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| Section | Weight | Objectives |
|---|---|---|
| Multi-Agent Interoperability | 5% | - Model Context Protocol (MCP) - Agent API usage scenarios - Agent-to-agent communication |
| Development Lifecycle | 15% | - Monitoring, adoption and optimization - Deployment: sandbox to production - Version control and maintenance - Testing agents in Testing Center |
| Prompt Engineering | 20% | - Prompt template creation: field generation, flex types - Prompt Builder usage and use cases - Grounding techniques and best practices - Prompt management and user roles |
| AI Agents | 35% | - Agent architecture and reasoning engine - Agent types: Service, Sales, Employee - Agent User security and permissions - Topics and actions configuration - Deterministic controls: filters and variables - Channel integration: digital experience, email, Slack |
| Data Cloud for Agentforce | 20% | - Agentforce Data Library types - Unstructured data processing: chunking, indexing - Retrievers and search types: keyword, vector, hybrid - Improving response accuracy with data grounding |
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NEW QUESTION # 68
How should an organization use the Einstein Trust layer to audit, track, and view masked data?
Answer: C
Explanation:
The Einstein Trust Layer is designed to ensure transparency, compliance, and security for organizations leveraging Salesforce's AI and generative AI capabilities. Specifically, for auditing, tracking, and viewing masked data, organizations can utilize:
Audit Trail in Data Cloud: The audit trail captures and stores all prompts submitted to large language models (LLMs), ensuring that sensitive or masked data interactions are logged. This allows organizations to monitor and audit all AI-generated outputs, ensuring that data handling complies with internal and regulatory guidelines. The Data Cloud provides the infrastructure for managing and accessing this audit data.
NEW QUESTION # 69
An Agentforce Specialist is creating a custom agent action. The topic is selected correctly, but the action is not. Which setting should the Agentforce Specialist test and iterate on to ensure the action performs as expected?
Answer: C
Explanation:
Per the AgentForce Custom Action Development Guide, if a topic is correctly triggered but the wrong action is executed, the issue typically lies in action instructions. The documentation notes:
"Action instructions provide the LLM with explicit guidance on when and how to use a given action. Poorly written or ambiguous instructions can cause the reasoning engine to select an incorrect action, even within the right topic."
NEW QUESTION # 70
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: A
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 # 71
Which configuration must An Agentforce complete for users to access generative Al-enabled fields in the Salesforce mobile app?
Answer: A
Explanation:
Universal Containers (UC) has generative AI-enabled fields that users can access in the desktop experience.
The Agentforce Specialist needs these same fields to be visible and usable in the Salesforce Mobile App.
NEW QUESTION # 72
When a verified customer in a help center says, "I want to upgrade my service plan," an AI agent needs to complete the following tasks:
- Verify identity and entitlement.
- Create a new quote.
- Calculate a prorated upgrade amount.
- Escalate to an Account Executive (AE) only if the reorder exceeds USD
25,000.
Which type of agent should an AgentForce Specialist build to support this use case?
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract of AgentForce Documents:
According to the AgentForce Specialist Implementation Guide and Agent Type Configuration Reference, this scenario represents a revenue-generating interaction where the AI agent is directly handling an upsell process. The tasks include verifying the customer's entitlement, generating a new quote, and calculating a prorated amount -- all of which align with the Sales Agent configuration type in AgentForce.
The Sales Agent is specifically designed to manage lead conversion, quoting, upselling, renewals, and escalation logic for higher-value opportunities. AgentForce documentation emphasizes that when an interaction involves quote generation, pricing calculations, or escalations to an Account Executive for large deal handling, the correct design is a Sales Agent.
NEW QUESTION # 73
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