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Free Salesforce-AI-Specialist Exam Braindumps (page: 8)

Page 7 of 39
PDF with 152 Questions

Universal Containers (UC) is implementing Einstein Generative AI to improve customer insights and interactions. UC needs audit and feedback data to be accessible for reporting purposes.
What is a consideration for this requirement?

  1. Storing this data requires Data Cloud to be provisioned.
  2. Storing this data requires a custom object for data to be configured.
  3. Storing this data requires Salesforce big objects.

Answer(s): A

Explanation:

When implementing Einstein Generative AI for improved customer insights and interactions, the Data Cloud is a key consideration for storing and managing large-scale audit and feedback data. The Salesforce Data Cloud (formerly known as Customer 360 Audiences) is designed to handle and unify massive datasets from various sources, making it ideal for storing data required for AI-powered insights and reporting. By provisioning Data Cloud, organizations like Universal Containers (UC) can gain real-time access to customer data, making it a central repository for unified reporting across various systems.
Audit and feedback data generated by Einstein Generative AI needs to be stored in a scalable and accessible environment, and the Data Cloud provides this capability, ensuring that data can be easily accessed for reporting, analytics, and further model improvement. Custom objects or Salesforce Big Objects are not designed for the scale or the specific type of real-

time, unified data processing required in such AI-driven interactions. Big Objects are more suited for archival data, whereas Data Cloud ensures more robust processing, segmentation, and analysis capabilities.


Reference:

Salesforce Data Cloud Documentation: https://www.salesforce.com/products/data-cloud/overview/ Salesforce Einstein AI Overview: https://www.salesforce.com/products/einstein/overview/



In Model Playground, which hyperparameters of an existing Salesforce-enabled foundational model can an AI Specialist change?

  1. Temperature, Frequency Penalty, Presence Penalty
  2. Temperature, Top-k sampling, Presence Penalty
  3. Temperature, Frequency Penalty, Output Tokens

Answer(s): A

Explanation:

In Model Playground, an AI specialist working with a Salesforce-enabled foundational model has control over specific hyperparameters that can directly affect the behavior of the generative model:
Temperature: Controls the randomness of predictions. A higher temperature leads to more diverse outputs, while a lower temperature makes the model's responses more focused and deterministic. Frequency Penalty: Reduces the likelihood of the model repeating the same phrases or outputs frequently.
Presence Penalty: Encourages the model to introduce new topics in its responses, rather than sticking with familiar, previously mentioned content.
These hyperparameters are adjustable to fine-tune the model's responses, ensuring that it meets the desired behavior and use case requirements. Salesforce documentation confirms that these three are the key tunable hyperparameters in the Model Playground. For more details, refer to Salesforce AI Model Playground guidance from Salesforce's official documentation on foundational model adjustments.



How should an organization use the Einstein Trust layer to audit, track, and view masked data?

  1. Utilize the audit trail that captures and stores all LLM submitted prompts in Data Cloud.
  2. In Setup, use Prompt Builder to send a prompt to the LLM requesting for the masked data.
  3. Access the audit trail in Setup and export all user-generated prompts.

Answer(s): A

Explanation:

The Einstein Trust Layer is designed to ensure transparency, compliance, and security for organizations leveraging Salesforce's AI and generative AI capabilities. Specifically, for auditing, tracking, and viewing masked data, organizations can utilize:

Audit Trail in Data Cloud: The audit trail captures and stores all prompts submitted to large language models (LLMs), ensuring that sensitive or masked data interactions are logged. This allows organizations to monitor and audit all AI-generated outputs, ensuring that data handling complies with internal and regulatory guidelines. The Data Cloud provides the infrastructure for managing and accessing this audit data.
Why not B? Using Prompt Builder in Setup to send prompts to the LLM is for creating and managing prompts, not for auditing or tracking data. It does not interact directly with the audit trail functionality.
Why not C? Although the audit trail can be accessed in Setup, the user-generated prompts are primarily tracked in the Data Cloud for broader control, auditing, and analysis. Setup is not the primary tool for exporting or managing these audit logs. More information on auditing AI interactions can be found in the Salesforce AI Trust Layer documentation, which outlines how organizations can manage and track generative AI interactions securely.



An AI Specialist implements Einstein Sales Emails for a sales team. The team wants to send personalized follow-up emails to leads based on their interactions and data stored in Salesforce. The AI Specialist needs to configure the system to use the most accurate and up-to-date information for email generation.
Which grounding technique should the AI Specialist use?

  1. Ground with Apex Merge Fields
  2. Ground with Record Merge Fields
  3. Automatic grounding using Draft with Einstein feature

Answer(s): C

Explanation:

For Einstein Sales Emails to generate personalized follow-up emails, it is crucial to ground the email content with the most up-to-date and accurate information. Grounding refers to connecting the AI model with real-time data. The most appropriate technique in this case is Ground with Record Merge Fields. This method ensures that the content in the emails pulls dynamic and accurate data directly from Salesforce records, such as lead or contact information, ensuring the follow-up is relevant and customized based on the specific record.

Record Merge Fields ensure the generated emails are highly personalized using data like lead name, company, or other Salesforce fields directly from the records. Apex Merge Fields are typically more suited for advanced, custom logic-driven scenarios but are not the most straightforward for this use case.
Automatic grounding using Draft with Einstein is a different feature where Einstein automatically drafts the email, but it does not specifically ground the content with record-specific data like Record Merge Fields.


Reference:

Salesforce Einstein Sales Emails Documentation:
https://help.salesforce.com/s/articleView?id=release-notes.rn_einstein_sales_emails.htm






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