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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Development Lifecycle | 15% | - Testing agents in Testing Center - Version control and maintenance - Monitoring, adoption and optimization - Deployment: sandbox to production |
| Topic 2: Multi-Agent Interoperability | 5% | - Model Context Protocol (MCP) - Agent API usage scenarios - Agent-to-agent communication |
| Topic 3: Data Cloud for Agentforce | 20% | - Agentforce Data Library types - Improving response accuracy with data grounding - Unstructured data processing: chunking, indexing - Retrievers and search types: keyword, vector, hybrid |
| Topic 4: Prompt Engineering | 20% | - Prompt management and user roles - Prompt Builder usage and use cases - Grounding techniques and best practices - Prompt template creation: field generation, flex types |
| Topic 5: AI Agents | 35% | - Deterministic controls: filters and variables - Channel integration: digital experience, email, Slack - Topics and actions configuration - Agent types: Service, Sales, Employee - Agent User security and permissions - Agent architecture and reasoning engine |
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NEW QUESTION # 32
Universal Containers wants to incorporate the current order fulfillment status into a prompt for a large language model (LLM). The order status is stored in the external enterprise resource planning (ERP) system. Which data grounding technique should the Agentforce Specialist recommend?
Answer: B
Explanation:
Context of the Requirement:
Universal Containers wants to pull in real-time order status data from an external ERP system into an LLM prompt.
Data Grounding in LLM Prompts:
Data grounding ensures the Large Language Model has access to the most current and relevant information. In Salesforce, one recommended approach is to use External Objects (via Salesforce Connect) when data resides outside of Salesforce.
NEW QUESTION # 33
Universal Containers wants to keep retrieval accurate as product documentation changes frequently. Which approach should the company implement?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract of AgentForce Documents:
According to the official AgentForce implementation guidelines and RAG (Retrieval-Augmented Generation) architecture within Salesforce, maintaining retrieval accuracy depends on ensuring that embeddings and indexed content remain synchronized with the most recent data. When product documentation or knowledge base content changes, the underlying text used for vector embeddings must also be updated to reflect the new information.
The AgentForce documentation clearly specifies that when content is modified, the recommended practice is to rebuild the search index. This process regenerates the document chunks, re- embeds them using the latest model, and updates the index used by the retrieval system. This ensures that queries return the most current and relevant responses aligned with the updated content.
NEW QUESTION # 34
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: B
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 # 35
Universal Containers (UC) wants to enable its sales team with automatic post-call visibility into mention of competitors, products, and other custom phrases. Which feature should the Agentforce Specialist set up to enable UC's sales team?
Answer: B
Explanation:
To enable Universal Containers' sales team with automatic post-call visibility into mentions of competitors, products, and custom phrases, the Agentforce Specialist should set up Call Insights.
Call Insights analyzes voice and video calls for key phrases, topics, and mentions, providing insights into critical aspects of the conversation. This feature automatically surfaces key details such as competitor mentions, product discussions, and custom phrases specified by the sales team.
Call Summaries provide a general overview of the call but do not specifically highlight keywords or topics.
Call Explorer is a tool for navigating through call data but does not focus on automatic insights.
NEW QUESTION # 36
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: A
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 # 37
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