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| Section | Weight | Objectives |
|---|---|---|
| Development Lifecycle | 15% | - Monitoring, adoption and optimization - Testing agents in Testing Center - Version control and maintenance - Deployment: sandbox to production |
| AI Agents | 35% | - Agent User security and permissions - Agent architecture and reasoning engine - Deterministic controls: filters and variables - Agent types: Service, Sales, Employee - Topics and actions configuration - Channel integration: digital experience, email, Slack |
| Data Cloud for Agentforce | 20% | - Unstructured data processing: chunking, indexing - Agentforce Data Library types - Retrievers and search types: keyword, vector, hybrid - Improving response accuracy with data grounding |
| Prompt Engineering | 20% | - Prompt Builder usage and use cases - Prompt template creation: field generation, flex types - Prompt management and user roles - Grounding techniques and best practices |
| Multi-Agent Interoperability | 5% | - Agent API usage scenarios - Model Context Protocol (MCP) - Agent-to-agent communication |
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NEW QUESTION # 76
What is the role of the large language model (LLM) in understanding intent and executing an Agent Action?
Answer: A
Explanation:
In Agentforce, the large language model (LLM), powered by the Atlas Reasoning Engine, interprets user requests and drives Agent Actions.
NEW QUESTION # 77
An Agentforce Specialist needs to create a prompt template that extracts the customer's name, phone number, and case number from a block of text, and nothing else. How should the Agentforce Specialist structure the prompt to ensure the large language model (LLM) doesn't include extra conversation or text?
Answer: C
Explanation:
According to the official AgentForce Prompt Template Design Guide, when extracting specific data such as customer name, phone number, and case number from unstructured text, the best practice is to use well-defined output instructions and examples. The documentation specifies:
"To ensure the LLM produces consistent and precise outputs, prompts must include explicit output formatting instructions and examples that demonstrate the desired structure." AgentForce guidance emphasizes structured output control to prevent the LLM from adding conversational or extraneous text. It states: "Always define your output schema clearly - for example, specify JSON or key-value pairs - and provide one or more examples of what the model should return. This ensures the model responds only with structured data and not natural language."
NEW QUESTION # 78
After a successful implementation of Agentforce Sates Agent with sales users. Universal Containers now aims to deploy it to the service team. Which key consideration should the Agentforce Specialist keep in mind for this deployment?
Answer: A
Explanation:
When deploying Einstein Agent (formerly Agentforce) from Sales to Service Cloud:
Agent Topics and Actions are context-specific. Service Cloud use cases (e.g., case resolution, knowledge retrieval) require validation of existing topics/actions to ensure alignment with service workflows.
NEW QUESTION # 79
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: A
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 # 80
Universal Containers (UC) recently rolled out Einstein Generative AI capabilities and has created a custom prompt to summarize case records. Users have reported that the case summaries generated are not returning the appropriate information. What is a possible explanation for the poor prompt performance?
Answer: A
Explanation:
UC's custom prompt for summarizing case records is underperforming, and we need to identify a likely cause.
NEW QUESTION # 81
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