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Salesforce AI-201 Exam Syllabus Topics:

SectionWeightObjectives
Data Cloud for Agentforce20%- Agentforce Data Library types
- Improving response accuracy with data grounding
- Unstructured data processing: chunking, indexing
- Retrievers and search types: keyword, vector, hybrid
Multi-Agent Interoperability5%- Agent-to-agent communication
- Agent API usage scenarios
- Model Context Protocol (MCP)
Prompt Engineering20%- Prompt Builder usage and use cases
- Prompt template creation: field generation, flex types
- Grounding techniques and best practices
- Prompt management and user roles
Development Lifecycle15%- Version control and maintenance
- Testing agents in Testing Center
- Deployment: sandbox to production
- Monitoring, adoption and optimization
AI Agents35%- Agent architecture and reasoning engine
- Agent types: Service, Sales, Employee
- Agent User security and permissions
- Topics and actions configuration
- Channel integration: digital experience, email, Slack
- Deterministic controls: filters and variables

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Salesforce Certified Agentforce Specialist Sample Questions (Q297-Q302):

NEW QUESTION # 297
Coral Cloud Resorts (CCR) sees the agent forgot the dietary/activity preferences gathered earlier.
They need those preferences to persist throughout the session. What should CCR implement?

Answer: C

Explanation:
According to the AgentForce Session Memory and Context Management Guide, when specific customer preferences (such as dietary or activity selections) must persist throughout an interaction, the correct approach is to use a context variable. The documentation states: "Context variables retain information across the user session, enabling the agent to reference prior inputs or outputs without re-asking. They are ideal for persisting customer preferences, authentication data, or ongoing session parameters."


NEW QUESTION # 298
Universal Containers (UC) wants to enable its sales team to use AI to suggest recommended products from its catalog. Which type of prompt template should UC use?

Answer: C

Explanation:
UC needs an AI solution to suggest products from a catalog for its sales team.


NEW QUESTION # 299
How does Agentforce select the correct action to resolve a user's request?

Answer: A

Explanation:
In the AgentForce Architecture and Reasoning Engine Overview, Salesforce explains that the large language model (LLM) drives topic and action selection. The documentation states:
"AgentForce uses an LLM to interpret user intent, map it to existing topics, and trigger the appropriate action when available. If no matching topic or action is found, the LLM attempts to generate a direct response using its available context."


NEW QUESTION # 300
Which business requirement presents a good use case for leveraging Einstein Prompt Builder?

Answer: C

Explanation:
Einstein Prompt Builder is a Salesforce feature that helps generate text (summaries, email content, responses) using AI models.
The question presents three potential use cases, asking which one best fits the capabilities of Einstein Prompt Builder.
Einstein Prompt Builder Typical Use Cases
Text Generation & Summaries: Great for writing or summarizing content, like responding to an email or generating text for a record field.


NEW QUESTION # 301
A company wants an Agentforce agent to update Case records and execute Flows on behalf of support agents. Which security model ensures the agent does NOT exceed the user's data access?

Answer: B

Explanation:
Running the agent in user context ensures it respects the user's permissions, sharing rules, and field-level security.


NEW QUESTION # 302
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