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Salesforce AI-201 Exam Syllabus Topics:

SectionWeightObjectives
Prompt Engineering30%- Agentforce AI Setup & Configuration
  • 1. Advanced implementation strategies
- Prompt Builder & Template Creation
  • 1. Best practices
Agent Force Concepts30%- Data Cloud & Grounding Techniques
  • 1. Real-world applications
- Einstein GPT Integration
  • 1. Advanced implementation strategies
Einstein Trust Layer & Security- Data Masking
  • 1. Configuration of privacy data entities
- Trust Layer & Security Features
  • 1. Enterprise-grade deployment
Reasoning Engine & AI Actions- Workflow mastery
  • 1. Custom Agent actions (Flows)
  • 2. Standard Agent actions
  • 3. Custom Agent actions (Apex)

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Salesforce Certified Agentforce Specialist Sample Questions (Q246-Q251):

NEW QUESTION # 246
An Agentforce wants to use the related lists from an account in a custom prompt template. What should the Agentforce Specialist consider when configuring the prompt template?

Answer: C

Explanation:
When configuring a custom prompt template to use related lists, the Agentforce Specialist must be aware of the maximum number of related list merge fields that can be included. Salesforce enforces limits to ensure prompt templates perform efficiently and do not overload the system with too much data. As a best practice, it's important to monitor and optimize the number of merge fields used.


NEW QUESTION # 247
A data scientist needs to view and manage models in Einstein Studio, and also needs to create prompt templates in Prompt Builder. Which permission sets should an Agentforce Specialist assign to the data scientist?

Answer: B

Explanation:
The data scientist requires permissions for Einstein Studio (model management) and Prompt Builder (template creation). Note: "Einstein Studio" may be a misnomer for Data Cloud's model management or a related tool, but we'll interpret based on context.


NEW QUESTION # 248
In the context of retriever and search indexes, what best describes the data preparation process in Data Cloud?

Answer: B

Explanation:
Why is "Loading, Chunking, Vectorizing, and Storing" the correct answer?
Agentforce AI-powered search and retriever indexing requires data to be structured and optimized for retrieval. The Data Cloud preparation process involves:
Key Steps in the Data Preparation Process for Agentforce:
Loading Data
Raw text from documents, emails, chat transcripts, and Knowledge articles is loaded into Data Cloud.
Chunking (Breaking Text into Small Parts)
AI divides long-form text into retrievable chunks to improve response accuracy.
Example: A 1000-word article might be split into multiple indexed paragraphs.
Vectorization (Transforming Text for AI Retrieval)
Each text chunk is converted into numerical vector embeddings.
This enables faster AI-powered searches based on semantic meaning, not just keywords.
Storing in a Vector Database
The processed data is stored in a search-optimized vector format.
Agentforce AI retrievers use this data to find relevant responses quickly.


NEW QUESTION # 249
Universal Containers (UC) has configured an Agentforce Data Library using Knowledge articles.
When testing in Agent Builder and the Experience Cloud site, the agent is not responding with grounded Knowledge article information. However, when tested in Prompt Builder, the response returns correctly. What should UC do to troubleshoot the issue?

Answer: A

Explanation:
UC has set up an Agentforce Data Library with Knowledge articles, and while Prompt Builder retrieves the data correctly, the agent fails to do so in Agent Builder and Experience Cloud.


NEW QUESTION # 250
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?

Answer: C

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 # 251
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