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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Multi-Agent Interoperability | 5% | - Agent-to-agent communication - Model Context Protocol (MCP) - Agent API usage scenarios |
| Topic 2: Prompt Engineering | 20% | - Prompt management and user roles - Grounding techniques and best practices - Prompt Builder usage and use cases - Prompt template creation: field generation, flex types |
| Topic 3: AI Agents | 35% | - Agent architecture and reasoning engine - Agent types: Service, Sales, Employee - Agent User security and permissions - Topics and actions configuration - Channel integration: digital experience, email, Slack - Deterministic controls: filters and variables |
| Topic 4: Development Lifecycle | 15% | - Deployment: sandbox to production - Monitoring, adoption and optimization - Version control and maintenance - Testing agents in Testing Center |
| Topic 5: Data Cloud for Agentforce | 20% | - Agentforce Data Library types - Retrievers and search types: keyword, vector, hybrid - Improving response accuracy with data grounding - Unstructured data processing: chunking, indexing |
>> AI-201 Zertifizierungsprüfung <<
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283. Frage
Universal Containers' sales team engages in numerous video sales calls with prospects across the nation. Sales management wants an easy way to understand key information such as deal terms or customer sentiments. Which Einstein Generative AI feature should An Agentforce recommend for this request?
Antwort: C
Begründung:
Einstein Conversation Insights analyzes sales calls (including video and voice) to capture key moments, deal terms, competitor mentions, and customer sentiment. It helps sales management quickly understand what was discussed without manually reviewing the entire call.This feature ensures actionable insights are delivered directly into the Salesforce CRM, allowing sales managers to gain a concise overview without manually reviewing long recordings.
284. Frage
An Agentforce has created a copilot custom action using flow as the reference action type.
However, it is not delivering the expected results to the conversation preview, and therefore needs troubleshooting. What should the Agentforce Specialist do to identify the root cause of the problem?
Antwort: A
Begründung:
When troubleshooting a copilot custom action using flow as the reference action type, enabling dynamic debugging within Copilot Builder's Dynamic Panel is the most effective way to identify the root cause. By turning on dynamic debugging, the Agentforce Specialist can see detailed logs showing both the inputs and outputs of the flow, which helps identify where the action might be failing or not delivering the expected results.
285. Frage
Universal Containers' data science team is hosting a generative large language model (LLM) on Amazon Web Services (AWS). What should the team use to access externally-hosted models in the Salesforce Platform?
Antwort: B
Begründung:
To access externally-hosted models, such as a large language model (LLM) hosted on AWS, the Model Builder in Salesforce is the appropriate tool. Model Builder allows teams to integrate and deploy external AI models into the Salesforce platform, making it possible to leverage models hosted outside of Salesforce infrastructure while still benefiting from the platform's native AI capabilities.
286. Frage
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?
Antwort: B
Begründung:
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.
287. Frage
Universal Containers wants to test agents while preserving real data and isolating from production. Which environment should the company use with Testing Center?
Antwort: B
Begründung:
The AgentForce Testing Center Implementation Guide specifies that organizations should perform all structured and regression testing in a sandbox environment replicated from production. The guide explains:
"Testing Center supports running tests safely in sandbox environments that mirror production data. This ensures realistic test conditions without impacting live systems or data."
288. Frage
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