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Salesforce AI-201 Exam Syllabus Topics:

Section	Weight	Objectives
Testing, Deployment & Lifecycle	15%	- Agent lifecycle management 1. Sandbox to production deployment 2. Testing and validation strategies
Prompt Engineering	20%	- Grounding techniques 1. Data grounding for reducing hallucinations 2. Best practices for prompt design - Prompt Builder usage 1. Field generation and flex templates 2. Prompt templates and configuration
Data Cloud & Grounding	20%	- Data integration for AI agents 1. Connecting external data sources 2. Using Data Cloud for retrieval augmentation
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 agent actions 2. Standard and custom topics
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 (Q266-Q271):

NEW QUESTION # 266
Coral Cloud Resorts is uploading thousands of new HTML knowledge articles files for a resort launch. To ensure Agentforce retrieves accurate responses quickly, which chunking strategy should be used when creating a new index?

A. Semantic-based passage extraction
B. Conversation-based chunking
C. Section-aware chunking

Answer: C

Explanation:
In AgentForce documentation on Knowledge Indexing and Chunking Strategies, Salesforce emphasizes that when uploading large volumes of structured content such as HTML or documentation files, the system should use section-aware chunking. The guide states: "Section- aware chunking preserves the logical boundaries of headings, paragraphs, and sub-sections in structured documents like HTML or PDF files, allowing the agent to retrieve contextually accurate and relevant responses quickly." This method ensures that the agent does not split content mid-section or lose contextual relationships between headings and body text. It enhances both retrieval speed and answer precision.

NEW QUESTION # 267
A support team handles a high volume of chat interactions and needs a solution to provide quick, relevant responses to customer inquiries. Responses must be grounded in the organization's knowledge base to maintain consistency and accuracy. Which feature in Einstein for Service should the support team use?

A. Einstein Service Replies
B. Einstein Reply Recommendations
C. Einstein Knowledge Recommendations

Answer: A

Explanation:
Einstein Service Replies: This feature is designed to generate contextual, AI-powered response suggestions for agents during chat and email interactions. Crucially, these replies can be grounded in your organization's knowledge base, ensuring consistency and accuracy. This aligns perfectly with the requirement for quick, relevant, and consistent responses.

NEW QUESTION # 268
Universal Containers wants to leverage the Record Snapshots grounding feature in a prompt template. What preparations are required?

A. Configure page layout of the master record type.
B. Enable and configure dynamic form for the object.
C. Create a field set for all the fields to be grounded.

Answer: A

Explanation:
Record Snapshots in Salesforce Prompt Builder leverage the data visible on the user's page layout for an object to ground the prompt. This means that the fields and related lists that are configured on the page layout directly influence the data included in the snapshot.

NEW QUESTION # 269
A Salesforce Administrator wants to generate personalized, targeted emails that incorporate customer interaction data. The admin wants to leverage large language models (LLMs) to write the emails, and wants to reuse templates for different products and customers. Which solution approach should the admin leverage?

A. Create a t field Generation prompt template type
B. Use sales Email standard templates
C. Create a Sales Email prompt template type.

Answer: C

Explanation:
To generate personalized emails using LLMs while reusing templates:
Sales Email Prompt Template Type (Option C): Designed specifically for generating dynamic email content by combining LLMs with structured templates. It allows admins to define placeholders (e.g., customer name, product details) and reuse templates across scenarios.

NEW QUESTION # 270
A company wants to retrieve patient history details to augment the Al agent response. [he company wants to leverage the Data Cloud search index feature. What is best practice when considering retrieval-augrmented generation (RAG) for information that may contain personally identifiable information (PII)?

A. Depend on the agent's prompt to avoid exposing PII.
B. Encrypt embeddings, but still index PII records.
C. Mask sensitive fields and index only non-PII data.

Answer: C

Explanation:
According to the AgentForce Data Governance and RAG Security Guidelines, when implementing retrieval-augmented generation (RAG) using Data Cloud search indexes, best practice is to ensure that personally identifiable information (PII) and other sensitive data are never indexed or embedded in the retrieval system.
The documented recommendation is to mask or exclude sensitive fields before creating embeddings or indexing content. This prevents the large language model (LLM) from accessing or generating responses that could inadvertently expose confidential information. Masking can include redacting names, IDs, contact details, or any regulated medical information.

NEW QUESTION # 271
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