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Salesforce AI-201 Exam Syllabus Topics:

Section	Weight	Objectives
Governance & Observability	10%	- AI governance 1. Monitoring agent behavior 2. Trust layer and compliance concepts
Prompt Engineering	20%	- Prompt Builder usage 1. Field generation and flex templates 2. Prompt templates and configuration - Grounding techniques 1. Best practices for prompt design 2. Data grounding for reducing hallucinations
Data Cloud & Grounding	20%	- Data integration for AI agents 1. Using Data Cloud for retrieval augmentation 2. Connecting external data sources
AI Agents	35%	- Agent setup and orchestration 1. Standard and custom topics 2. Standard and custom agent actions - Agent behavior configuration 1. Agent reasoning engine concepts 2. Filters and variables for deterministic behavior
Testing, Deployment & Lifecycle	15%	- Agent lifecycle management 1. Sandbox to production deployment 2. Testing and validation strategies

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Salesforce Certified Agentforce Specialist Sample Questions (Q292-Q297):

NEW QUESTION # 292
Universal Containers wants to allow its service agents to query the current fulfillment status of an order with natural language. There is an existing auto launched flow to query the information from Oracle ERP, which is the system of record for the order fulfillment process. How should An Agentforce apply the power of conversational AI to this use case?

A. Configure the Integration Flow Standard Action in Agent.
B. Create a Flex prompt template in Prompt Builder.
C. Create a custom copilot action which calls a flow.

Answer: C

Explanation:
To enable Universal Containers service agents to query the current fulfillment status of an order using natural language and leverage an existing auto-launched flow that queries Oracle ERP, the best solution is to create a custom copilot action that calls the flow. This action will allow Agent to interact with the flow and retrieve the required order fulfillment information seamlessly. Custom copilot actions can be tailored to call various backend systems or flows in response to user requests.

NEW QUESTION # 293
Universal Containers (UC) wants to ensure its compliance team can retrieve exact matches of policy clause numbers from a structured legal document library. Which search type should UC implement?

A. Use keyword search for exact term matching on structured fields like clause numbers.
B. Use semantic search to interpret synonyms of clauses dynamically.
C. Use hybrid search to blend keyword and semantic recall.

Answer: A

Explanation:
According to the AgentForce Search Optimization Guide, when the use case requires retrieving exact matches (such as policy clause numbers, legal identifiers, or invoice IDs) from structured data, the recommended approach is to use keyword search. The documentation specifies:
"Keyword search ensures deterministic retrieval of exact term matches from structured fields, preserving precision for identifiers, numeric values, and code references."

NEW QUESTION # 294
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 is Read Only.
B. The related list prompt template option is not enabled.
C. The related list is not on the parent object's page layout.

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 # 295
A data science team has trained an XGBoost classification model for product recommendations on Databricks. The Agentforce Specialist is tasked with bringing inferences for product recommendations from this model into Data Cloud as a stand-alone data model object (DMO).
How should the Agentforce Specialist set this up?

A. Create the serving endpoint in Einstein Studio, then configure the model using Model Builder.
B. Create the serving endpoint in Databricks, then configure the model using Model Builder.
C. Create the serving endpoint in Databricks, then configure the model using a Python SDK connector.

Answer: B

Explanation:
To integrate inferences from an XGBoost model into Salesforce's Data Cloud as a stand-alone Data Model Object (DMO):
Create the Serving Endpoint in Databricks:
The serving endpoint is necessary to make the trained model available for real-time inference.
Databricks provides tools to host and expose the model via an endpoint.
Configure the Model Using Model Builder:
After creating the endpoint, the Agentforce Specialist should configure it within Einstein Studio's Model Builder, which integrates external endpoints with Salesforce Data Cloud for processing and storing inferences as DMOs.

NEW QUESTION # 296
Universal Containers has PDF maintenance guides in an external folder, not yet in Salesforce.
The team wants a standard, clicks-only setup for the Service Agent to use these documents.
Which approach should the Agentforce Specialist implement?

A. Upload the PDFs as File source in the Agentforce Data Library which will build a Search Index, and create a retriever to ground responses from those documents.
B. Paste external PDF links into topic instructions and rely on the model to follow them, avoiding configuration of a retrieval source, index, or retriever action.
C. Configure Data Cloud to ingest file attachments and create custom index and retriever for product record and attachment data.

Answer: A

Explanation:
According to the AgentForce Data Library and Retrieval Configuration Guide, when organizations have external PDF or text documents that need to be used by an AI agent, the recommended clicks-only approach is to upload the documents as a File Source in the AgentForce Data Library.
The system automatically processes the uploaded files, chunks their content, builds a Search Index, and allows you to create a retriever to ground agent responses from those indexed documents.
This method requires no code or manual integration and ensures that all document content becomes queryable through retrieval-augmented generation (RAG).

NEW QUESTION # 297
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