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Salesforce AI-201 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Cloud for Agentforce	20%	- Retrievers and search types: keyword, vector, hybrid - Unstructured data processing: chunking, indexing - Improving response accuracy with data grounding - Agentforce Data Library types
Topic 2: Prompt Engineering	20%	- Prompt management and user roles - Prompt template creation: field generation, flex types - Grounding techniques and best practices - Prompt Builder usage and use cases
Topic 3: AI Agents	35%	- Deterministic controls: filters and variables - Channel integration: digital experience, email, Slack - Agent types: Service, Sales, Employee - Agent architecture and reasoning engine - Topics and actions configuration - Agent User security and permissions
Topic 4: Multi-Agent Interoperability	5%	- Agent API usage scenarios - Model Context Protocol (MCP) - Agent-to-agent communication
Topic 5: Development Lifecycle	15%	- Monitoring, adoption and optimization - Deployment: sandbox to production - Version control and maintenance - Testing agents in Testing Center

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Salesforce Certified Agentforce Specialist Sample Questions (Q225-Q230):

NEW QUESTION # 225
Support agents at Universal Containers are using Agentforce to find troubleshooting information.
They've reported that the agent frequently provides knowledge articles that are outdated, even when newer versions of the articles are available. The administrator has confirmed that all articles are correctly chunked and indexed. Which configuration change in the Data Cloud hybrid search index best addresses this problem?

A. Add a ranking factor for regency based on the LastModifiedDate field.
B. Disable the keyword index to rely solely on the vector index.
C. Switch the chunking strategy from section-aware to fixed-size.

Answer: A

Explanation:
The AgentForce Data Cloud Retrieval and Ranking Guide highlights that when outdated Knowledge articles appear before newer ones, administrators should configure ranking factors that prioritize content based on recency. The documentation specifies: "Adding a recency ranking factor using the LastModifiedDate or LastPublishedDate fields ensures the retrieval prioritizes the most up-to-date documents, improving response relevance."

NEW QUESTION # 226
Universal Containers has grounded a prompt template with a related list. During user acceptance testing (UAT), users are not getting the correct responses. What is causing this issue?

A. The related list prompt template option is not enabled.
B. The related list is not on the parent object's page layout.
C. The related list is Read Only.

Answer: B

Explanation:
UC has grounded a prompt template with a related list, but the responses are incorrect during UAT. Grounding with related lists in Agentforce allows the AI to access data from child records linked to a parent object.

NEW QUESTION # 227
Universal Containers (UC) wants to enable its sales team to use Al to suggest recommended products from its catalog. Which type of prompt template should UC use?

A. Flex prompt template
B. Email generation prompt template
C. Record summary prompt template

Answer: A

Explanation:
Universal Containers (UC) wants to enable its sales team to leverage AI to recommend products from its catalog. The best option for this use case is a Flex prompt template.
A Flex prompt template is designed to provide flexible, customizable AI-driven recommendations or responses based on specific data points, such as product information, customer needs, or sales history. This template type allows the AI to consider various inputs and parameters, making it ideal for generating product recommendations dynamically.

NEW QUESTION # 228
What considerations should an Agentforce Specialist be aware of when using Record Snapshots grounding in a prompt template?

A. Email addresses associated with the object are excluded.
B. Activities such as tasks and events are excluded.
C. Empty data, such as fields without values or sections without limits, is filtered out.

Answer: B

Explanation:
Record Snapshots grounding in Agentforce prompt templates allows the AI to access and use data from a specific Salesforce record (e.g., fields and related records) to generate contextually relevant responses. However, there are specific limitations to consider. Let's analyze each option based on official documentation.

NEW QUESTION # 229
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. Service AI Grounding
B. Service Replies
C. Work Summaries

Answer: A

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 # 230
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