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| Section | Weight | Objectives |
|---|---|---|
| Data Cloud for Agentforce | 20% | - Improving response accuracy with data grounding - Agentforce Data Library types - Retrievers and search types: keyword, vector, hybrid - Unstructured data processing: chunking, indexing |
| Prompt Engineering | 20% | - Prompt Builder usage and use cases - Grounding techniques and best practices - Prompt management and user roles - Prompt template creation: field generation, flex types |
| Development Lifecycle | 15% | - Deployment: sandbox to production - Version control and maintenance - Monitoring, adoption and optimization - Testing agents in Testing Center |
| AI Agents | 35% | - Agent architecture and reasoning engine - Agent User security and permissions - Deterministic controls: filters and variables - Topics and actions configuration - Agent types: Service, Sales, Employee - Channel integration: digital experience, email, Slack |
| Multi-Agent Interoperability | 5% | - Model Context Protocol (MCP) - Agent API usage scenarios - Agent-to-agent communication |
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NEW QUESTION # 276
A company wants to retrieve patient history details to augment the Al agent response. [he company wants to leverage the Data Cloud search index feature. What is best practice when considering retrieval-augrmented generation (RAG) for information that may contain personally identifiable information (PII)?
Answer: C
Explanation:
According to the AgentForce Data Governance and RAG Security Guidelines, when implementing retrieval-augmented generation (RAG) using Data Cloud search indexes, best practice is to ensure that personally identifiable information (PII) and other sensitive data are never indexed or embedded in the retrieval system.
The documented recommendation is to mask or exclude sensitive fields before creating embeddings or indexing content. This prevents the large language model (LLM) from accessing or generating responses that could inadvertently expose confidential information. Masking can include redacting names, IDs, contact details, or any regulated medical information.
NEW QUESTION # 277
An Agentforce wants to ground a new prompt template with the User related list. What should the Agentforce Specialist consider?
Answer: B
Explanation:
Salesforce has restrictions on which objects and related lists can be used for grounding prompt templates. This is likely due to security and privacy concerns related to user data.
While it might seem intuitive to use the User related list to provide context to the LLM, Salesforce prevents this to ensure that sensitive user information is not inadvertently exposed or misused.
Therefore, the Agentforce Specialist needs to explore alternative ways to incorporate the necessary user information into the prompt template, perhaps by using other related objects or fields that are supported.
NEW QUESTION # 278
An Agentforce agent must retrieve data from Salesforce and an external system. What is the recommended approach?
Answer: C
Explanation:
External data access should be handled via Apex or Flow integrations, not direct LLM training.
NEW QUESTION # 279
A sales manager is using Agent Assistant to streamline their daily tasks. They ask the agent to Show me a list of my open opportunities. How does the large language model (LLM) in Agentforce identify and execute the action to show the sales manager a list of open opportunities?
Answer: C
Explanation:
Agentforce's LLM dynamically interprets natural language requests (e.g., "Show me open opportunities"), generates an execution plan using the planner service, and retrieves data via actions (e.g., querying Salesforce records). This contrasts with static rules (B) or rigid dialog patterns (C), which lack contextual adaptability. Salesforce documentation highlights the planner's role in converting intents into actionable steps while adhering to security and business logic.
NEW QUESTION # 280
Universal Containers implemented Agent for its users. One user complains that Agent is not deleting activities from the past 7 days. What is the reason for this issue?
Answer: A
Explanation:
Agent currently supports various actions like creating and updating records but does not support the Delete Record action. Therefore, the user's request to delete activities from the past 7 days cannot be fulfilled using Agent.
Unsupported Action: The inability to delete records is due to the current limitations of Agent's supported actions. It is designed to assist with tasks like data retrieval, creation, and updates, but for security and data integrity reasons, it does not facilitate the deletion of records.
User Permissions: Even if the user has the necessary permissions to delete records within Salesforce, Agent itself does not have the capability to execute delete operations.
NEW QUESTION # 281
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