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Salesforce AI-201 Exam Syllabus Topics:

Section	Weight	Objectives
Testing, Deployment & Lifecycle	15%	- Agent lifecycle management 1. Testing and validation strategies 2. Sandbox to production deployment
Governance & Observability	10%	- AI governance 1. Trust layer and compliance concepts 2. Monitoring agent behavior
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
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. 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

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Salesforce Certified Agentforce Specialist Sample Questions (Q288-Q293):

NEW QUESTION # 288
Universal Containers' service team wants to customize the standard case summary response from Agentforce. What should the Agentforce Specialist do to achieve this?

A. Summarize the Case with a standard Agent action.
B. Create a custom Record Summary prompt template for the Case object.
C. Customize the standard Record Summary template for the Case object.

Answer: B

Explanation:
UC's service team seeks to customize the standard case summary response provided by Agentforce.

NEW QUESTION # 289
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 will perform an HTTP callout to an LLM provider.
C. Agent analyzes the user's request and LLM technology is used to generate and display the appropriate response.

Answer: C

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 # 290
Universal Containers is interested in using Call Explorer to quickly gain insights from meetings recorded by its sales team. What should the Agentforce Specialist be aware of before enabling this feature?

A. Custom Call Explorer actions need to be built before it can be configured.
B. Call Explorer requires the Einstein Conversation Insights permission set to be enabled.
C. Call Explorer operates independently of Salesforce Knowledge, requiring no prior setup.

Answer: B

Explanation:
Before enabling Call Explorer, the Salesforce Agentforce Specialist must ensure that the Einstein Conversation Insights permission set is assigned to users (Option C). Call Explorer is a feature within Einstein Conversation Insights (ECI) that analyzes meeting recordings to surface trends, keywords, and actionable insights.
Key Considerations:
Permission Set Requirement:
Users (including admins) need the Einstein Conversation Insights permission set to access and use Call Explorer. Without this, the feature remains inaccessible.
The permission set grants access to ECI tools, including call transcription, analysis, and dashboard visibility.

NEW QUESTION # 291
Universal Containers wants support agents to use Agentforce to ask questions about its product tutorials and product guides. What should the Agentforce Specialist do to meet this requirement?

A. Add an Answer Questions custom field in the product object for tutorial instructions.
B. Create a prompt template for product tutorials and guides.
C. Publish product tutorials and guides as Knowledge articles.

Answer: C

Explanation:
Universal Containers (UC) wants its support agents to use Agentforce to ask questions about product tutorials and product guides. Agentforce typically references knowledge sources to provide accurate and contextual responses.
Why Knowledge Articles?
Centralized Repository: Publishing product tutorials and guides as Knowledge articles in Salesforce ensures that the information is readily available and searchable by Agentforce.
AI Integration: Salesforce's AI solutions, including Agentforce, can often be configured to pull content directly from Salesforce Knowledge articles, giving users on-demand answers without manual data duplication.
Maintenance & Updates: Storing content in Salesforce Knowledge simplifies content updates, versioning, and user permissions.

NEW QUESTION # 292
An Agentforce at Universal Containers (UC) is building with no-code tools only. They have many small accounts that are only touched periodically by a specialized sales team, and UC wants to maximize the sales operations team's time. UC wants to help prep the sales team for the calls by summarizing past purchases, interests in products shown by the Contact captured via Data Cloud, and a recap of past email and phone conversations for which there are transcripts. Which approach should the Agentforce Specialist recommend to achieve this use case?

A. Use a prompt template grounded on CRH and Data Cloud data using standard foundation model.
B. Deploy UC's own custom foundational model on this data first.
C. Fine-Tune the standard foundational model due to the complexity of the data.

Answer: A

Explanation:
For no-code implementations, Prompt Builder allows Agentforce Specialists to create prompt templates that dynamically ground responses in Salesforce CRM data (e.g., past purchases) and Data Cloud insights (e.g., product interests) without custom coding. The standard foundation model (e.g., Einstein GPT) can synthesize this data into summaries, leveraging structured and unstructured sources (e.g., email/phone transcripts). Fine-tuning (B) or custom models (C) require code and are unnecessary here, as the use case does not involve unique data patterns requiring model retraining.

NEW QUESTION # 293
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