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Salesforce AI-201 Exam Syllabus Topics:

Section	Weight	Objectives
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
Testing, Deployment & Lifecycle	15%	- Agent lifecycle management 1. Testing and validation strategies 2. Sandbox to production deployment
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
Governance & Observability	10%	- AI governance 1. Trust layer and compliance concepts 2. Monitoring agent behavior
Data Cloud & Grounding	20%	- Data integration for AI agents 1. Using Data Cloud for retrieval augmentation 2. Connecting external data sources

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Salesforce Certified Agentforce Specialist Sample Questions (Q149-Q154):

NEW QUESTION # 149
Universal Containers (UC) implements a custom retriever to improve the accuracy of AI- generated responses. UC notices that the retriever is returning too many irrelevant results, making the responses less useful. What should UC do to ensure only relevant data is retrieved?

A. Change the search index to a different data model object (DMO).
B. Increase the maximum number of results returned to capture a broader dataset.
C. Define filters to narrow the search results based on specific conditions.

Answer: C

Explanation:
In Salesforce Agentforce, a custom retriever is used to fetch relevant data (e.g., from Data Cloud's vector database or Salesforce records) to ground AI responses. UC's issue is that their retriever returns too many irrelevant results, reducing response accuracy. The best solution is to define filters (Option A) to refine the retriever's search criteria. Filters allow UC to specify conditions (e.g., "only retrieve documents from the `Policy' category" or "records created after a certain date") that narrow the dataset, ensuring the retriever returns only relevant results. This directly improves the precision of AI-generated responses by excluding extraneous data, addressing UC's problem effectively.

NEW QUESTION # 150
The marketing team at Universal Containers is looking for a way personalize emails based on customer behavior, preferences, and purchase history. Why should the team use Agent as the solution?

A. To send automated emails to all customers
B. To analyze past campaign performance
C. To generate relevant content when engaging with each customer

Answer: C

Explanation:
Agent is designed to assist in generating personalized, AI-driven content based on customer data such as behavior, preferences, and purchase history. For the marketing team at Universal Containers, this is the perfect solution to create dynamic and relevant email content. By leveraging Agent, they can ensure that each customer receives tailored communications, improving engagement and conversion rates.

NEW QUESTION # 151
A Service Agent at Universal Containers (UC) is designed to help customers resolve issues by searching against knowledge articles. Knowledge articles have PDF attachments that add critical details. UC reports that the agent provides excellent summaries of the knowledge articles, but seems completely unaware of the PDF attachments. How should an Agentforce Specialist configure the Data Cloud search index to include the content of these attached files?

A. Use Data Cloud's `Include Attachments' option and select the ContentDocumentVersion unstructured data model object (UDMO).
B. Enable 'Include Related Attachments' for Knowledge-kav and map the ContentDocumentLink unstructured data model object (UDMO).
C. Increase article chunk size and token limits for Knowledge indexing so larger contexts capture attachment references.

Answer: A

Explanation:
The AgentForce Data Cloud Indexing Guide clearly states that to include content from attached files such as PDFs in Knowledge articles, the correct configuration is to enable "Include Attachments" and map the ContentDocumentVersion unstructured data model object (UDMO).
The documentation specifies: "When indexing Knowledge or Case data, enabling the `Include Attachments' option allows the Data Cloud index to extract and embed content from linked ContentDocumentVersion records, ensuring the agent retrieves relevant information from attachments."

NEW QUESTION # 152
Universal Containers (UC) needs to save agents time with AI-generated case summaries. UC has implemented the Work Summary feature. What does Einstein consider when generating a summary?

A. Generation is grounded with existing conversation context only.
B. Generation is grounded with conversation context and Knowledge articles.
C. Generation is grounded with conversation context, Knowledge articles, and cases.

Answer: C

Explanation:
When generating a Work Summary, Einstein leverages multiple sources of information to provide a comprehensive and accurate case summary for agents.
Conversation Context:
Einstein analyzes the details of the customer interaction, including chat or email threads, to extract relevant information for the summary.
Knowledge Articles:
It considers linked Knowledge Articles or articles referred to during the case resolution process, ensuring the summary incorporates accurate resolutions or additional resources provided to the customer.
Cases:
Einstein also examines historical cases and related case records to ground the summary in context from past resolutions or interactions.

NEW QUESTION # 153
Universal Containers Is Interested In Improving the sales operation efficiency by analyzing their data using Al-powered predictions in Einstein Studio. Which use case works for this scenario?

A. Predict customer sentiment toward a promotion message.
B. Predict customer lifetime value of an account.
C. Predict most popular products from new product catalog.

Answer: B

Explanation:
For improving sales operations efficiency, Einstein Studio is ideal for creating AI-powered models that can predict outcomes based on data. One of the most valuable use cases is predicting customer lifetime value, which helps sales teams focus on high-value accounts and make more informed decisions. Customer lifetime value (CLV) predictions can optimize strategies around customer retention, cross-selling, and long-term engagement.

NEW QUESTION # 154
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