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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prompt Engineering | 20% | - Grounding techniques
|
| Topic 2: AI Agents | 35% | - Agent setup and orchestration
|
| Topic 3: Governance & Observability | 10% | - AI governance
|
| Topic 4: Data Cloud & Grounding | 20% | - Data integration for AI agents
|
| Topic 5: Testing, Deployment & Lifecycle | 15% | - Agent lifecycle management
|
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NEW QUESTION # 244
Universal Containers' sales team engages in numerous video sales calls with prospects across the nation. Sales management wants an easy way to understand key information such as deal terms or customer sentiments. Which Einstein Generative AI feature should An Agentforce recommend for this request?
Answer: C
Explanation:
Einstein Conversation Insights analyzes sales calls (including video and voice) to capture key moments, deal terms, competitor mentions, and customer sentiment. It helps sales management quickly understand what was discussed without manually reviewing the entire call.This feature ensures actionable insights are delivered directly into the Salesforce CRM, allowing sales managers to gain a concise overview without manually reviewing long recordings.
NEW QUESTION # 245
For an Agentforce Data Library that contains uploaded files, what occurs once it is created and configured?
Answer: B
Explanation:
In Salesforce Agentforce, a Data Library is a feature that allows organizations to upload files (e.g., PDFs, documents) to be used as grounding data for AI-driven agents. Once the Data Library is created and configured, the uploaded files are indexed to make their content searchable and usable by the AI (e.g., for retrieval-augmented generation or prompt enhancement). The key question is where this indexing occurs. Salesforce Agentforce integrates tightly with Data Cloud, a unified data platform that includes a vector database optimized for storing and indexing unstructured data like uploaded files. When a Data Library is set up, the files are ingested and indexed into Data Cloud's vector database, enabling the AI to efficiently retrieve relevant information from them during conversations or actions.
NEW QUESTION # 246
Universal Containers has a strict change management process that requires all possible configuration to be completed in a sandbox which will be deployed to production. The Agentforce Specialist is tasked with setting up Work Summaries for Enhanced Messaging. Einstein Generative AI is already enabled in production, and the Einstein Work Summaries permission set is already available in production. Which other configuration steps should the Agentforce Specialist take in the sandbox that can be deployed to the production org?
Answer: C
Explanation:
Universal Containers (UC) has a strict change management process that requires all possible configuration be completed in a sandbox and deployed to Production.
Einstein Generative AI is already enabled in Production, and the "Einstein Work Summaries" permission set is already available in Production.
The Agentforce Specialist needs to configure Work Summaries for Enhanced Messaging in the sandbox.
What Can Actually Be Deployed from Sandbox to Production?
Custom Fields: Metadata that is easily created in sandbox and then deployed.
Quick Actions: Also metadata-based and can be deployed from sandbox to production.
Layout Components: Page layout changes (such as adding the Wrap Up component) can be added to a change set or deployment package.
NEW QUESTION # 247
Based on the user utterance, 'Show me all the customers in New York', which standard Agent action will the planner service use?
Answer: A
Explanation:
Why is Query Records the Correct Answer?
In Agentforce, the Planner Service is responsible for interpreting user requests and selecting the appropriate Copilot Action to fulfill them. When a user issues a command like:
"Show me all the customers in New York", the system must retrieve a list of customers filtered by location.
The Query Records action is designed precisely for this purpose.
Key Features of Query Records in Agentforce:
Retrieves Data Based on Specific Field Values
This action fetches Salesforce records that match a set of criteria, such as customers located in New York.
Uses standard or custom object fields (e.g., BillingState = 'New York').
Works with Large Language Models (LLMs) and Copilot Actions
When a user asks for filtered data, Query Records is the default action assigned by the Planner Service.
Optimized for Structured Data Retrieval
Ensures AI retrieves relevant CRM records quickly and accurately.
NEW QUESTION # 248
Universal Containers (UC) wants to implement an AI-powered customer service agent that can:
- Retrieve proprietary policy documents that are stored as PDFs.
- Ensure responses are grounded in approved company data, not generic
LLM knowledge.
What should UC do first?
Answer: B
Explanation:
To implement an AI-powered customer service agent that retrieves proprietary policy documents (stored as PDFs) and ensures responses are grounded in approved company data, UC must first establish a foundation for the AI to access and use this data. The Agentforce Data Library (Option A) is the correct starting point. A Data Library allows UC to upload PDFs containing policy documents, index them into Salesforce Data Cloud's vector database, and make them available for AI retrieval. This setup ensures the agent can perform Retrieval-Augmented Generation (RAG), grounding its responses in the specific, approved content from the PDFs rather than relying on generic LLM knowledge, directly meeting UC's requirements.
NEW QUESTION # 249
......
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