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Salesforce Data-Cloud-Consultant Exam Syllabus Topics:

Topic
Details

Topic 1

Data Cloud Overview: This topic covers Data Cloud's function, key terminology, business value, typical use cases, the Data Cloud lifecycle, dependencies, and principles of data ethics. These sub-topics provide an overview of Data Cloud's capabilities and applications.

Topic 2

Act on Data: This topic defines activations and their basic use cases, using attributes and related attributes, identifying and analyzing timing dependencies affecting the Data Cloud lifecycle. Additionally it focuses on troubleshooting common problems with activations, and using data actions, including their requirements and intended use cases.

Topic 3

Data Ingestion and Modeling: This topic covers the different transformation capabilities within Data Cloud. It includes describing processes and considerations for data ingestion from various sources, defining, mapping, and modeling data using best practices aligned with identity resolution. Lastly, it discusses using available tools to inspect and validate ingested and modeled data.

Salesforce Certified Data Cloud Consultant Sample Questions (Q24-Q29):

NEW QUESTION # 24
The marketing manager at Cloud Kicks plans to bring in corporate phone numbers for its accounts into Data Cloud. They plan to use a custom field with data set to Phone to store these phone numbers.
Which statement is true when ingesting phone numbers?

A. The phone number field car only accept 10-digit values.
B. Text value can be accepted for ingestion into = phone data type field.
C. The phone number field should be used as a primary key.
D. Data Cloud validates the format of the phone number at the time of Ingestion.

Answer: B

NEW QUESTION # 25
Cumulus Financial uses Service Cloud as its CRM and stores mobile phone, home phone, and work phone as three separate fields for its customers on the Contact record. The company plans to use Data Cloud and ingest the Contact object via the CRM Connector.
What is the most efficient approach that a consultant should take when ingesting this data to ensure all the different phone numbers are properly mapped and available for use in activation?

A. Ingest the Contact object and use streaming transforms to normalize the phone numbers from the Contact data stream into a separate Phone data lake object (DLO) that contains three rows, and then map this new DLO to the Contact Point Phone data map object.
B. Ingest the Contact object and map the Work Phone, Mobile Phone, and Home Phone to the Contact Point Phone data map object from the Contact data stream.
C. Ingest the Contact object and then create a calculated insight to normalize the phone numbers, and then map to the Contact Point Phone data map object.
D. Ingest the Contact object and create formula fields in the Contact data stream on the phone numbers, and then map to the Contact Point Phone data map object.

Answer: A

Explanation:
Explanation
The most efficient approach that a consultant should take when ingesting this data to ensure all the different phone numbers are properly mapped and available for use in activation is B. Ingest the Contact object and use streaming transforms to normalize the phone numbers from the Contact data stream into a separate Phone data lake object (DLO) that contains three rows, and then map this new DLO to the Contact Point Phone data map object. This approach allows the consultant to use the streaming transforms feature of Data Cloud, which enables data manipulation and transformation at the time of ingestion, without requiring any additional processing or storage. Streaming transforms can be used to normalize the phone numbers from the Contact data stream, such as removing spaces, dashes, or parentheses, and adding country codes if needed. The normalized phone numbers can then be stored in a separate Phone DLO, which can have one row for each phone number type (work, home, mobile). The Phone DLO can then be mapped to the Contact Point Phone data map object, which is a standard object that represents a phone number associated with a contact point.
This way, the consultant can ensure that all the phone numbers are available for activation, such as sending SMS messages or making calls to the customers.
The other options are not as efficient as option B. Option A is incorrect because it does not normalize the phone numbers, which may cause issues with activation or identity resolution. Option C is incorrect because it requires creating a calculated insight, which is an additional step that consumes more resources and time than streamingtransforms. Option D is incorrect because it requires creating formula fields in the Contact data stream, which may not be supported by the CRM Connector or may cause conflicts with the existing fields in the Contact object. References: Salesforce Data Cloud Consultant Exam Guide, Data Ingestion and Modeling, Streaming Transforms, Contact Point Phone

NEW QUESTION # 26
A customer has a custom Customer Email c object related to the standard Contact object in Salesforce CRM.
This custom object
stores the email address a Contact that they want to use for activation.
To which data entity is mapped?

A. Custom customer Email__c object
B. Contact
C. Contact Point_Email
D. Individual

Answer: C

Explanation:
The Contact Point_Email object is the data entity that represents an email address associated with an individual in Data Cloud. It is part of the Customer 360 Data Model, which is a standardized data model that defines common entities and relationships for customer data. The Contact Point_Email object can be mapped to any custom or standard object that stores email addresses in Salesforce CRM, such as the custom Customer Email__c object. The other options are not the correct data entities to map to because:
A: The Contact object is the data entity that represents a person who is associated with an account that is a customer, partner, or competitor in Salesforce CRM. It is not the data entity that represents an email address in Data Cloud.
C: The custom Customer Email__c object is not a data entity in Data Cloud, but a custom object in Salesforce CRM. It can be mapped to a data entity in Data Cloud, such as the Contact Point_Email object, but it is not a data entity itself.
D: The Individual object is the data entity that represents a unique person in Data Cloud. It is the core entity for managing consent and privacy preferences, and it can be related to one or more contact points, such as email addresses, phone numbers, or social media handles. It is not the data entity that represents an email address in Data Cloud. References: Customer 360 Data Model: Individual and Contact Points - Salesforce, Contact Point_Email | Object Reference for the Salesforce Platform | Salesforce Developers, [Contact | Object Reference for the Salesforce Platform | Salesforce Developers], [Individual | Object Reference for the Salesforce Platform | Salesforce Developers]

NEW QUESTION # 27
The leadership team at Cumulus Financial has determined that customers who deposited more than $250,000 in the last five years and are not using advisory services will be the central focus for all new campaigns in the next year.
Which features support this use case?

A. Streaming insight and data action
B. Streaming insight and segment
C. Calculated insight and segment
D. Calculated insight and data action

Answer: C

Explanation:
Understanding the Use Case:
* The leadership team wants to focus on customers who have deposited more than $250,000 in the last five years and are not using advisory services.

NEW QUESTION # 28
A Data Cloud consultant recently discovered that their identity resolution process is matching individuals that share email addresses or phone numbers, but are not actually the same individual.
What should the consultant do to address this issue?

A. Create and run a new rules fewer matching rules, compare the two rulesets to review and verify the results, and then migrate to the new ruleset once approved.
B. Create and run a new ruleset with stricter matching criteria, compare the two rulesets to review and verify the results, and then migrate to the new ruleset once approved.
C. Modify the existing ruleset with stricter matching criteria, compare the two rulesets to review and verify the results, and then migrate to the new ruleset once approved.
D. Modify the existing ruleset with stricter matching criteria, run the ruleset and review the updated results, then adjust as needed until the individuals are matching correctly.

Answer: B

Explanation:
Identity resolution is the process of linking source profiles from different data sources into unified individual profiles based on match and reconciliation rules. If the identity resolution process is matching individuals that share email addresses or phone numbers, but are not actually the same individual, it means that the match rules are too loose and need to be refined. The best way to address this issue is to create and run a new ruleset with stricter matching criteria, such as adding more attributes or increasing the match score threshold. Then, the consultant can compare the two rulesets to review and verify the results, and see if the new ruleset reduces the false positives and improves the accuracy of the identity resolution. Once the new ruleset is approved, the consultant can migrate to the new ruleset and delete the old one. The other options are incorrect because modifying the existing ruleset can affect the existing unified profiles and cause data loss or inconsistency.
Creating and running a new ruleset with fewer matching rules can increase the false negatives and reduce the coverage of the identity resolution. References: Create Unified Individual Profiles, AI-based Identity Resolution: Linking Diverse Customer Data, Data Cloud Identiy Resolution.

NEW QUESTION # 29
......

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