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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prompt Engineering | 30% | - Prompt Builder & Template Creation
|
| Topic 2: Einstein Trust Layer & Security | - Data Masking
| |
| Topic 3: Reasoning Engine & AI Actions | - Workflow mastery
| |
| Topic 4: Agent Force Concepts | 30% | - Einstein GPT Integration
|
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NEW QUESTION # 302
Universal Containers (UC) stores case details and updates in several custom fields and custom objects related to the case. UC would like its Agentforce Service Agent to be able to provide information in these fields and related records as part of an answer back to its customers when the customer is asking for updates. Which best practice should UC follow to grant access to this information for the Agentforce Service Agent?
Answer: C
Explanation:
Per the AgentForce Security and Permission Management Guide, the
AgentForceServiceAgentUserPsg (Permission Set Group) controls access privileges for Service Agents, including which Salesforce objects, fields, and related data they can read or interact with.
When extending an agent's access to additional custom fields or related objects, the documented best practice is to update the existing permission set group assigned to that agent type rather than creating new or profile-based permissions. This approach maintains centralized permission governance, ensures license alignment, and avoids conflicts or redundancy across multiple permission layers.
NEW QUESTION # 303
Universal Containers is interested in using Call Explorer to quickly gain insights from meetings recorded by its sales team. What should the Agentforce Specialist be aware of before enabling this feature?
Answer: B
Explanation:
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.
NEW QUESTION # 304
An Al Specialist is tasked with configuring a generative model to create personalized sales emails using customer data stored in Salesforce. The AI Specialist has already fine-tuned a large language model (LLM) on the OpenAI platform. Security and data privacy are critical concerns for the client. How should the Agentforce Specialist integrate the custom LLM into Salesforce?
Answer: B
Explanation:
Since security and data privacy are critical, the best option for the Agentforce Specialist is to integrate the fine-tuned LLM (Large Language Model) into Salesforce by adding it to Einstein Studio Model Builder. Einstein Studio allows organizations to bring their own AI models (BYOM), ensuring the model is securely managed within Salesforce's environment, adhering to data privacy standards.
NEW QUESTION # 305
Universal Containers, dealing with a high volume of chat inquiries, implements Einstein Work Summaries to boost productivity. After an agent-customer conversation, which additional information does Einstein generate and fill, apart from the "summary"'
Answer: A
Explanation:
Einstein Work Summaries automatically generate concise summaries of customer interactions (e.g., chat transcripts). Beyond the "summary" field, it extracts and populates Issue (key problem discussed) and Resolution (action taken to resolve the issue). These fields help agents and upervisors quickly grasp the conversation's context without reviewing the full transcript.
NEW QUESTION # 306
What is a valid use case for Data Cloud retrievers?
Answer: A
Explanation:
Salesforce Data Cloud integrates with Agentforce to provide real-time, unified data access for AI- driven applications. Data Cloud retrievers are specialized components that fetch relevant data from Data Cloud's vector database-a storage system optimized for semantic search and retrieval- to enhance agent responses or actions. A valid use case, as described in Option A, is using these retrievers to return pertinent data (e.g., customer purchase history, support tickets) from the vector database to augment a prompt. This process, often part of Retrieval-Augmented Generation (RAG), allows the LLM to generate more accurate, context-aware responses by grounding its output in structured, searchable data stored in Data Cloud.
NEW QUESTION # 307
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