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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prompt Engineering | 20% | - Prompt template creation: field generation, flex types - Prompt Builder usage and use cases - Prompt management and user roles - Grounding techniques and best practices |
| Topic 2: Multi-Agent Interoperability | 5% | - Model Context Protocol (MCP) - Agent-to-agent communication - Agent API usage scenarios |
| Topic 3: Development Lifecycle | 15% | - Version control and maintenance - Deployment: sandbox to production - Testing agents in Testing Center - Monitoring, adoption and optimization |
| Topic 4: Data Cloud for Agentforce | 20% | - Improving response accuracy with data grounding - Agentforce Data Library types - Unstructured data processing: chunking, indexing - Retrievers and search types: keyword, vector, hybrid |
| Topic 5: AI Agents | 35% | - Topics and actions configuration - Agent architecture and reasoning engine - Deterministic controls: filters and variables - Channel integration: digital experience, email, Slack - Agent types: Service, Sales, Employee - Agent User security and permissions |
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NEW QUESTION # 31
Coral Cloud Resorts (CCR) sees the agent forgot the dietary/activity preferences gathered earlier.
They need those preferences to persist throughout the session. What should CCR implement?
Answer: A
Explanation:
According to the AgentForce Session Memory and Context Management Guide, when specific customer preferences (such as dietary or activity selections) must persist throughout an interaction, the correct approach is to use a context variable. The documentation states: "Context variables retain information across the user session, enabling the agent to reference prior inputs or outputs without re-asking. They are ideal for persisting customer preferences, authentication data, or ongoing session parameters."
NEW QUESTION # 32
Universal Containers needs to provide insights on the usability of Agents to drive adoption in the organization. What should the Agentforce Specialist recommend?
Answer: C
Explanation:
To measure adoption and usability of Agents across the organization, Agentforce Analytics is the right tool. It provides dashboards and reports that track how Agents are being used, which actions are triggered most often, and overall performance trends. This data helps organizations drive adoption by identifying gaps, monitoring usage, and demonstrating business value.
NEW QUESTION # 33
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: C
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 # 34
Universal Containers is setting up the data library configuration within the Agentforce Builder.
What is true regarding Agentforce Data Libraries?
Answer: C
Explanation:
The correct statement regarding the configuration limit of Agentforce Data Libraries is that An agent can have only one data library assigned to it (C).
Agentforce Data Libraries are the mechanism by which an agent is "grounded" in an organization's internal, trusted knowledge (using Retrieval Augmented Generation or RAG). To ensure that the agent's focus remains sharp and its retrieval process is efficient and accurate, there is a one-to-one relationship between an Agentforce Agent and the Data Library it uses for grounding.
NEW QUESTION # 35
Universal Containers (UC) wants to use Generative AI Salesforce functionality to reduce Service Agent handling time by providing recommended replies based on the existing Knowledge articles.
On which AI capability should UC train the service agents?
Answer: C
Explanation:
Service Replies (specifically Einstein Service Replies) is the Salesforce Generative AI functionality designed to automatically draft responses for service agents in real-time, based on contextual information, including existing knowledge articles. This directly addresses Universal Containers' need to reduce handling time by providing recommended replies grounded in their knowledge base
NEW QUESTION # 36
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