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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
Prompt Engineering	20%	- Prompt Builder usage 1. Field generation and flex templates 2. Prompt templates and configuration - Grounding techniques 1. Data grounding for reducing hallucinations 2. Best practices for prompt design
Testing, Deployment & Lifecycle	15%	- Agent lifecycle management 1. Testing and validation strategies 2. Sandbox to production deployment
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
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 (Q79-Q84):

NEW QUESTION # 79
A Salesforce Administrator is exploring the capabilities of Agent to enhance user interaction within their organization. They are particularly interested in how Agent processes user requests and the mechanism it employs to deliver responses. The administrator is evaluating whether Agent directly interfaces with a large language model (LLM) to fetch and display responses to user inquiries, facilitating a broad range of requests from users. How does Agent handle user requests In Salesforce?

A. Agent will trigger a flow that utilizes a prompt template to generate the message.
B. Agent analyzes the user's request and LLM technology is used to generate and display the appropriate response.
C. Agent will perform an HTTP callout to an LLM provider.

Answer: B

Explanation:
Agent is designed to enhance user interaction within Salesforce by leveraging Large Language Models (LLMs) to process and respond to user inquiries. When a user submits a request, Agent analyzes the input using natural language processing techniques. It then utilizes LLM technology to generate an appropriate and contextually relevant response, which is displayed directly to the user within the Salesforce interface.

NEW QUESTION # 80
Universal Containers (UC) currently tracks Leads with a custom object. UC is preparing to implement the Sales Development Representative (SDR) Agent. Which consideration should UC keep in mind?

A. Agentforce SDR only works with the standard Lead object.
B. Agentforce SDR only works on Opportunities.
C. Agentforce SDR only supports custom objects associated with Accounts.

Answer: A

Explanation:
Universal Containers (UC) uses a custom object for Leads and plans to implement the Agentforce Sales Development Representative (SDR) Agent. The SDR Agent is a prebuilt, configurable AI agent designed to assist sales teams by qualifying leads and scheduling meetings. Let's evaluate the options based on its functionality and limitations.

NEW QUESTION # 81
An agent must comply with regional data regulations (e.g., GDPR). Which Salesforce capability MOST directly supports this?

A. Reports and Dashboards
B. Lightning App Builder
C. Custom metadata types
D. Einstein Trust Layer governance controls

Answer: D

Explanation:
The Einstein Trust Layer enforces data residency, masking, and compliance policies.

NEW QUESTION # 82
When a verified customer in a help center says, "I want to upgrade my service plan," an AI agent needs to complete the following tasks:
- Verify identity and entitlement.
- Create a new quote.
- Calculate a prorated upgrade amount.
- Escalate to an Account Executive (AE) only if the reorder exceeds USD
25,000.
Which type of agent should an AgentForce Specialist build to support this use case?

A. Service Agent to resolve the case end-to-end and create a new opportunity for the sales team
B. Sales Agent to handle the upsell and large-deal escalation
C. Employee Agent to orchestrate internal logistics and finance

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract of AgentForce Documents:
According to the AgentForce Specialist Implementation Guide and Agent Type Configuration Reference, this scenario represents a revenue-generating interaction where the AI agent is directly handling an upsell process. The tasks include verifying the customer's entitlement, generating a new quote, and calculating a prorated amount -- all of which align with the Sales Agent configuration type in AgentForce.
The Sales Agent is specifically designed to manage lead conversion, quoting, upselling, renewals, and escalation logic for higher-value opportunities. AgentForce documentation emphasizes that when an interaction involves quote generation, pricing calculations, or escalations to an Account Executive for large deal handling, the correct design is a Sales Agent.

NEW QUESTION # 83
Coral Cloud Resorts (CCR) sees the agent forgot the dietary/activity preferences gathered earlier.
They need those preferences to persist throughout the session. What should CCR implement?

A. Rely on natural conversation memory and instruct the agent to look back.
B. Configure custom variables to capture/store customer preferences from action outputs.
C. Create a context variable to capture/store customer preferences as action outputs.

Answer: C

Explanation:
According to the AgentForce Session Memory and Context Management Guide, when specific customer preferences (such as dietary or activity selections) must persist throughout an interaction, the correct approach is to use a context variable. The documentation states: "Context variables retain information across the user session, enabling the agent to reference prior inputs or outputs without re-asking. They are ideal for persisting customer preferences, authentication data, or ongoing session parameters."

NEW QUESTION # 84
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