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Salesforce Certified
Agentforce Specialist
(AI-201)
Version: Demo
[ Total Questions: 10]
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Agentforce-Specialist
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Salesforce - Agentforce-SpecialistPass Exam
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A. 
B. 
C. 
Question #:1
Once a data source is chosen for an Agentforce Data Library, what is true about changing that data source 
later?
The data source can be changed through the Data Cloud settings.
The Data Retriever can be reconfigured to use a different data source.
The data source cannot be changed after it is selected.  
Answer: C
Explanation
Why is "The data source cannot be changed after it is selected" the correct answer?
When configuring an , the . Once a data source Agentforce Data Library data source selection is permanent
is set, it cannot be modified or replaced. This design ensures data consistency, security, and reliability
within Salesforce's AI-driven environment.
Key Considerations in Agentforce Data Library
Data Source Lock-In
The chosen data source remains fixed to maintain and avoid inconsistencies.data integrity
Any updates or modifications require instead of modifying the creating a new Data Library
existing one.
Why Can't the Data Source Be Changed?
The data source defines , and any modification would the foundation of AI-driven workflows
disrupt processing logic.
Agentforce tools rely on structured datasets to , and enable AI-powered recommendations
changing data sources could lead to inconsistencies in grounding techniques.
Workarounds for Changing Data Sources
If an organization needs to use a different data source, must be a new Agentforce Data Library
created and configured from scratch.
Old data can be into the new data source for continuity.manually migrated
Why Not the Other Options?
# A. The data source can be changed through the Data Cloud settings.
Salesforce - Agentforce-SpecialistPass Exam
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A. 
B. 
C. 
Incorrect because once the data source is linked to an Agentforce Data Library, it cannot be altered, 
even via Data Cloud settings.
# B. The Data Retriever can be reconfigured to use a different data source.
Incorrect as the Data Retriever works within the constraints of the selected data source and does not 
provide an option to swap data sources post-selection.
Agentforce Specialist References
The and confirm that Salesforce AI Specialist Material Salesforce Instructions for the Certification once 
.a data source is set for an Agentforce Data Library, it cannot be changed
Question #:2
Universal Containers is interested in using Call Explorer to quickly gain insights from meetings recorded by 
its sales team.
What should the Agentforce Specialist be aware of before enabling this feature?
Call Explorer operates independently of Salesforce Knowledge, requiring no prior setup.
Custom Call Explorer actions need to be built before it can be configured.
Call Explorer requires the Einstein Conversation Insights permission set to be enabled.
Answer: C
Explanation
Before enabling  , the Salesforce Agentforce Specialist must ensure that the Call Explorer Einstein 
 is assigned to users (Option C). Call Explorer is a feature within Conversation Insights permission set
Einstein Conversation Insights (ECI) that analyzes meeting recordings to surface trends, keywords, and 
actionable insights.
Key Considerations:
Permission Set Requirement:
Users (including admins) need the   permission set to access and Einstein Conversation Insights
use Call Explorer. Without this, the feature remains inaccessible.
The permission set grants access to ECI tools, including call transcription, analysis, and 
dashboard visibility.
Why Other Options Are Incorrect:
Salesforce - Agentforce-SpecialistPass Exam
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A. 
B. 
C. 
A. Independence from Salesforce Knowledge: While Call Explorer does not rely on Salesforce 
Knowledge, this is irrelevant to the setup prerequisite. The critical dependency is the permission 
set, not Knowledge configuration.
B. Custom Actions: Call Explorer does not require custom actions to be built before 
configuration. It is a pre-built analytics tool that works once permissions and data sources (e.g., 
call recordings) are configured.
:
Salesforce Einstein Conversation Insights Guide: Explicitly states that the Einstein Conversation Insights 
permission set is required to access Call Explorer.
Trailhead Module: "Einstein Conversation Insights Basics" outlines permission prerequisites for enabling 
call analytics.
Salesforce Help Documentation: Confirms that Call Explorer functionality is governed by ECI permissions.
Question #:3
An Agentforce is setting up a new org and needs to ensure that users can create and execute prompt templates. 
The Agentforce Specialist is unsure which roles are necessary for these tasks.
Which permission sets should the Agentforce Specialist assign to users who need to create and execute 
prompt templates?
Prompt Template Manager for creating templates and Data Cloud Admin for executing templates
Prompt Template Manager for creating templates and Prompt Template User for executing templates
Data Cloud Admin for creating templates and Prompt Template User for executing templates
Answer: B
Explanation
To effectively manage and use prompt templates, two distinct permission sets are required:
Prompt Template Manager: This permission set allows users to create prompt templates. It provides 
the necessary access to define templates, which can be shared and utilized across the organization.
Prompt Template User: This permission set is designed for users who need to execute the templates. It 
provides the ability to interact with pre-designed prompts and generate outcomes based on these 
templates.
The permission set is not directly relevant to creating or executing prompt templates but Data Cloud Admin
is more focused on managing the Data Cloud.
Reference:
Salesforce - Agentforce-SpecialistPass Exam
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A. 
B. 
C. 
"Permissions and Access for Prompt Templates | Salesforce Trailhead" .
Question #:4
What is the primary function of the reasoning engine in Agentforce?
Identifying agent topics and actions to respond to user utterances
Offering real-time natural language response during conversations
Generating record queries based on conversation history  
Answer: A
Explanation
Why is "Identifying agent topics and actions to respond to user utterances" the correct answer?
In , the plays a critical role in and Agentforce reasoning engine interpreting user queries determining the 
.appropriate agent response
Key Functions of the Reasoning Engine in Agentforce:
Analyzing User Intent
The reasoning engine the meaning behind .interprets natural language user inputs
It maps user utterances to to determine the correct AI-generated response.predefined topics
Selecting the Appropriate Agent Action
The engine and selects the best response evaluates available actions based on the detected topic
.
For example, if a user asks, , the reasoning engine:"What is my current account balance?"
Identifies the topic:"Account Information"
Chooses the correct action: "Retrieve account balance"
Executes the action and returns the response
Ensuring AI Accuracy and Context Awareness
The reasoning engine in relevant , ensuring grounds AI-generated responses Salesforce data
.accurate outputs
Why Not the Other Options?
Salesforce - Agentforce-SpecialistPass Exam
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A. 
B. 
C. 
# B. Offering real-time natural language response during conversations.
Incorrect because real-time (NLP) is handled by the natural language processing large language 
, not the .model (LLM) reasoning engine
The , not .reasoning engine focuses on action selection linguistic processing
# C. Generating record queries based on conversation history.
Incorrect because is handled by , not the query generation Copilot Actions (e.g., Query Records)
.reasoning engine
The reasoning engine which query should be run, but does not generate queries itself.decides
Agentforce Specialist References
Salesforce AI Specialist Material explains that the reasoning engine identifies topics and selects 
.agent actions
Salesforce Instructions for the Certification confirm that the reasoning engine determines AI 
.workflow execution
Question #:5
An Agentforce is creating a custom action in Agent.
Which option is available for the Agentforce Specialist to choose for the custom copilot action?
Apex trigger
SOQL
Flows
Answer: C
Explanation
When creating a in Agent, one of the available options is to use . Flows are a powerful custom action Flows
automation tool in Salesforce, allowing the Agentforce Specialist to define custom logic and actions within 
the Copilot system. This makes it easy to extend Copilot's functionality without needing custom code.
While and are important Salesforce tools, are the recommended method for Apex triggers SOQL Flows
creating custom actions within Agent because they are declarative and highly adaptable.
For further guidance, refer to and .Salesforce Flow documentation Agent customization resources
Question #:6
Salesforce - Agentforce-SpecialistPass Exam
6 of 10Verified Solution - 100% Result
A. 
B. 
C. 
What is true of Agentforce Testing Center?
Running tests risks modifying CRM data in a production environment.
Running tests does not consume Einstein Requests.
Agentforce Testing Center can only be used in a production environment.
Answer: B
Explanation
Comprehensive and Detailed In-Depth Explanation:
The Agentforce Testing Center is a tool in Agentforce Studio for validating agent performance. Let’s evaluate 
the statements.
Option A: Running tests risks modifying CRM data in a production environment.Agentforce 
Testing Center runs synthetic interactions in a controlled environment (e.g., sandbox or isolated test 
space) and doesn’t modify live CRM data. It’s designed for safe pre-deployment testing, making this 
incorrect.
Option B: Running tests does not consume Einstein Requests.Einstein Requests are part of the usage 
quota for Einstein Generative AI features (e.g., prompt executions in production). Testing Center uses 
synthetic data to simulate interactions without invoking live AI calls that count against this quota. 
Salesforce documentation confirms tests don’t consume requests, making this the correct answer.
Option C: Agentforce Testing Center can only be used in a production environment.Testing 
Center is available in both sandbox and production orgs, but it’s primarily used pre-deployment (e.g., in 
sandboxes) to validate agents safely. This restriction is false, making it incorrect.
Why Option B is Correct:
Not consuming Einstein Requests is a key feature of Testing Center, allowing extensive testing without 
impacting quotas, as per Salesforce documentation.
References:
Salesforce Agentforce Documentation: Testing Center > Overview – Confirms no request consumption.
Trailhead: Test Your Agentforce Agents – Notes quota-free testing.
Salesforce Help: Agentforce Testing – Details safe, isolated testing.
Question #:7
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).
Salesforce - Agentforce-SpecialistPass Exam
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A. 
B. 
C. 
A. 
B. 
C. 
How should the Agentforce Specialist set this up?
Create the serving endpoint in Databricks, then configure the model using Model Builder.
Create the serving endpoint in Einstein Studio, then configure the model using Model Builder.
Create the serving endpoint in Databricks, then configure the model using a Python SDK connector.
Answer: A
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 
, which integrates external endpoints with Salesforce Data Cloud for processing Model Builder
and storing inferences as DMOs.
Option B: Serving endpoints are not created in Einstein Studio; they are set up in external platforms 
like Databricks before integration.
Option C: A Python SDK connector is not used to bring model inferences into Salesforce Data Cloud; 
Model Builder is the correct tool.
Reference:
"Einstein Studio and Model Integration with External Endpoints | Salesforce Trailhead" .
Question #:8
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?
Create a Flex prompt template in Prompt Builder.
Create a custom copilot action which calls a flow.
Configure the Integration Flow Standard Action in Agent.
Salesforce - Agentforce-SpecialistPass Exam
8 of 10Verified Solution - 100% Result
A. 
B. 
C. 
Answer: B
Explanation
To enable service agents to query the current fulfillment status of an order using Universal Containers
natural language and leverage an existing auto-launched flow that queries Oracle ERP, the best solution is to 
. This action will allow to interact with the flow and create a custom copilot action that calls the flow Agent
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.
Option B is correct because it enables integration between and the flow that connects to Oracle Agent
ERP.
Option A (Flex prompt template) is more suited for static responses and not for invoking flows.
Option C (Integration Flow Standard Action) is not directly related to creating a specific copilot action 
for this use case.
:
Salesforce Agent Actions: https://help.salesforce.com/s/articleView?id=einstein_copilot_actions.htm
Question #:9
An Agentforce created a custom Agent action, but it is not being picked up by the planner service in the 
correct order.
Which adjustment should the Al Specialist make in the custom Agent action instructions for the planner 
service to work as expected?
Specify the dependent actions with the reference to the action API name.
Specify the profiles or custom permissions allowed to invoke the action.
Specify the LLM model provider and version to be used to invoke the action.
Answer: A
Explanation
When a custom Agent action is not being prioritized correctly by the planner service, the root cause is often 
missingor improperly defined  . The planner service determines the execution order of action dependencies
actions based on dependencies defined in the action instructions. To resolve this, the Agentforce Specialist 
must   in the custom action’s configuration. This explicitly specify dependent actions using their API names
ensures the planner understands the sequence in which actions must be executed to meet business logic 
requirements.
Salesforce documentation highlights that dependencies are critical for orchestrating workflows in Einstein 
Bots and Agentforce. For example, if Action B requires data from Action A, Action A’s API name must be 
Salesforce - Agentforce-SpecialistPass Exam
9 of 10Verified Solution - 100% Result
A. 
B. 
C. 
listed as a dependency in Action B’s instructions. The   states that failing to Einstein Bot Developer Guide
define dependencies can lead to race conditions or incorrect execution order.
In contrast:
Profiles or custom permissions (B) control access to the action but do not influence execution order.
LLM model provider and version (C) determine the AI model used for processing but are unrelated to 
the planner’s sequencing logic.
Reference:
Salesforce Help Article: Configure Custom Actions for Einstein Bots (Section: "Defining Action 
Dependencies").
Einstein Bot Developer Guide: "Orchestrating Workflows with the Planner Service" (Dependency 
Management best practices).
Question #:10
The marketing team at Universal Containers is looking for a way personalize emails based on customer 
behavior, preferences, and purchase history.
Why should the team use Agent as the solution?
To generate relevant content when engaging with each customer
To analyze past campaign performance
To send automated emails to all customers
Answer: A
Explanation
Agent is designed to assist in generating personalized, AI-driven content based on customer data such as 
behavior, preferences, and purchase history. For the marketing team at , this is the Universal Containers
perfect solution to create dynamic and relevant email content. By leveraging , they can ensure that each Agent
customer receives tailored communications, improving engagement and conversion rates.
Option A is correct as helps generate real-time, personalized content based on comprehensive Agent
data about the customer.
Option B refers more to Einstein Analytics or
Marketing Cloud Intelligence, and deals with automation, which isn't the primary focus of Option C
.Agent
:
Salesforce - Agentforce-SpecialistPass Exam
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Salesforce Agent Overview: https://help.salesforce.com/s/articleView?id=einstein_copilot_overview.htm
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