Universal Containers is considering leveraging the Einstein Trust Layer in conjunction with Einstein Generative AI Audit Data.
Which audit data is available using the Einstein Trust Layer?
Correct Answer:
C
Universal Containers is considering the use of the Einstein Trust Layer along with Einstein Generative AI Audit Data. The Einstein Trust Layer provides a secure and compliant way to use AI by offering features like data masking and toxicity assessment.
The audit data available through the Einstein Trust Layer includes information about masked data—which ensures sensitive information is not exposed—and the toxicity score, which evaluates the generated content for inappropriate or harmful language. References:
✑ Salesforce Agentforce Specialist Documentation - Einstein Trust Layer: Details the
auditing capabilities, including logging of masked data and evaluation of generated responses for toxicity to maintain compliance and trust.
A Salesforce Administrator is exploring the capabilities of Agent to enhance user interaction within their organization. They are particularly interested in how Agent processes user requests and the mechanism it employs to deliver responses. The administrator is evaluating whether Agent directly interfaces with a large language model (LLM) to fetch and display responses to user inquiries, facilitating a broad range of requests from users.
How does Agent handle user requests In Salesforce?
Correct Answer:
C
Agent is designed to enhance user interaction within Salesforce by leveraging Large Language Models (LLMs) to process and respond to user inquiries. When a user submits a request, Agent analyzes the input using natural language processing techniques. It then utilizes LLM technology to generate an appropriate and contextually relevant response, which is displayed directly to the user within the Salesforce interface. Option C accurately describes this process. Agent does not necessarily trigger a flow (Option A) or perform an HTTP callout to an LLM provider (Option B) for each user request. Instead, it integrates LLM capabilities to provide immediate and intelligent responses,
facilitating a broad range of user requests.
References:
✑ Salesforce Agentforce Specialist Documentation - Agent Overview: Details how Agent employs LLMs to interpret user inputs and generate responses within the Salesforce ecosystem.
✑ Salesforce Help - How Agent Works: Explains the underlying mechanisms of how Agent processes user requests using AI technologies.
In Model Playground, which hyperparameters of an existing Salesforce-enabled foundational model can An Agentforce change?
Correct Answer:
A
In Model Playground, An Agentforce 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.
Universal Containers has a new AI project.
What should An Agentforce consider when adding a related list on the Account object to be used in the prompt template?
Correct Answer:
A
✑ Context of the QuestionUniversal Containers (UC) wants to include details from a related list on the Account object in a prompt template. This is typically done via Prompt Builder in Salesforce??s generative AI setup.
✑ Prompt Builder Behavior
✑ Why Option A is Correct
✑ Why Not Option B (JSON Formatting)
✑ Why Not Option C (Default Page Layout)
✑ ConclusionSince the official Salesforce approach involves selecting a related list and then using the field picker to insert merge fields, Option A is the correct and verified answer.
Salesforce Agentforce Specialist References & Documents
✑ Salesforce Official Documentation: Prompt Builder BasicsExplains how to reference objects and related lists when building AI prompts.
✑ Salesforce Trailhead: Get Started with Prompt BuilderProvides hands-on exercises demonstrating how to pick fields from related objects or lists.
✑ Salesforce Agentforce Specialist Study GuideOutlines best practices for
referencing related records and fields in generative AI prompts.
How does the Einstein Trust Layer ensure that sensitive data is protected while generating useful and meaningful responses?
Correct Answer:
A
The Einstein Trust Layer ensures that sensitive data is protected while generating useful and meaningful responses by masking sensitive data before it is sent to the Large Language Model (LLM) and then de-masking it during the response journey.
How It Works:
✑ Data Masking in the Request Journey:
✑ Processing by the LLM:
✑ De-masking in the Response Journey:
Why Option A is Correct:
✑ De-masking During Response Journey: The de-masking process occurs after the LLM has generated its response, ensuring that sensitive data is only reintroduced into the output at the final stage, securely and appropriately.
✑ Balancing Security and Utility: This approach allows the system to generate useful and meaningful responses that include necessary sensitive information without compromising data security.
Why Options B and C are Incorrect:
✑ Option B (Masked data will be de-masked during request journey):
✑ Option C (Responses that do not meet the relevance threshold will be automatically rejected):
References:
✑ Salesforce Agentforce Specialist Documentation - Einstein Trust Layer Overview:
✑ Salesforce Help - Data Masking and De-masking Process:
✑ Salesforce Agentforce Specialist Exam Guide - Security and Compliance in AI:
Conclusion:
The Einstein Trust Layer ensures sensitive data is protected by masking it before sending any prompts to the LLM and then de-masking it during the response journey. This process allows Salesforce to generate useful and meaningful responses that include necessary sensitive information without exposing that data during the AI processing, thereby maintaining data security and compliance.
