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Salesforce Salesforce-Data-Cloud Exam Syllabus Topics:

SectionObjectives
Topic 1: Segmentation and Calculated Insights- Create and manage segments
- Explain calculated insights
Topic 2: Data Ingestion and Modeling- Describe data streams and data sources
- Explain data modeling concepts including Data Model Objects (DMOs)
Topic 3: Data Cloud Setup and Administration- Configure Data Cloud settings
- Manage Data Cloud permissions and security
Topic 4: Data Cloud Reporting and Analytics- Utilize Data Cloud reports and dashboards
Topic 5: Activation- Describe activation use cases
- Configure activations to various targets
Topic 6: Identity Resolution- Describe matching and reconciliation rules
- Explain the purpose and configuration of Identity Resolution
Topic 7: Data Cloud Overview- Describe the Data Cloud ecosystem and how it fits into the Salesforce platform
- Explain the purpose and functionality of Data Cloud

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Salesforce Data Cloud Accredited Professional Exam Sample Questions (Q78-Q83):

NEW QUESTION # 78
A consultant is preparing to implement Data Cloud.
Which ethic should the consultant adhere to regarding customer data?

Answer: B

Explanation:
When implementing Data Cloud, the consultant should adhere to ethical practices regarding customer data, particularly by carefully considering the collection and use of sensitive data such as age, gender, or ethnicity .
Here's why:
Understanding Ethical Considerations
Collecting and using customer data comes with significant ethical responsibilities, especially when dealing with sensitive information.
The consultant must ensure compliance with privacy regulations (e.g., GDPR, CCPA) and uphold ethical standards to protect customer trust.
Why Carefully Consider Sensitive Data?
Privacy and Trust :
Collecting sensitive data (e.g., age, gender, ethnicity) can raise privacy concerns and erode customer trust if not handled appropriately.
Customers are increasingly aware of their data rights and expect transparency and accountability.
Regulatory Compliance :
Regulations like GDPR and CCPA impose strict requirements on the collection, storage, and use of sensitive data.
Careful consideration ensures compliance and avoids potential legal issues.
Other Options Are Less Suitable :
A). Allow senior leaders in the firm to access customer data for audit purposes : While audits are important, unrestricted access to sensitive data is unethical and violates privacy principles.
B). Collect and use all of the data to create more personalized experiences : Collecting all data without regard for sensitivity is unethical and risks violating privacy regulations.
C). Map sensitive data to the same DMO for ease of deletion : While mapping data for deletion is a good practice, it does not address the ethical considerations of collecting sensitive data in the first place.
Steps to Ensure Ethical Practices
Step 1: Evaluate Necessity
Assess whether sensitive data is truly necessary for achieving business objectives.
Step 2: Obtain Explicit Consent
If sensitive data is required, obtain explicit consent from customers and provide clear explanations of how the data will be used.
Step 3: Minimize Data Collection
Limit the collection of sensitive data to only what is essential and anonymize or pseudonymize data where possible.
Step 4: Implement Security Measures
Use encryption, access controls, and other security measures to protect sensitive data.
Conclusion
The consultant should carefully consider asking for sensitive data such as age, gender, or ethnicity to uphold ethical standards, maintain customer trust, and ensure regulatory compliance.


NEW QUESTION # 79
When can the data types be changed during ingestion?

Answer: A

Explanation:
The data types can be changed during ingestion before the Data Source Object (DSO) is created. A DSO is a container for ingested data that defines the schema, format, and metadata of the data. Data types can be changed in the Data Explorer before creating a DSO by using the Edit Schema option. Once a DSO is created, the data types cannot be changed. Reference: Create a Data Source Object


NEW QUESTION # 80
What are the two distinct phases of data model management in Data Cloud?

Answer: A,D

Explanation:
These two phases are distinct phases of data model management in Data Cloud. Data ingestion is the process of bringing data from different sources into Data Cloud using connectors or APIs. Data modeling is the process of mappingthe ingested data to the Data Cloud canonical model or creating custom data model objects. References:https://help.salesforce.com/s/articleView?id=sf.c360_a_data_cloud_data_model.
htm&type=5


NEW QUESTION # 81
A retailer wants to unify profiles using Loyalty ID which is different than the unique ID of their customers.
Which object should the consultant use in identity resolution to perform exact match rules on the Loyalty ID?

Answer: C

Explanation:
The Party Identification object is the correct object to use in identity resolution to perform exact match rules on the Loyalty ID. The Party Identification object is a child object of the Individual object that stores different types of identifiers for an individual, such as email, phone, loyalty ID, social media handle, etc. Each identifier has a type, a value, and a source. The consultant can use the Party Identification object to create a match rule that compares the Loyalty ID type and value across different sources and links the corresponding individuals.
The other options are not correct objects to use in identity resolution to perform exact match rules on the Loyalty ID. The Loyalty Identification object does not exist in Data Cloud. The Individual object is the parent object that represents a unified profile of an individual, but it does not store the Loyalty ID directly. The Contact Identification object is a child object of the Contact object that stores identifiers for a contact, such as email, phone, etc., but it does not store the Loyalty ID.
Data Modeling Requirements for Identity Resolution
Identity Resolution in a Data Space
Configure Identity Resolution Rulesets
Map Required Objects
Data and Identity in Data Cloud


NEW QUESTION # 82
Which type of measures with aggregate functions are supported?

Answer: B

Explanation:
Explanation
Numeric is the only type of measures that support aggregate functions. Measures are numeric fields that can be aggregated using functions such as SUM, AVG, MIN, MAX, or COUNT. For example, you can use a measure to calculate the total revenue or the average order value from your data. Date, Text, and Boolean are not types of measures, but types of dimensions. Dimensions are fields that can be used to group or filter data, but not to perform calculations. References: Measures and Dimensions


NEW QUESTION # 83
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