What should Universal Containers consider when deploying an Agentforce Service Agent with multiple topics and Agent Actions to production?
Correct Answer:
B
Comprehensive and Detailed In-Depth Explanation:UC is deploying an Agentforce Service Agent with multiple topics and actions to production. Let??s assess deployment considerations.
✑ Option A: Deploy agent components without a test run in staging, relying on production data for reliable results. Sandbox configuration alone ensures seamless production deployment.Skipping staging tests is risky and against best practices. Sandbox configuration doesn??t guarantee production success without validation, making this incorrect.
✑ Option B: Ensure all dependencies are included, Apex classes meet 75% test
coverage, and configuration settings are aligned with production. Plan for version management and post-deployment activation.This is a comprehensive approach: dependencies (e.g., flows, Apex) must be deployed, Apex requires 75% coverage, and production settings (e.g., permissions, channels) must align. Version management tracks changes, and post-deployment activation ensures controlled rollout. This aligns with Salesforce deployment best practices for Agentforce, making it the correct answer.
✑ Option C: Deploy flows or Apex after agents, topics, and Agent Actions to avoid deployment failures and potential production agent issues requiring complete redeployment.Deploying components separately risks failures (e.g., actions needing flows failing). All components should deploy together for consistency, making this incorrect.
Why Option B is Correct:Option B covers all critical deployment considerations for a robust Agentforce rollout, as per Salesforce guidelines.
References:
✑ Salesforce Agentforce Documentation: Deploy Agents to Production – Lists dependencies and coverage.
✑ Trailhead: Deploy Agentforce Agents – Emphasizes testing and activation planning.
✑ Salesforce Help: Agentforce Deployment Best Practices – Confirms comprehensive approach.
Universal Containers Is Interested In Improving the sales operation efficiency by analyzing their data using Al-powered predictions in Einstein Studio.
Which use case works for this scenario?
Correct Answer:
B
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
Universal Containers (UC) is using standard Service AI Grounding. UC created a custom rich text field to be used with Service AI Grounding.
What should UC consider when using standard Service AI Grounding?
Correct Answer:
B
Service AI Grounding retrieves data from Salesforce objects to ground AI- generated responses. Key considerations:
✑ Field Types: Standard Service AI Grounding supports String and Text Area fields.
Custom rich text fields (e.g., RichTextArea) are not supported, making Option B correct.
✑ Objects: While Service AI Grounding primarily uses Case and Knowledge objects (Option A), the limitation here is the field type, not the object.
✑ Visibility: Service AI Grounding respects user permissions and sharing settings
unless overridden (Option C is incorrect).
References:
✑ Salesforce Help: Service AI Grounding Requirements
✑ Explicitly states support for "Text Area and String fields" only.
Universal Containers (UC) has a mature Salesforce org with a lot of data in cases and Knowledge articles. UC is concerned that there are many legacy fields, with data that might not be applicable for Einstein AI to draft accurate email responses.
Which solution should UC use to ensure Einstein AI can draft responses from a defined data source?
Correct Answer:
A
Service AI Grounding is the solution that Universal Containers should use to ensure Einstein AI drafts responses based on a well-defined data source. Service AI Grounding allows the AI model to be anchored in specific, relevant data sources, ensuring that any AI-generated responses (e.g., email replies) are accurate, relevant, and drawn from up-to-date information, such as Knowledge articles or cases.
Given that UC has legacy fields and outdated data, Service AI Grounding ensures that only the valid and applicable data is used by Einstein AI to craft responses. This helps improve the relevance of responses and avoids inaccuracies caused by outdated or irrelevant fields. Work Summaries and Service Replies are useful features but do not address the need for grounding AI outputs in specific, current data sources like Service AI Grounding does. For more details, you can refer to Salesforce??s Service AI Grounding documentation for managing AI-generated content based on accurate data sources.
Universal Containers wants to leverage the Record Snapshots grounding feature in a prompt template. What preparations are required?
Correct Answer:
B
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.
