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Salesforce AI-201 Exam Syllabus Topics:

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

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Salesforce Certified Agentforce Specialist Sample Questions (Q185-Q190):

NEW QUESTION # 185
Universal Containers plans to enhance the customer support team's productivity using AI. Which specific use case necessitates the use of Prompt Builder?

A. Creating a draft of a support bulletin post for new product patches
B. Estimating support ticket volume based on historical data and seasonal trends
C. Creating an Al-generated customer support agent performance score

Answer: A

Explanation:
The use case that necessitates the use of Prompt Builder is creating a draft of a support bulletin post for new product patches. Prompt Builder allows the Agentforce Specialist to create and refine prompts that generate specific, relevant outputs, such as drafting support communication based on product information and patch details.

NEW QUESTION # 186
Universal Containers (UC) has a mature Salesforce org with a lot of data in cases and Knowledge articles. UC is concerned that there are many legacy fields, with data that might not be applicable for Einstein AI to draft accurate email responses. Which solution should UC use to ensure Einstein AI can draft responses from a defined data source?

A. Work Summaries
B. Service AI Grounding
C. Service Replies

Answer: B

Explanation:
Service AI Grounding is the solution that Universal Containers should use to ensure Einstein AI drafts responses based on a well-defined data source. Service AI Grounding allows the AI model to be anchored in specific, relevant data sources, ensuring that any AI-generated responses (e.g., email replies) are accurate, relevant, and drawn from up-to-date information, such as Knowledge articles or cases.
Given that UC has legacy fields and outdated data, Service AI Grounding ensures that only the valid and applicable data is used by Einstein AI to craft responses. This helps improve the relevance of responses and avoids inaccuracies caused by outdated or irrelevant fields.
Work Summaries and Service Replies are useful features but do not address the need for grounding AI outputs in specific, current data sources like Service AI Grounding does.
For more details, you can refer to Salesforce's Service AI Grounding documentation for managing AI-generated content based on accurate data sources.

NEW QUESTION # 187
Universal Containers wants to assign agents to improve department efficiency. Which configuration ensures the right tasks are handled by the right agents?

A. SDR Agent for lead qualification, Service Agent for support tickets, Employee Agent for HR requests
B. Sales Coach Agent for lead and service Agent for HR requests, and Support tickets to ensure cases are available
C. One Service Agent to efficiently handle each of these scenarios, which reduces the number of agent types needed for support

Answer: A

Explanation:
According to the AgentForce Product Overview and Deployment Guide, Salesforce recommends using purpose-built agents to maximize efficiency across departments. The documentation states:
"Each AgentForce agent type is optimized for a specific function - SDR Agent for sales development and lead nurturing, Service Agent for customer service and support cases, and Employee Agent for internal HR, IT, and productivity tasks." This separation ensures that each team benefits from a domain-specific agent equipped with the correct data access and actions.

NEW QUESTION # 188
An Agentforce is considering using a Field Generation prompt template type. What should the Agentforce Specialist check before creating the Field Generation prompt to ensure it is possible for the field to be enabled for generative AI?

A. That the field chosen must be a rich text field with 255 characters or more.
B. That the org is set to API version 59 or higher
C. That the Lightning page layout where the field will reside has been upgraded to Dynamic Forms

Answer: C

Explanation:
Before creating a Field Generation prompt template in Agentforce, the Specialist must ensure that the target field is available on a Dynamic Form-enabled Lightning page layout. Field Generation prompt templates work by embedding AI-generated suggestions directly into editable record fields on the Lightning record page. This functionality requires Dynamic Forms, which allow fields to be placed and managed as individual components on the Lightning page. Without Dynamic Forms enabled, the AI cannot surface suggestions directly into the field.Generation templates.

NEW QUESTION # 189
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?

A. Copilot Builder
B. Model Builder
C. App Builder

Answer: B

Explanation:
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.

NEW QUESTION # 190
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