Data Cloud, Segmentation

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Question # 4

A consultant at Northern Trail Outfitters is implementing Data Cloud and creating an activation target for their segment. For activation membership, which object should the consultant choose?

A. Data Model Object

B. Data Activation Object

C. Data Segmentation Object

D. Data Lake Object

Answer

B. Data Activation Object

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Question # 5

Cumulus Financial is currently using Data Cloud and ingesting transactional data from its backend system via an S3 Connector in upsert mode. During the initial setup six months ago, the company created a formula field in Data Cloud to create a custom classification. It now needs to update this formula to account for more classifications. What should the consultant keep in mind with regard to formula field updates when using the S3 Connector?

A. Data Cloud will initiate a full refresh of data from $3 and will update the formula on all records.

B. Data Cloud will only update the formula on a go-forward basis for new records.

C. Data Cloud does not support formula field updates for data streams of type upsert.

D. Data Cloud will update the formula for all records at the next incremental upsert refresh.

Answer

D. Data Cloud will update the formula for all records at the next incremental upsert refresh.

Explanation: A formula field is a field that calculates a value based on other fields or constants. When using the S3 Connector to ingest data from an Amazon S3 bucket, Data Cloud supports creating and updating formula fields on the data lake objects (DLOs) that store the data from the S3 source. However, the formula field updates are not applied immediately, but rather at the next incremental upsert refresh of the data stream. An incremental upsert refresh is a process that adds new records and updates existing records from the S3 source to the DLO based on the primary key field. Therefore, the consultant should keep in mind that the formula field updates will affect both new and existing records, but only after the next incremental upsert refresh of the data stream. The other options are incorrect because Data Cloud does not initiate a full refresh of data from S3, does not update the formula only for new records, and does support formula field updates for data streams of type upsert. 

References: Create a Formula Field, Amazon S3 Connection, Data Lake Object

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