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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. 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 setup and orchestration 1. Standard and custom agent actions 2. Standard and custom topics - Agent behavior configuration 1. Filters and variables for deterministic behavior 2. Agent reasoning engine concepts
Data Cloud & Grounding	20%	- Data integration for AI agents 1. Using Data Cloud for retrieval augmentation 2. Connecting external data sources
Governance & Observability	10%	- AI governance 1. Trust layer and compliance concepts 2. Monitoring agent behavior

>> Current Salesforce AI-201 Exam Content <<

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The study material to get Salesforce Salesforce Certified Agentforce Specialist certified should be according to individual's learning style and experience. Real Salesforce AI-201 Exam Questions certification makes you more dedicated and professional as it will provide you complete information required to work within a professional working environment.

Salesforce Certified Agentforce Specialist Sample Questions (Q32-Q37):

NEW QUESTION # 32
Universal Containers (UC) uses a file upload-based data library and custom prompt to support AI- driven training content. However, users report that the AI frequently returns outdated documents.
Which corrective action should UC implement to improve content relevancy?

A. Continue using the default retriever without filters, because periodic re-uploads will eventually phase out outdated documents without further configuration or the need for custom retrievers.
B. Switch the data library source from file uploads to a Knowledge-based data library, because Salesforce Knowledge bases automatically manage document recency, ensuring current documents are returned.
C. Configure a custom retriever that includes a filter condition limiting retrieval to documents updated within a defined recent period, ensuring that only current content is used for AI responses.

Answer: C

Explanation:
UC's issue is that their file upload-based Data Library (where PDFs or documents are uploaded and indexed into Data Cloud's vector database) is returning outdated training content in AI responses. To improve relevancy by ensuring only current documents are retrieved, the most effective solution is to configure a custom retriever with a filter (Option B). In Agentforce, a custom retriever allows UC to define specific conditions-such as a filter on a "Last Modified Date" or similar timestamp field-to limit retrieval to documents updated within a recent period (e.g., last
6 months). This ensures the AI grounds its responses in the most current content, directly addressing the problem of outdated documents without requiring a complete overhaul of the data source.

NEW QUESTION # 33
Which object stores the conversation transcript between the customer and the agent?

A. Messaging Session
B. Case
C. Messaging End User

Answer: A

Explanation:
Why is "Messaging Session" the correct answer?
In Agentforce, the Messaging Session object stores the conversation transcript between the customer and the agent.
Key Features of the Messaging Session Object:
Stores the Entire Customer-Agent Conversation
The Messaging Session object maintains a record of the full chat history, including timestamps, messages, and interactions.
This ensures that past interactions can be referenced during follow-ups.
Supports AI-Powered Work Summaries
Einstein AI uses Messaging Sessions to generate summaries of chat interactions for agents.
These summaries are stored and accessible for later reference.
Links with Service Cloud for Case Resolution
If a conversation escalates into a case, the Messaging Session object can be linked to it.
This allows support teams to review the conversation history without switching contexts.

NEW QUESTION # 34
Universal Containers deployed the new Agentforce Sales Development Representative (SDR) Into production, but sales reps are saying they can't find it. What is causing this issue?

A. Sales rep users do not have access to the SDR Agent object.
B. Sales rep users are missing the Use SDR Agent permission set.
C. Sales rep users profiles are missing the Allow SDR Agent permission.

Answer: B

Explanation:
Why is "Sales rep users are missing the Use SDR Agent permission set" the correct answer?
If sales reps are unable to find the Agentforce Sales Development Representative (SDR) Agent, the most likely cause is missing permissions. The "Use SDR Agent" permission set is required for users to access and interact with the SDR Agent in Agentforce.
Key Considerations for This Issue:
Permission Set Restriction
Users must have the "Use SDR Agent" permission set to access Agentforce SDR in their Salesforce environment.
If they lack this permission, the SDR Agent will not appear in their interface.
Agentforce Role-Based Access Control
Agentforce assigns specific permissions based on user roles.
Sales reps require explicit permission to access the SDR Agent.
Fixing the Issue
The Salesforce Admin should assign the "Use SDR Agent" permission set to all relevant sales reps.
This is done in Setup Permission Sets Assign to Users.

NEW QUESTION # 35
Universal Containers (UC) is creating a new custom prompt template to populate a field with generated output. UC enabled the Einstein Trust Layer to ensure AI Audit data is captured and monitored for adoption and possible enhancements. Which prompt template type should UC use and which consideration should UC review?

A. Flex, and that Dynamic Fields is enabled
B. Field Generation, and that Dynamic Fields is enabled
C. Field Generation, and that Dynamic Forms is enabled

Answer: B

Explanation:
Salesforce Agentforce provides various prompt template types to support AI-driven tasks, such as generating text or populating fields. In this case, UC needs a custom prompt template to populate a field with generated output, which directly aligns with the Field Generation prompt template type. This type is designed to use generative AI to create field values (e.g., summaries, descriptions) based on input data or prompts, making it the ideal choice for UC's requirement.
Additionally, UC has enabled the Einstein Trust Layer, a governance framework that ensures AI outputs are safe, explainable, and auditable, capturing AI Audit data for monitoring adoption and identifying improvement areas.
The consideration UC should review is whether Dynamic Fields is enabled. Dynamic Fields allow the prompt template to incorporate variable data from Salesforce records (e.g., case details, customer info) into the prompt, ensuring the generated output is contextually relevant to each record. This is critical for field population tasks, as static prompts wouldn't adapt to record-specific needs. The Einstein Trust Layer further benefits from this, as it can track how dynamic inputs influence outputs for audit purposes.

NEW QUESTION # 36
A sales manager needs to contact leads at scale with hyper-relevant solutions and customized communications in the most efficient manner possible. Which Salesforce solution best suits this need?

A. Einstein Lead follow-up
B. Prompt Builder
C. Einstein Sales Assistant

Answer: B

Explanation:
The question specifies a sales manager's need to:
Contact leads at scale: Handle a large volume of leads simultaneously.
Hyper-relevant solutions: Deliver tailored solutions based on lead-specific data (e.g., CRM data, behavior).
Customized communications: Personalize outreach (e.g., emails, messages) for each lead.
Most efficient manner possible: Minimize manual effort and maximize automation.
This suggests a solution that leverages AI for personalization and automation for scale, ideally within the Salesforce ecosystem.

NEW QUESTION # 37
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