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| Section | Weight | Objectives |
|---|---|---|
| AI Agents | 35% | - Agent User security and permissions - Deterministic controls: filters and variables - Agent types: Service, Sales, Employee - Agent architecture and reasoning engine - Topics and actions configuration - Channel integration: digital experience, email, Slack |
| Multi-Agent Interoperability | 5% | - Model Context Protocol (MCP) - Agent-to-agent communication - Agent API usage scenarios |
| Development Lifecycle | 15% | - Monitoring, adoption and optimization - Deployment: sandbox to production - Testing agents in Testing Center - Version control and maintenance |
| Data Cloud for Agentforce | 20% | - Retrievers and search types: keyword, vector, hybrid - Agentforce Data Library types - Improving response accuracy with data grounding - Unstructured data processing: chunking, indexing |
| Prompt Engineering | 20% | - Prompt management and user roles - Prompt template creation: field generation, flex types - Grounding techniques and best practices - Prompt Builder usage and use cases |
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NEW QUESTION # 99
Universal Containers is building a digital shopping assistant that needs to dynamically generate product recommendations using information from the company's external product recommendation predictive model through APIs. Which Agentforce capability should make it easier for the agent to consume the external product recommendation tool?
Answer: C
Explanation:
Per the AgentForce Integration and AI Architecture Guide, the Model Context Protocol (MCP) enables agents to interact dynamically with external predictive or AI models. The documentation states:
"Through MCP, agents can discover, connect, and invoke external models via standardized schema definitions. This allows agents to use third-party tools-like recommendation engines or classifiers-without pre-coding fixed API calls."
NEW QUESTION # 100
Before deploying an Agentforce agent to production, which step is MOST critical?
Answer: B
Explanation:
Testing with real scenarios and monitoring outputs ensures accuracy, safety, and business alignment.
NEW QUESTION # 101
Universal Containers needs to create Data Cloud reports to understand agent behavior. Which data lake object (DLO) represents an overarching container capturing contiguous interactions with one or more Al agents?
Answer: B
Explanation:
Per the AgentForce Data Cloud Integration Guide, the AI Agent Session (AIAGENTSESSION) object represents an overarching container that tracks a continuous interaction between one or more AI agents and a user. The document describes: "AI Agent Session is the parent container for a contiguous set of AI Agent interactions. It captures metadata, start and end times, and the relationship to individual messages exchanged during the session."
NEW QUESTION # 102
What is best practice when refining Agent custom action instructions?
Answer: C
Explanation:
When refining Agent custom action instructions, it is considered best practice to provide examples of user messages that are expected to trigger the action. This helps ensure that the custom action understands a variety of user inputs and can effectively respond to the intent behind the messages.
NEW QUESTION # 103
What is the purpose of applying filters in a custom retriever configuration?
Answer: B
Explanation:
The AgentForce Retriever Configuration Guide specifies that filters are used to refine and constrain search results within a retriever setup. Filters operate by applying conditions (up to 10) on indexed fields such as document type, category, region, or update date. This targeted filtering ensures that the retrieved data is highly relevant to the current user query or context.
For instance, an AgentForce retriever could be configured to include only documents tagged as
"Active" or within a specific product line, reducing noise and improving grounding accuracy for the LLM. This mechanism supports precision retrieval, which directly improves both the accuracy and reliability of generated responses.
NEW QUESTION # 104
......
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