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Salesforce AI-201 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Prompt Engineering	20%	- Grounding techniques 1. Best practices for prompt design 2. Data grounding for reducing hallucinations - Prompt Builder usage 1. Field generation and flex templates 2. Prompt templates and configuration
Topic 2: Testing, Deployment & Lifecycle	15%	- Agent lifecycle management 1. Sandbox to production deployment 2. Testing and validation strategies
Topic 3: AI Agents	35%	- Agent setup and orchestration 1. Standard and custom topics 2. Standard and custom agent actions - Agent behavior configuration 1. Filters and variables for deterministic behavior 2. Agent reasoning engine concepts
Topic 4: Governance & Observability	10%	- AI governance 1. Trust layer and compliance concepts 2. Monitoring agent behavior
Topic 5: Data Cloud & Grounding	20%	- Data integration for AI agents 1. Using Data Cloud for retrieval augmentation 2. Connecting external data sources

>> AI-201 Exam Lab Questions <<

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Salesforce Certified Agentforce Specialist Sample Questions (Q21-Q26):

NEW QUESTION # 21
An Agentforce Specialist is creating a custom action in Agentforce. Which option is available for the Agentforce Specialist to choose for the custom Agent action?

A. Flows
B. Apex Trigger
C. SOQL

Answer: A

Explanation:
The Agentforce Specialist is defining a custom action for an Agentforce agent in Agent Builder.
Actions determine what the agent does (e.g., retrieve data, update records).

NEW QUESTION # 22
Universal Containers wants to be able to detect with a high level confidence if content generated by a large language model (LLM) contains toxic language. Which action should an Al Specialist take in the Trust Layer to confirm toxicity is being appropriately managed?

A. Create a flow that sends an email to a specified address each time the toxicity score from the response exceeds a predefined threshold.
B. Access the Toxicity Detection log in Setup and export all entries where isToxicityDetected is true.
C. Create a Trust Layer audit report within Data Cloud that uses a toxicity detector type filter to display toxic responses and their respective scores.

Answer: C

Explanation:
To ensure that content generated by a large language model (LLM) is appropriately screened for toxic language, the Agentforce Specialist should create a Trust Layer audit report within Data Cloud. By using the toxicity detector type filter, the report can display toxic responses along with their respective toxicity scores, allowing Universal Containers to monitor and manage any toxic content generated with a high level of confidence.

NEW QUESTION # 23
Universal Containers has a strict change management process that requires all possible configuration to be completed in a sandbox which will be deployed to production. The Agentforce Specialist is tasked with setting up Work Summaries for Enhanced Messaging. Einstein Generative AI is already enabled in production, and the Einstein Work Summaries permission set is already available in production. Which other configuration steps should the Agentforce Specialist take in the sandbox that can be deployed to the production org?

A. From the Epstein setup menu, select Turn on Einstein: create custom fields to store Issue, Resolution, and Summary: create a Quick Action that updates these fields: and add the wrap up componert to the Messaging session record page layout.
B. Create custom fields to store issue, Resolution, and Summary; create a Quick Action that updates these fields: and ado the Wrap up component to the Messaging session record page lavcut.
C. Create custom fields to store issue, Resolution, and Summary; create a Quick Action that updates these fields: add the Wrap Up component to the Messaging Session record paae layout: and create Permission Set Assignments for the intended Agents.

Answer: B

Explanation:
Universal Containers (UC) has a strict change management process that requires all possible configuration be completed in a sandbox and deployed to Production.
Einstein Generative AI is already enabled in Production, and the "Einstein Work Summaries" permission set is already available in Production.
The Agentforce Specialist needs to configure Work Summaries for Enhanced Messaging in the sandbox.
What Can Actually Be Deployed from Sandbox to Production?
Custom Fields: Metadata that is easily created in sandbox and then deployed.
Quick Actions: Also metadata-based and can be deployed from sandbox to production.
Layout Components: Page layout changes (such as adding the Wrap Up component) can be added to a change set or deployment package.

NEW QUESTION # 24
Universal Containers implements Custom Agent Actions to enhance its customer service operations. The development team needs to understand the core components of a Custom Agent Action to ensure proper configuration and functionality. What should the development team review in the Custom Agent Action configuration to identify one of the core components of a Custom Agent Action?

A. Instructions
B. Output Types
C. Action Triggers

Answer: A

Explanation:
UC's development team needs to identify a core component of a Custom Agent Action in Agent Builder.

NEW QUESTION # 25
A sales manager is using Agent Assistant to streamline their daily tasks. They ask the agent to Show me a list of my open opportunities. How does the large language model (LLM) in Agentforce identify and execute the action to show the sales manager a list of open opportunities?

A. Using a dialog pattern. the LLM matches the user query to the available topic, action and steps then performs the steps for each action, such as retrieving a fast of open opportunities.
B. The LLM uses a static set of rules to match the user's request with predefined topics and actions, bypassing the need for dynamic interpretation and planning.
C. The LLM interprets the user's request, generates a plan by identifying the apcMopnete topics and actions, and executes the actions to retrieve and display the open opportunities

Answer: C

Explanation:
Agentforce's LLM dynamically interprets natural language requests (e.g., "Show me open opportunities"), generates an execution plan using the planner service, and retrieves data via actions (e.g., querying Salesforce records). This contrasts with static rules (B) or rigid dialog patterns (C), which lack contextual adaptability. Salesforce documentation highlights the planner's role in converting intents into actionable steps while adhering to security and business logic.

NEW QUESTION # 26
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