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Salesforce AI-201 Exam Syllabus Topics:

Section	Weight	Objectives
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
Data Cloud & Grounding	20%	- Data integration for AI agents 1. Connecting external data sources 2. Using Data Cloud for retrieval augmentation
Testing, Deployment & Lifecycle	15%	- Agent lifecycle management 1. Sandbox to production deployment 2. Testing and validation strategies
Prompt Engineering	20%	- Prompt Builder usage 1. Prompt templates and configuration 2. Field generation and flex templates - Grounding techniques 1. Best practices for prompt design 2. Data grounding for reducing hallucinations
Governance & Observability	10%	- AI governance 1. Monitoring agent behavior 2. Trust layer and compliance concepts

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Salesforce Certified Agentforce Specialist Sample Questions (Q234-Q239):

NEW QUESTION # 234
Universal Containers wants to allow its service agents to query the current fulfillment status of an order with natural language. There is an existing autolaunched flow to query the Information from Oracle ERP, which is the system of record for the order fulfillment process. How should an Agentforce Specialist apply the power of conversational AI to this use case?

A. Create a Flex prompt template in Prompt Builder.
B. Create a custom Agent action which calls a flow.
C. Configure the Integration Flow Standard Action in Agent Builder.

Answer: B

Explanation:
Why is "Create a custom Agent action which calls a flow" the correct answer?
In Agentforce, the best way to allow service agents to query order fulfillment status from an external system (Oracle ERP) using natural language is to create a custom Agent action that invokes an existing autolaunched flow.
Key Considerations for This Approach:
Custom Agent Action Triggers the Flow
A custom Agent action is designed to call Salesforce flows, enabling external system integration.
The flow retrieves real-time fulfillment data from Oracle ERP and returns results to the agent.
Enables AI-Powered Query Execution
The Agent can understand natural language and map user utterances to the correct Agent action.
This ensures that agents receive accurate order fulfillment updates quickly.
No Need for Manual Data Entry
Instead of manually searching Oracle ERP, agents can query fulfillment status using AI-powered Agentforce workflows.

NEW QUESTION # 235
Universal Containers (UC) is preparing to use the Agentforce Testing Center to ensure the reliability of a new agent. UC has a CSV file with test cases and is reviewing the documentation to understand best practices and limitations. Which best practice should the company follow to avoid modifying CRM data while running tests in the Testing Center?

A. Limit the number of test cases to 50 per test to minimize data changes.
B. Run tests in the production environment to ensure real-time data accuracy.
C. Use the Testing Center only in the sandbox environment.

Answer: C

Explanation:
According to the AgentForce Testing and Validation Guidelines, all automated or large-scale test runs in Testing Center should be executed in a sandbox environment to prevent any unintended modifications to live CRM data.
Running tests in production can trigger record updates, create cases, or call actions that alter live data, violating best practice standards for safe validation. Testing in a sandbox ensures the environment mirrors production logic while maintaining data isolation.

NEW QUESTION # 236
Which action should an admin take if an Agentforce agent consistently fails on edge cases?

A. Disable logging
B. Add fallback and escalation paths
C. Remove human oversight
D. Increase autonomy

Answer: B

Explanation:
Fallbacks and escalation paths ensure reliability when agents encounter edge cases.

NEW QUESTION # 237
What is a key benefit of the Agent-to-Agent (A2A) protocol?

A. Provides a standardized framework for cross-vendor agent discovery and communication
B. Provides a standardized runtime engine for internal agent discovery and communication
C. Allows auto-onboard third-party agents without additional contracts, trust scores, or shared identity controls

Answer: A

Explanation:
The Agent-to-Agent (A2A) Protocol Overview describes A2A as a standardized framework for cross- vendor agent discovery and communication. The documentation specifies:
"A2A enables secure, interoperable communication between AI agents across vendors, platforms, and ecosystems, using standardized APIs and schemas for message exchange and capability discovery." This allows AgentForce agents to interact with external AI systems or partner agents while maintaining data governance and identity controls.

NEW QUESTION # 238
An Al Specialist is tasked with configuring a generative model to create personalized sales emails using customer data stored in Salesforce. The AI Specialist has already fine-tuned a large language model (LLM) on the OpenAI platform. Security and data privacy are critical concerns for the client. How should the Agentforce Specialist integrate the custom LLM into Salesforce?

A. Add the fine-tuned LLM in Einstein Studio Model Builder.
B. Enable model endpoint on OpenAl and make callouts to the model to generate emails.
C. Create an application of the custom LLM and embed it in Sales Cloud via iFrame.

Answer: A

NEW QUESTION # 239
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