Salesforce Updated Salesforce-AI-Specialist Exam Questions and Answers by lake

Dynamic grounding with secure data retrieval is a key feature in Salesforce's Einstein Trust Layer, which provides enhanced data protection and ensures that AI-generated outputs are both accurate and securely sourced. This feature allows relevant Salesforce data to be merged into the AI-generated responses, ensuring that the AI outputs are contextually aware and aligned with real-time CRM data.

Dynamic grounding means that AI models are dynamically retrieving relevant information from Salesforce records (such as customer records, case data, or custom object data) in a secure manner. This ensures that any sensitive data is protected during AI processing and that the AI model’s outputs are trustworthy and reliable for business use.

The other options are less aligned with the requirement:

Data masking refers to obscuring sensitive data for privacy purposes and is not related to merging Salesforce records into prompts.

Zero-data retention policy ensures that AI processes do not store any user data after processing, but this does not address the need to merge Salesforce record information into a prompt.

References:

Salesforce Developer Documentation on Einstein Trust Layer

Salesforce Security Documentation for AI and Data Privacy

Before creating a Field Generation prompt template, the AI Specialist must ensure that the Salesforce org is set to API version 59 or higher. This version of the API introduces support for advanced generative AI features, such as enabling fields for generative AI outputs. This is a critical technical requirement for the Field Generation prompt template to function correctly.

Option A (rich text field requirement) is not necessary for generative AI functionality.

Option C (Dynamic Forms) does not impact the ability of a field to be generative AI-enabled, although it might enhance the user interface.

For more information, refer to Salesforce documentation on API versioning and Field Generation templates.

Universal Containers is enhancing its customer service operations with Custom Copilot Actions. The development team needs to understand the core components of a Custom Copilot Action to ensure proper configuration and functionality. One of these core components is the Output Types.

Core Components of a Custom Copilot Action:

Instructions:

Defines what the action should accomplish.

Provides guidance to the AI assistant on how to execute the action.

Input Parameters:

The data inputs required for the action to perform its task.

Specifies the parameters that users or systems need to provide.

Output Types:

Definition: Specifies the type of data the action will output after execution.

Importance: Ensures that the Copilot and other components understand the format and structure of the action's results.

Examples: Text, JSON, records, or other data structures.

Action Reference:

Points to the underlying implementation, such as an Apex class or Flow.

Action Triggers:

Conditions or events that initiate the action.

Focus on Output Types:

Relevance in Configuration:

The development team must define the Output Types correctly to ensure that the action's results are properly processed and displayed by Copilot.

Incorrect or undefined Output Types can lead to misinterpretation of data and failures in functionality.

Why Output Types are a Core Component:

Integration with Copilot:

Copilot relies on the Output Types to understand how to handle the data returned by the action.

Data Consistency:

Defines the structure and format of the output, ensuring consistent communication between the action and Copilot.

User Experience:

Proper Output Types ensure that users receive the expected results in an understandable format.

Why Other Options are Less Suitable:

Option A (Instructions):

While Instructions are a core component, the question asks for what should be reviewed in the configuration to identify one of the core components.

In this context, reviewing Output Types is more critical to ensuring proper configuration and functionality.

Option C (Action Triggers):

Action Triggers are important but are not always considered a core component within the configuration of a Custom Copilot Action.

Triggers often relate to when an action is initiated rather than the configuration of the action itself.

References:

Salesforce AI Specialist Documentation - Custom Copilot Actions:

Details the components and configuration of Custom Copilot Actions.

Salesforce Help - Defining Output Types in Custom Actions:

Explains the importance of Output Types and how to configure them.

Salesforce Trailhead - Building Custom Copilot Actions:

Provides a hands-on approach to creating and configuring Custom Copilot Actions, highlighting key components.

For Northern Trail Outfitters (NTO) to configure the Einstein Trust Layer, the Einstein Generative AI feature must be enabled. The Einstein Trust Layer is closely tied to generative AI capabilities, ensuring that AI-generated content complies with data privacy, security, and trust standards.

Option A (Turning on Einstein Copilot) is unrelated to the setup of the Einstein Trust Layer, which focuses more on generative AI interactions and data handling.

Option C (Turning on Prompt Builder) is used for configuring and building AI-driven prompts, but it does not enable the Einstein Trust Layer.

Salesforce AI Specialist References:For more details on the Einstein Trust Layer and setup steps: https://help.salesforce.com/s/articleView?id=sf.einstein_trust_layer_overview.htm