Salesforce Updated Agentforce-Specialist Exam Questions and Answers by andre

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.

Comprehensive and Detailed In-Depth Explanation:Universal Containers (UC) aims to use Record Snapshots grounding in a prompt template to provide context from a specific record. Let’s evaluate the preparation steps.

Option A: Configure page layout of the master record type.While page layouts define field visibility for users, Record Snapshots grounding relies on field accessibility at the object level, not the layout. The AI accesses data based on permissions and configuration, not layout alone, making this insufficient and incorrect.

Option B: Create a field set for all the fields to be grounded.Record Snapshots in Prompt Builder allow grounding with fields from a record, but you must specify which fields to include. Creating a field set is a recommended preparation step—it groups the fields (e.g., from the object) to be passed to the prompt template, ensuring the AI has the right data. This is a documented best practice for controlling snapshot scope, making it the correct answer.

Option C: Enable and configure dynamic form for the object.Dynamic Forms enhance UI flexibility but aren’t required for Record Snapshots grounding. The feature pulls data directly from the object, not the form configuration, making this irrelevant and incorrect.

Why Option B is Correct:Creating a field set ensures the prompt template uses the intended fields for grounding, a key preparation step per Salesforce documentation.

References:

Salesforce Agentforce Documentation: Prompt Builder > Record Snapshots – Recommends field sets for grounding.

Trailhead: Ground Your Agentforce Prompts – Details field set preparation.

Salesforce Help: Set Up Record Snapshots – Confirms field set usage.

For improving sales operations efficiency, Einstein Studio is ideal for creating AI-powered models that can predict outcomes based on data. One of the most valuable use cases is predicting customer lifetime value, which helps sales teams focus on high-value accounts and make more informed decisions. Customer lifetime value (CLV) predictions can optimize strategies around customer retention, cross-selling, and long-term engagement.

Option B is the correct choice as predicting customer lifetime value is a well-established use case for AI in sales.

Option A (customer sentiment) is typically handled through NLP models, while Option C (product popularity) is more of a marketing analysis use case.

References:

Salesforce Einstein Studio Use Case Overview: https://help.salesforce.com/s/articleView?id=sf.einstein_studio_overview

Comprehensive and Detailed In-Depth Explanation:Agentforce Agents are designed to handle situations where they cannot interpret a request or retrieve requested data gracefully. Let’s assess the options based on Agentforce behavior.

Option A: With a preconfigured message, based on the action type.While Agentforce allows customization of responses, there’s no specific mechanism tying preconfigured messages to action types for unhandled requests. Fallback responses are more general, not action-specific, making this incorrect.

Option B: With a general message asking the user to rephrase the request.When an Agentforce Agent fails to understand a request or find information, it defaults to a general fallback response, typically asking the user to rephrase or clarify their input (e.g., “I didn’t quite get that—could you try asking again?”). This is configurable in Agent Builder but defaults to a user-friendly prompt to encourage retry, aligning with Salesforce’s focus on conversational UX. This is the correct answer per documentation.

Option C: With a generated error message.Agentforce Agents prioritize user experience over technical error messages. While errors might log internally (e.g., in Event Logs), the user-facing response avoids jargon and focuses on retry prompts, making this incorrect.

Why Option B is Correct:The default behavior of asking users to rephrase aligns with Agentforce’s conversational design principles, ensuring a helpful response when comprehension fails, as noted in official resources.

References:

Salesforce Agentforce Documentation: Agent Builder > Fallback Responses – Describes general retry messages.

Trailhead: Build Agents with Agentforce – Covers handling ununderstood requests.

Salesforce Help: Agentforce Interaction Design – Confirms user-friendly fallback behavior.