2026 Latest Salesforce Agentforce-Specialist Real Exam Dumps PDF [Q138-Q161]

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2026 Latest Salesforce Agentforce-Specialist Real Exam Dumps PDF

Agentforce-Specialist Exam Dumps, Agentforce-Specialist Practice Test Questions

Salesforce Agentforce-Specialist Exam Syllabus Topics:

Topic	Details
Topic 1	Agentforce and Data Cloud: This section measures the skills of AI Developers and addresses how Agentforce integrates with Data Cloud to improve response accuracy and personalize answers. It involves grounding with retrievers in Data Cloud to enhance agent performance.
Topic 2	Agentforce and Service Cloud: This section measures the skills of AI Engineers and focuses on building agents that answer questions based on Knowledge articles and connecting them to digital channels. It also covers identifying the correct generative AI features in Agentforce for Service Cloud scenarios.
Topic 3	Agentforce and Sales Cloud: This section assesses the skills of AI Developers and covers identifying the correct generative AI features in Agentforce for Sales Cloud scenarios. It also includes determining when to use Agentforce Sales Agents, such as Sales Development Representatives (SDRs) and Sales Coaches.
Topic 4	Prompt Engineering: This section measures the skills of AI Developers and focuses on prompt engineering techniques. It covers identifying when to use Prompt Builder, managing prompt templates, selecting appropriate grounding techniques, and explaining the process for creating and executing prompt templates.
Topic 5	Agentforce Concepts: This section assesses the skills of AI Engineers and covers how Agentforce works, including its reasoning engine, standard and custom topics, agent actions, and user security management. It also includes testing and deploying agents from sandbox to production environments.

Q138. When creating a custom retriever in Einstein Studio, which step is considered essential?

Select the search index, specify the associated data model object (DMO) and data space, and optionally define filters to narrow search results.

Define the output configuration by specifying the maximum number of results to return, and map the output fields that will ground the prompt.

Configure the search index, choose vector or hybrid search, choose the fields for filtering, the data space and model, then define the ranking method.

Comprehensive and Detailed In-Depth Explanation:In Salesforce’s Einstein Studio (part of the Agentforce ecosystem), creating acustom retrieverinvolves setting up a mechanism to fetch data for AI prompts or responses. Theessential stepis defining the foundation of the retriever: selecting thesearch index, specifying thedata model object (DMO), and identifying thedata space(Option A). These elements establish where and what the retriever searches:
* Search Index: Determines the indexed dataset (e.g., a vector database in Data Cloud) the retriever queries.
* Data Model Object (DMO): Specifies the object (e.g., Knowledge Articles, Custom Objects) containing the data to retrieve.
* Data Space: Defines the scope or environment (e.g., a specific Data Cloud instance) for the data.
Filters are noted as optional in Option A, which is accurate-they enhance precision but aren’t mandatory for the retriever to function. This step is foundational because without it, the retriever lacks a target dataset, rendering it unusable.
* Option B: Defining output configuration (e.g., max results, field mapping) is important for shaping the retriever’s output, but it’s a secondary step. The retriever must first know where to search (A) before output can be configured.
* Option C: This option includes advanced configurations (vector/hybrid search, filtering fields, ranking method), which are valuable but not essential. A basic retriever can operate without specifying search type or ranking, as defaults apply, but it cannot function without a search index, DMO, and data space.
* Option A: This is the minimum required step to create a functional retriever, making it essential.
Option A is the correct answer as it captures the core, mandatory components of retriever setup in Einstein Studio.
References:
* Salesforce Agentforce Documentation: “Custom Retrievers in Einstein Studio” (Salesforce Help:
https://help.salesforce.com/s/articleView?id=sf.einstein_studio_retrievers.htm&type=5)
* Trailhead: “Einstein Studio for Agentforce” (https://trailhead.salesforce.com/content/learn/modules
/einstein-studio-for-agentforce)

Q139. The sales team at a hotel resort would like to generate a guest summary about the guests’ interests and provide recommendations based on their activity preferences captured in each guest profile. They want the summary to be available only on the contact record page. Which AI capability should the team use?

Model Builder

Agent Builder

Prompt Builder

Q140. Choose 1 option.
Universal Containers wants to systematically validate agent responses before deployment using a scalable testing process.
Which Testing Center approach should the company implement?

Upload a structured CSV test template and run batch test cases in Testing Center.

Manually interact with the agent in Builder until responses seem correct.

Use pilot users in production to flag incorrect responses post-launch.

Q141. An Agentforce wants to ground a new prompt template with the User related list.
What should theAgentforce Specialistconsider?

The User related list should have View All access.

The User related list needs to be included on the record page.

The User related list is not supported in prompt templates.

Q142. Universal Containers (UC) has configured a data library and wants to restrict indexing of knowledge articles to articles which are only publicly available in their knowledge base, UC also wants the agent to link sources that the large language model (LLM) grounded its response on.
Which settings should help UC with this?

In the data library setting window, under Knowledge Settings, enable Use Public Knowledge Article and select Show sources,

In the data library setting window, under Knowledge Settings, enable Use Public Knowledge Article. It is not possible to display articles that the LLM grounded its response in.

Use Data Categories to categorize publicly available articles to index. Sources are automatically displayed when knowledge articles are categorized as Public.

Q143. The Agentforce Specialist of Northern Trail Outfitters reviewed the organization’s data masking settings within the Configure Data Masking menu within Setup. Upon assessing all of the fields, a few additional fields were deemed sensitive and have been masked within Einstein’s Trust Layer.
Which steps should the Agentforce Specialist take upon modifying the masked fields?

Turn off the Einstein Trust Layer and turn it on again.

Test and confirm that the responses generated from prompts that utilize the data and masked data do not adversely affect the quality of the generated response

Turn on Einstein Feedback so that end users can report if there are any negative side effects on AI features.

Q144. For an Agentforce Data Library that contains uploaded files, what occurs once it is created and configured?

Indexes the uploaded files in a location specified by the user

Indexes the uploaded files into Data Cloud

Indexes the uploaded files in Salesforce File Storage

Q145. Universal Containers (UC) wants to implement an AI-powered customer service agent that can:
Retrieve proprietary policy documents that are stored as PDFs.
Ensure responses are grounded in approved company data, not generic LLM knowledge.What should UC do first?

Set up an Agentforce Data Library for AI retrieval of policy documents.

Expand the AI agent’s scope to search all Salesforce records.

Add the files to the content, and then select the data library option.

Q146. Which use case is best supported by Salesforce Einstein Copilot’s capabilities?

Bring together a conversational interface for interacting with AI for all Salesforce users, such as developers and ecommerce retailers.

Enable Salesforce admin users to create and train custom large language models (LLMs) using CRM data.

Enable data scientists to train predictive AI models with historical CRM data using built-in machine learning capabilities

Q147. After an agent selects a topic, what is an important factor the reasoning engine uses to select the action?

The priority given to each action

The explicit order of actions in the topic

The name and instructions of the actions

Q148. Universal Containers wants to use an external large language model (LLM) in Prompt Builder.
What should An Agentforce recommend?

Use Apex to connect to an external LLM and ground the prompt.

Use BYO-LLM functionality in Einstein Studio.

Use Flow and External Services to bring data from an external LLM.

Q149. Universal Containers has an active standard email prompt template that does not fully deliver on the business requirements. Which steps should an Agentforce Specialist take to use the content of the standard prompt email template in question and customize it to fully meet the business requirements?

Save as New Template and edit as needed.

Clone the existing template and modify as needed.

Save as New Version and edit as needed.

Universal Containers (UC) has a standard email prompt template (likely a prebuilt template provided by Salesforce) that isn’t meeting their needs, and they want to customize it while retaining its original content as a starting point. Let’s assess the options based on Agentforce prompt template management practices.
* Option A: Save as New Template and edit as needed.In Agentforce Studio’s Prompt Builder, there’s no explicit “Save as New Template” option for standard templates. This phrasing suggests creating a new template from scratch, but the question specifies using the content of the existing standard template
. Without a direct “save as” feature for standards, this option is imprecise and less applicable than cloning.
* Option B: Clone the existing template and modify as needed.Salesforce documentation confirms that standard prompt templates (e.g., for email drafting or summarization) can be cloned in Prompt Builder. Cloning creates a custom copy of the standard template, preserving its original content and structure while allowing modifications. The Agentforce Specialist can then edit the cloned template- adjusting instructions, grounding, or output format-to meet UC’s specific business requirements. This is the recommended approach for customizing standard templates without altering the original, making it the correct answer.
* Option C: Save as New Version and edit as needed.Prompt Builder supports versioning for custom templates, allowing users to save new versions of an existing template to track changes. However, standard templates are typically read-only and cannot be versioned directly-versioning applies to custom templates after cloning. The question implies starting with the standard template’s content, so cloning precedes versioning. This option is a secondary step, not the initial action, making it incorrect.
Why Option B is Correct:
Cloning is the documented method to repurpose a standard prompt template’s content while enabling customization. After cloning, the specialist can modify the new custom template (e.g., tweak the email prompt’s tone, structure, or grounding) to align with UC’s requirements. This preserves the original standard template and follows Salesforce best practices.
References:
Salesforce Agentforce Documentation: Prompt Builder > Managing Templates – Details cloning standard templates for customization.
Trailhead: Build Prompt Templates in Agentforce – Explains how to clone standard templates to create editable copies.
Salesforce Help: Customize Standard Prompt Templates – Recommends cloning as the first step for modifying prebuilt templates.

Q150. 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?

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.

Q151. 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.

Q152. Universal Containers (UC) wants to enable its sales team to use Al to suggest recommended products from its catalog.
Which type of prompt template should UC use?

Record summary prompt template

Email generation prompt template

Flex prompt template

Q153. The sales team at a hotel resort would like to generate a guest summary about the guests’ interests and provide recommendations based on their activity preferences captured in each guest profile. They want the summary to be available only on the contact record page. Which AI capability should the team use?

Model Builder

Agent Builder

Prompt Builder

Q154. 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?

The related list is Read Only.

The related list prompt template option is not enabled.

The related list is not on the parent object’s page layout.

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. Let’s analyze the options.
* Option A: The related list is Read Only.Read-only status (e.g., via field-level security or sharing rules) might limit user edits, but it doesn’t inherently prevent the AI from accessing related list data for grounding, as long as the running user (or system context) has read access. This is unlikely to cause incorrect responses and is not a primary consideration, making it incorrect.
* Option B: The related list prompt template option is not enabled.There’s no specific “related list prompt template option” toggle in Prompt Builder. When grounding with a Record Snapshot or Flex template, related lists are included if properly configured (e.g., via object relationships). This option seems to be a misphrasing and doesn’t align with documented settings, making it incorrect.
* Option C: The related list is not on the parent object’s page layout.In Agentforce, grounding with related lists relies on the related list being defined and accessible in the parent object’s metadata, often tied to its presence on the page layout. If the related list isn’t on the layout, the AI might not recognize or retrieve its data correctly, leading to incomplete or incorrect responses. Salesforce documentation notes that related list data availability can depend on layout configuration, making this a plausible and common issue during UAT, and thus the correct answer.
Why Option C is Correct:
The absence of the related list from the parent object’s page layout can disrupt data retrieval for grounding, leading to incorrect AI responses. This is a known configuration consideration in Agentforce setup and testing, as per official guidance.
References:
Salesforce Agentforce Documentation: Grounding with Related Lists – Notes dependency on page layout configuration.
Trailhead: Ground Your Agentforce Prompts – Highlights related list setup for accurate grounding.
Salesforce Help: Troubleshoot Prompt Responses – Lists layout issues as a common grounding problem.

Q155. An administrator wants to check the response of the Flex prompt
template they’ve built, but the preview button is greyed out.
What is the reason for this?

The records related to the prompt have not been selected.

The prompt has not been saved and activated,

A merge field has not been inserted in the prompt.

Q156. Universal Containers wants to automatically populate the Description field on the Account object.

Sales Email

Flex

Field Generation

Q157. An Agentforce Specialist at Universal Containers (UC) is building with no-code tools only. They have many small accounts that are only touched periodically by a specialized sales team, and UC wants to maximize the sales operations team’s time, UC wants to help prep the sales team for calls by:
* Summarizing past purchases
* Displaying products the contact has shown interest in (with data captured via Data Cloud)
* Providing a recap of past email and phone conversations that have transcripts Which approach should the Agentforce Specialist recommend to achieve this goal?

Deploy UC’s own custom foundational model on this data first.

Fine-tune the standard foundational model due to the complexity of the data.

Use a prompt template grounded on CRM and Data Cloud data using standard foundation models.

Q158. Universal Containers wants to implement a customer verification process where sensitive account information can only be accessed after the customer passes identity verification. The agent must enforce this security rule deterministically without allowing the large language model (LLM) to bypass the verification requirement.
What should an Agentforce Specialist recommend as the best solution?

Use context variables to store verification status in the messaging session and configure the agent to check these variables through natural language prompts during each sensitive action.

Include detailed verification instructions in the agent’s topic instructions explaining when customers should be verified and rely on the LLM to follow these guidelines consistently across all interactions.

Create a custom variable IsCustomerVerified set by a verification action, then apply a conditional filter using the expression IsCustomerVerified equals true to all sensitive data actions, ensuring deterministic access control that the LLM can’t alter.

Q159. Choose 1 option.
What is an Agentforce Specialist able to do when the ‘Enrich event logs with conversation data’ setting in the Agentforce configuration is enabled?

View the user click path that led to each agent action.

View session data including user input and agent responses for sessions.

Generate details reports on all agent conversations over any time period.

Q160. Universal Containers (UC) users are complaining that agent answers are not satisfactory. The agent is using PDF files as a knowledge source.
How should UC troubleshoot this issue?

Analyze the data mapping between source fields and Data Cloud object fields.

Check that the agent has the PDF file field permission access for the data library.

Verify the retriever’s filter criteria and data source connection.