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Salesforce AI-201 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI Agents	35%	- Agent behavior configuration 1. Agent reasoning engine concepts 2. Filters and variables for deterministic behavior - Agent setup and orchestration 1. Standard and custom agent actions 2. Standard and custom topics
Topic 2: Data Cloud & Grounding	20%	- Data integration for AI agents 1. Using Data Cloud for retrieval augmentation 2. Connecting external data sources
Topic 3: Governance & Observability	10%	- AI governance 1. Trust layer and compliance concepts 2. Monitoring agent behavior
Topic 4: Testing, Deployment & Lifecycle	15%	- Agent lifecycle management 1. Testing and validation strategies 2. Sandbox to production deployment
Topic 5: Prompt Engineering	20%	- Grounding techniques 1. Best practices for prompt design 2. Data grounding for reducing hallucinations - Prompt Builder usage 1. Field generation and flex templates 2. Prompt templates and configuration

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Salesforce Certified Agentforce Specialist AI-201 Prüfungsfragen mit Lösungen (Q239-Q244):

239. Frage
An Agentforce Specialist builds a new Service Agent that uses a custom action built on a flow.
The agent has been tested in a sandbox and is now ready to deploy. What is a key consideration regarding the activation status of the agent in the production environment?

A. The agent will automatically be activated upon successful deployment.
B. The agent will be activated automatically only if the flow is also active.
C. The agent must be manually activated in production, regardless of its status in the sandbox.

Antwort: C

Begründung:
According to the AgentForce Deployment and Lifecycle Management Guide, when an agent is deployed from a sandbox to a production environment, activation does not carry over automatically. The documentation clarifies: "Each environment maintains its own activation state.
Agents must be manually activated in production after deployment to ensure controlled rollout and compliance validation."

240. Frage
What is one key purpose of action instructions when creating a custom agent action in Agentforce?

A. Action instructions help the reasoning engine decide which action to use.
B. Action instructions define the temperature of the large language model (LLM) powering the Reasoning Engine.
C. Action instructions tell the user how to call this action in a conversation.

Antwort: A

Begründung:
According to the AgentForce Action Design and Configuration Guide, action instructions serve as the directive text that informs the Reasoning Engine about when and how to invoke a specific action. The documentation states: "Action instructions guide the reasoning engine by describing the action's purpose, inputs, and when it should be selected. Clear, concise instructions improve the LLM's accuracy in mapping user intent to the correct action."

241. Frage
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?

A. Agent-to-Agent (A2A) protocol
B. Hugging Face
C. Model Context Protocol (MCP)

Antwort: C

Begründung:
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."

242. Frage
Which metric provides the MOST insight into whether an Agentforce agent is delivering business value?

A. Prompt length
B. Task success and resolution rate
C. Model response time
D. Number of conversations

Antwort: B

Begründung:
Business value is best measured by successful task completion and resolution outcomes.

243. Frage
Universal Containers has multiple Salesforce orgs, each with a unique customer service agent where a verification agent must pass customer identity data to downstream agents handling account modifications. The customer ID must remain secure and persistent across agent handoffs without exposure to large language model (LLM) modification. What is the most appropriate configuration?

A. Use the Agent API to start the downstream agent's session and pass the verified customer ID as a read-only context variable, ensuring security and preventing LLM alteration.
B. Implement a custom object to temporarily store verification status and have each agent query it via SOQL actions during execution.
C. Store customer identity information in conversation variables created by the first agent and have other agents read those same conversation variables.

Antwort: A

Begründung:
The AgentForce Inter-Agent Communication and Security Configuration Guide specifies that when sensitive identity data (like a verified customer ID) must be shared between agents, the correct approach is to use the Agent API to initiate the downstream agent's session. The verified data should be passed as a read-only context variable, ensuring persistence across sessions while preventing modification by the large language model (LLM).
This setup maintains data integrity and security compliance by isolating sensitive variables from the LLM's reasoning layer. Context variables passed via the Agent API are immutable during runtime, ensuring they cannot be altered or exposed in agent-generated responses.

244. Frage
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