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Universal Containers (UC) wants to use Flow to bring data from unified Data Cloud objects to prompt templates. Which type of flow should UC use?
A. Data Cloud-triggered flow
B. Template-triggered prompt flow
C. Unified-object linking flow
Explanation:
In this scenario, Universal Containers wants to bring data from unified Data Cloud objects into prompt templates, and the best way to do that is through a Data Cloud-triggered flow. This type of flow is specifically designed to trigger actions based on data changes within Salesforce Data Cloud objects.
Data Cloud-triggered flows can listen for changes in the unified data model and automatically bring relevant data into the system, making it available for prompt templates. This ensures that the data is both real-time and up-to-date when used in generative AI contexts.
For more detailed guidance, refer to Salesforce documentation on Data Cloud-triggered flows and Data Cloud integrations with generative AI solutions.
Universal Containers (UC) is using Einstein Generative AI to generate an account summary. UC aims to ensure the content is safe and inclusive, utilizing the Einstein Trust Layer's toxicity scoring to assess the content's safety level. What does a safety category score of 1 indicate in the Einstein Generative Toxicity Score?
A. Not safe
B. Safe
C. Moderately safe
Explanation:
In the Einstein Trust Layer, the toxicity scoring system is used to evaluate the safety level of content generated by AI, particularly to ensure that it is non-toxic, inclusive, and appropriate for business contexts. A toxicity score of 1indicates that the content is deemed safe.
The scoring system ranges from 0 (unsafe) to 1 (safe), with intermediate values indicating varying degrees of safety. In this case, a score of 1 means that the generated content is fully safe and meets the trust and compliance guidelines set by the Einstein Trust Layer.
How should an organization use the Einstein Trust layer to audit, track, and view masked data?
A. Utilize the audit trail that captures and stores all LLM submitted prompts in Data Cloud.
B. In Setup, use Prompt Builder to send a prompt to the LLM requesting for the masked data.
C. Access the audit trail in Setup and export all user-generated prompts.
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? Al though 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.
Where should the AI Specialist go to add/update actions assigned to a copilot?
A. Copilot Actions page, the record page for the copilot action, or the Copilot Action Library tab
B. Copilot Actions page or Global Actions
C. Copilot Detail page, Global Actions, or the record page for the copilot action
Explanation:
To add or update actions assigned to a copilot, an AI Specialist can manage this through several areas:
Copilot Actions Page: This is the central location where copilot actions are managed and configured.
Record Page for the Copilot Action: From the record page, individual copilot actions can be updated or modified.
Copilot Action Library Tab: This tab serves as a repository where predefined or custom actions for Copilot can be accessed and modified.
These areas provide flexibility in managing and updating the actions assigned to Copilot, ensuring that the AI assistant remains aligned with business requirements and processes.
The other options are incorrect:
B) misses the Copilot Action Library, which is crucial for managing actions.
C) includes the Copilot Detail page, which isn't the primary place for action management.
Which use case is best supported by Salesforce Einstein Copilot's capabilities?
A. Bring together a conversational interface for interacting with AI for all Salesforce users, such as developers and ecommerce retailers.
B. Enable Salesforce admin users to create and train custom large language models (LLMs) using CRM data.
C. Enable data scientists to train predictive AI models with historical CRM data using built-in machine learning capabilities.
Explanation:
Salesforce Einstein Copilot is designed to provide a conversational AI interface that can be utilized by different types of Salesforce users, such as developers, sales agents, and retailers. It acts as anAI-powered assistant that facilitates natural interactions with the system, enabling users to perform tasks and access data easily. This includes tasks like pulling reports, updating records, and generating personalized responses in real time.
Option A is correct because Einstein Copilot brings a conversational interface that caters to a wide range of users.
Option B and Option Care more focused on developing and training AI models, which are not the primary functions of Einstein Copilot.
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