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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Cloud for Agentforce | 20% | - Agentforce Data Library types - Unstructured data processing: chunking, indexing - Improving response accuracy with data grounding - Retrievers and search types: keyword, vector, hybrid |
| Topic 2: Multi-Agent Interoperability | 5% | - Agent-to-agent communication - Model Context Protocol (MCP) - Agent API usage scenarios |
| Topic 3: Development Lifecycle | 15% | - Testing agents in Testing Center - Monitoring, adoption and optimization - Version control and maintenance - Deployment: sandbox to production |
| Topic 4: Prompt Engineering | 20% | - Grounding techniques and best practices - Prompt management and user roles - Prompt Builder usage and use cases - Prompt template creation: field generation, flex types |
| Topic 5: AI Agents | 35% | - Agent User security and permissions - Channel integration: digital experience, email, Slack - Agent types: Service, Sales, Employee - Agent architecture and reasoning engine - Topics and actions configuration - Deterministic controls: filters and variables |
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NEW QUESTION # 27
Which action should an admin take if an Agentforce agent consistently fails on edge cases?
Answer: A
Explanation:
Fallbacks and escalation paths ensure reliability when agents encounter edge cases.
NEW QUESTION # 28
A developer is using the Salesforce CLI to deploy agent components from a sandbox to production. They recently made a change to several topics, instructions, and actions. Which metadata component should the developer include in their package.xml file that contains all of the topics and actions an agent will interact with?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract of AgentForce documents:
The metadata component that contains the complete configuration for an Agentforce Agent, including references to all its topics and actions, is GenAiPlannerBundle (A).
In Salesforce development using the Metadata API or the Salesforce CLI, an Agentforce Agent is represented by a planner metadata type.
In recent Salesforce API versions (v64 and above, as of this knowledge base), the core agent component is the GenAiPlannerBundle. This acts as the container or planner that defines the agent's reasoning engine and bundles together all the necessary references to the Topics (represented by the GenAiPlugin metadata type) and the Actions (represented by the GenAiFunction metadata type) that the agent is allowed to execute. Deploying the GenAiPlannerBundle is essential for deploying a complete, updated Agentforce Agent configuration.
Prior to version 64, the component was named GenAiPlanner, which serves the same conceptual role. Since modern DevOps pipelines strive for the latest capabilities, GenAiPlannerBundle is the most current and accurate answer for a full agent deployment.
NEW QUESTION # 29
Universal Containers wants to utilize Agentforce for Sales to help sales reps reach their sales quotas by providing AI-generated plans containing guidance and steps for closing deals. Which feature meets this requirement?
Answer: B
Explanation:
Universal Containers (UC) aims to leverage Agentforce for Sales to assist sales reps with AI- generated plans that provide guidance and steps for closing deals.
NEW QUESTION # 30
Which part of the Einstein Trust Layer architecture leverages an organization's own data within a large language model (LLM) prompt to confidently return relevant and accurate responses?
Answer: A
Explanation:
Dynamic Grounding in the Einstein Trust Layer architecture ensures that large language model (LLM) prompts are enriched with organization-specific data (e.g., Salesforce records, Knowledge articles) to generate accurate and relevant responses. By dynamically injecting contextual data into prompts, it reduces hallucinations and aligns outputs with trusted business data.
NEW QUESTION # 31
Universal Containers (UC) wants to improve the productivity of its sales team with generative AI technology. However, UC is concerned that public AI virtual assistants lack adequate company data to general useful responses. Which solution should UC consider?
Answer: C
Explanation:
Universal Containers (UC) wants to harness generative AI to boost sales productivity. They are wary of public AI virtual assistants (like generic chatbots) that lack sufficient UC-specific data to generate useful business responses.
NEW QUESTION # 32
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