Salesforce Agentforce-Specialist Exam Questions

Bring Your Own Large Language Model (BYO-LLM) functionality in Einstein Studio allows organizations to integrate and use external large language models (LLMs) within the Salesforce ecosystem. Universal Containers can leverage this feature to connect and ground prompts with external LLMs, allowing for custom AI model use cases and seamless integration with Salesforce data.

* Option B is the correct choice as Einstein Studio provides a built-in feature to work with external models.

* Option A suggests using Apex, but BYO-LLM functionality offers a more streamlined solution.

* Option C focuses on Flow and External Services, which is more about data integration and isn't ideal for working with LLMs.

Salesforce Einstein Studio BYO-LLM Documentation: https://help.salesforce.com/s/articleView?id=sf.einstein_studio_llm.htm

Before enabling Call Explorer, the Salesforce Agentforce Specialist must ensure that the Einstein Conversation Insights permission set is assigned to users (Option C). Call Explorer is a feature within Einstein Conversation Insights (ECI) that analyzes meeting recordings to surface trends, keywords, and actionable insights.

Key Considerations:

Permission Set Requirement:

Users (including admins) need the Einstein Conversation Insights permission set to access and use Call Explorer. Without this, the feature remains inaccessible.

The permission set grants access to ECI tools, including call transcription, analysis, and dashboard visibility.

Why Other Options Are Incorrect:

A . Independence from Salesforce Knowledge: While Call Explorer does not rely on Salesforce Knowledge, this is irrelevant to the setup prerequisite. The critical dependency is the permission set, not Knowledge configuration.

B . Custom Actions: Call Explorer does not require custom actions to be built before configuration. It is a pre-built analytics tool that works once permissions and data sources (e.g., call recordings) are configured.

Salesforce Einstein Conversation Insights Guide: Explicitly states that the Einstein Conversation Insights permission set is required to access Call Explorer.

Trailhead Module: 'Einstein Conversation Insights Basics' outlines permission prerequisites for enabling call analytics.

Salesforce Help Documentation: Confirms that Call Explorer functionality is governed by ECI permissions.

To effectively manage and use prompt templates, two distinct permission sets are required:

Prompt Template Manager: This permission set allows users to create prompt templates. It provides the necessary access to define templates, which can be shared and utilized across the organization.

Prompt Template User: This permission set is designed for users who need to execute the templates. It provides the ability to interact with pre-designed prompts and generate outcomes based on these templates.

The Data Cloud Admin permission set is not directly relevant to creating or executing prompt templates but is more focused on managing the Data Cloud.

'Permissions and Access for Prompt Templates | Salesforce Trailhead' .

Context of the Question

Universal Containers (UC) wants to send well-formatted JSON data in a prompt to a large language model (LLM).

The question is about an important technical or design consideration for including CRM data as JSON in that prompt.

Why Apex Code for JSON Formatting?

Apex to Generate JSON: Salesforce does not have a simple ''checkbox'' or single setting to ''convert CRM data to JSON.'' Typically, to structure data as JSON in a template, you either:

Use an Apex class that queries or processes the data, then returns a JSON string.

Use a Flow or formula approach (though complex data structures often require Apex).

No Built-In ''Enable JSON Format in Prompt Builder'': Prompt Builder doesn't have a toggle that automatically transforms data into JSON.

Conclusion The practical solution to pass CRM data in JSON format to an LLM is to use Apex code (or a specialized Flow approach) to produce a JSON string, which the prompt can then merge and pass along. Hence, Option B is correct.

Salesforce AI Specialist Reference & Documents

Salesforce Documentation: Working with JSON in Apex Describes how to serialize and deserialize data using Apex for integration or AI prompts.

Salesforce AI Specialist Study Guide Emphasizes the need for custom logic (often in Apex) when complex data transformations (like JSON formatting) are required.

Context of the Question

Einstein Prompt Builder is a Salesforce feature that helps generate text (summaries, email content, responses) using AI models.

The question presents three potential use cases, asking which one best fits the capabilities of Einstein Prompt Builder.

Einstein Prompt Builder Typical Use Cases

Text Generation & Summaries: Great for writing or summarizing content, like responding to an email or generating text for a record field.

Why Not Forecast Future Sales Trends or Identify Potential High-Value Leads?

(Option A) Forecasting trends typically involves predictive analytics and modeling capabilities found in Einstein Discovery or standard reporting, not generative text solutions.

(Option B) Identifying leads for marketing campaigns involves lead scoring or analytics, again an Einstein Discovery or Lead Scoring scenario.

Sending a Personalized RFP Email (Option C) is a classic example of using generative AI to compose well-structured, context-aware text.

Conclusion Option C (Send reply to a request for proposal via a personalized email) is the best match for Einstein Prompt Builder's generative text functionality.

Salesforce AI Specialist Reference & Documents

Salesforce Documentation: Einstein Prompt Builder Overview Highlights how to use Prompt Builder to create and customize text-based responses, especially for email or record fields.

Salesforce AI Specialist Study Guide Explains that generative AI features in Salesforce are designed for creating or summarizing text, not for advanced predictive use cases (like forecasting or lead scoring).