Salesforce

Agentforce-Specialist
Salesforce Certified Agentforce Specialist
QUESTION & ANSWERS

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QUESTION: 1

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?

Option A :

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

Option 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.

Option C :

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.

Correct Answer: A

Explanation/Reference:

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.

QUESTION: 2

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?

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 Option A : Action Triggers
Option B : Instructions
Option C : Output Types

Correct Answer: B

Explanation/Reference:

Comprehensive and Detailed In-Depth Explanation:UC’s development team needs to identify a core

component of a Custom Agent Action in Agent Builder. Let’s assess the options.

Option A: Action Triggers"Action Triggers" isn’t a term used in Agentforce Custom Agent Action

configuration. Actions are invoked by topics or plans, not standalone triggers, making this incorrect.

Option B: InstructionsInstructions are a core component of a Custom Agent Action in Agentforce.

Defined in Agent Builder, they guide the Atlas Reasoning Engine on how to execute the action (e.g.,

what to do with inputs, how to process data). Reviewing the instructions helps the team understand the

action’s purpose and logic, making this the correct answer.

Option C: Output TypesWhile outputs are part of an action’s result, "Output Types" isn’t a distinct

configuration element in Agent Builder. Outputs are determined by the action’s execution (e.g., Flow or

Apex), not a separate setting, making this less core and incorrect.

Why Option B is Correct:Instructions are a fundamental component of Custom Agent Actions, providing

the AI’s execution directives, as per Salesforce documentation.

References:

Salesforce Agentforce Documentation: Agent Builder > Custom Actions – Highlights instructions as

key.

Trailhead: Build Agents with Agentforce – Details configuring actions with instructions.

Salesforce Help: Create Custom Actions – Confirms instructions’ role.

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QUESTION: 3

How does the AI Retriever function within Data Cloud?

Option A :

It performs contextual searches over an indexed repository to quickly fetch the most relevant documents,
enabling grounding AI responses with trustworthy, verifiable information.

Option B : It monitors and aggregates data quality metrics across various data pipelines to ensure only
highintegrity data is used for strategic decision-making.
Option C :

It automatically extracts and reformats raw data from diverse sources into standardized datasets for use
in historical trend analysis and forecasting.

Correct Answer: A

Explanation/Reference:

Comprehensive and Detailed In-Depth Explanation:The AI Retriever is a key component in Salesforce

Data Cloud, designed to support AI-driven processes like Agentforce by retrieving relevant data. Let’s

evaluate each option based on its documented functionality.

Option A: It performs contextual searches over an indexed repository to quickly fetch the most

relevant documents, enabling grounding AI responses with trustworthy, verifiable information.

The AI Retriever in Data Cloud uses vector-based search technology to query an indexed repository (e.

g., documents, records, or ingested data) and retrieve the most relevant results based on context. It

employs embeddings to match user queries or prompts with stored data, ensuring AI responses (e.g., in Agentforce
prompt templates) are grounded in accurate, verifiable information from Data Cloud. This

enhances trustworthiness by linking outputs to source data, making it the primary function of the AI

Retriever. This aligns with Salesforce documentation and is the correct answer.

Option B: It monitors and aggregates data quality metrics across various data pipelines to ensure

only high-integrity data is used for strategic decision-making.Data quality monitoring is handled by

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other Data Cloud features, such as Data Quality Analysis or ingestion validation tools, not the AI

Retriever. The Retriever’s role is retrieval, not quality assessment or pipeline management. This option

is incorrect as it misattributes functionality unrelated to the AI Retriever.

Option C: It automatically extracts and reformats raw data from diverse sources into

standardized datasets for use in historical trend analysis and forecasting.Data extraction and

standardization are part of Data Cloud’s ingestion and harmonization processes (e.g., via Data Streams

or Data Lake), not the AI Retriever’s function. The Retriever works with already-indexed data to fetch

results, not to process or reformat raw data. This option is incorrect.

Why Option A is Correct:The AI Retriever’s core purpose is to perform contextual searches over indexed

data, enabling AI grounding with reliable information. This is critical for Agentforce agents to provide

accurate responses, as outlined in Data Cloud and Agentforce documentation.

References:

Salesforce Data Cloud Documentation: AI Retriever– Describes its role in contextual searches for

grounding.

Trailhead: Data Cloud for Agentforce– Explains how the AI Retriever fetches relevant data for AI

responses.

Salesforce Help: Grounding with Data Cloud– Confirms the Retriever’s search functionality over

indexed repositories.

QUESTION: 4

Northern Trail Outfitters (NTO) wants to configure Einstein Trust Layer in its production org but is unable to
see the option on the Setup page. After provisioning Data Cloud, which step must an Al Specialist take to
make this option available to NTO?

Option A : Turn on Einstein Copilot.

Option B : Turn on Einstein Generative AI.
Option C : Turn on Prompt Builder.

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 Correct Answer: B

Explanation/Reference:

For Northern Trail Outfitters (NTO) to configure theEinstein Trust Layer, theEinstein Generative AI

feature must be enabled. The Einstein Trust Layer is closely tied to generative AI capabilities, ensuring that

AI-generated content complies with data privacy, security, and trust standards.

Option A(Turning on Einstein Copilot) is unrelated to the setup of the Einstein Trust Layer, which

focuses more on generative AI interactions and data handling.

Option C(Turning on Prompt Builder) is used for configuring and building AI-driven prompts, but it

does not enable the Einstein Trust Layer.

SalesforceAgentforce SpecialistReferences:For more details on the Einstein Trust Layer and setup steps:

https://help.salesforce.com/s/articleView?id=sf.einstein_trust_layer_overview.htm

QUESTION: 5

An Agentforce Service Agent, who has been successfully assisting customers with service requests in
Salesforce, is now unable to help customers with issues related to a new product replacement process. The
company recently implemented a custom Product Replacement object in Salesforce to track and manage
these replacements. Which Agentforce Agent User change must be implemented to address this issue?

Option A :

The permission set group assigned to the Agent User needs to grant access to the Product Replacement
flow

Option B :

The permission set assigned to the Agent User needs Read access to the custom Product Replacement
object.

Option C :

The profile assigned to the Agentforce Agent User needs AI training permission to the custom Product
Replacement object.

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 Correct Answer: B

Explanation/Reference:

Why is "Permission Set Read Access" the correct answer?

If an Agentforce Service Agent is unable to assist customers with the new Product Replacement process, it

is likely due to missing object permissions.

Key Considerations for Object Access in Agentforce:

Custom Objects Require Permission Set Access

The new Product Replacement object must be explicitly assigned to the agent's permission set.

Without Read access, the agent cannot view or interact with the object.

Ensuring Full Data Access for Agents

In Setup # Permission Sets, the admin should:# Grant Read access to the Product Replacement

object# Ensure that related fields (e.g., status, replacement reason) are also accessible

Aligning AI and Agent Workflows

If Einstein AI is used to suggest solutions, the agent must have visibility into the Product

Replacement object for context-aware responses.

Why Not the Other Options?

# A. The permission set group assigned to the Agent User needs to grant access to the Product

Replacement flow.

Incorrect because flow permissions only control automation access, not direct object access.

If an agent cannot view the object, the flow will not be visible or usable.

# C. The profile assigned to the Agentforce Agent User needs AI training permission to the custom

Product Replacement object. Incorrect because AI training permissions relate to model learning and improvement,
not object

visibility.

Agentforce Specialist References

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Salesforce AI Specialist Material confirms that permission sets control object-level access for

Agentforce users.

QUESTION: 6

What is the main benefit of using a Knowledge article in an Agentforce Data Library?

Option A :

Only the retriever for Knowledge articles allows for agents to access Knowledge from both inside the
platform and on a customer's website.

Option B :

It provides a structured, searchable repository of approved documents so the agent can retrieve reliable
information for each inquiry..

Option C : The retriever for Knowledge articles has better accuracy and performance than the default
retriever.

Correct Answer: B

Explanation/Reference:

Why is "A structured, searchable repository of approved documents" the correct answer?

Using a Knowledge Article in an Agentforce Data Library ensures that agents can quickly access reliable

and pre-approved information during customer interactions.

Key Benefits of Knowledge Articles in an Agentforce Data Library:

Ensures Information Accuracy and Consistency

Knowledge articles provide approved, well-structured responses, reducing the risk of

misinformation.

This ensures customer service consistency across different agents.

Improves Searchability and AI-Grounded Responses

Articles are indexed and retrieved efficiently by AI-powered search engines.

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AI-generated responses are grounded in accurate, structured knowledge, improving response

quality.

Enhances Customer Support and Agent Productivity

Agents spend less time searching for information and more time resolving customer inquiries.

Einstein AI can suggest the most relevant articles based on conversation context.

Why Not the Other Options?

# A. Only the retriever for Knowledge articles allows for agents to access Knowledge from both inside

the platform and on a customer's website.

Incorrect because other retrievers (e.g., standard Salesforce Data Cloud retrievers) can also provide

knowledge access.

Knowledge articles can be accessed via multiple retrieval mechanisms, not just one specific retriever.

# C. The retriever for Knowledge articles has better accuracy and performance than the default

retriever.

Incorrect because retriever accuracy depends on indexing and search configuration, not the article

type.

The default retriever works just as efficiently when properly configured.

Agentforce Specialist References

Salesforce AI Specialist Material confirms that Knowledge articles provide structured, searchable,

and approved information for AI-grounded responses.

QUESTION: 7

How should an organization use the Einstein Trust layer to audit, track, and view masked data?

Option A : Utilize the audit trail that captures and stores all LLM submitted prompts in Data Cloud.
Option B : In Setup, use Prompt Builder to send a prompt to the LLM requesting for the masked data.
Option C : Access the audit trail in Setup and export all user-generated prompts.

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 Correct Answer: A

Explanation/Reference:

TheEinstein Trust Layeris designed to ensure transparency, compliance, and security for organizations

leveraging Salesforce’s AI and generative AI capabilities. Specifically, for auditing, tracking, and viewing

masked data, organizations can utilize:

Audit Trail in Data Cloud: Theaudit trailcaptures and stores all prompts submitted to large language

models (LLMs), ensuring that sensitive or masked data interactions are logged. This allows

organizations to monitor and audit all AI-generated outputs, ensuring that data handling complies with

internal and regulatory guidelines. TheData Cloudprovides the infrastructure for managing and

accessing this audit data.

Why not B?UsingPrompt Builderin Setup to send prompts to the LLM is for creating and managing

prompts, not for auditing or tracking data. It does not interact directly with the audit trail functionality.

Why not C?Although the audit trail can be accessed in Setup, the user-generated prompts are primarily

tracked in the Data Cloud for broader control, auditing, and analysis. Setup is not the primary tool for

exporting or managing these audit logs.

More information on auditing AI interactions can be found in theSalesforce AI Trust Layerdocumentation,

which outlines how organizations can manage and track generative AI interactions securely.

QUESTION: 8

Universal Containers (UC) needs to save agents time with AI-generated case summaries. UC has
implemented the Work Summary feature. What does Einstein consider when generating a summary?

Option A : Generation is grounded with conversation context, Knowledge articles, and cases.
Option B : Generation is grounded with existing conversation context only.
Option C : Generation is grounded with conversation context and Knowledge articles.

Correct Answer: A

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Explanation/Reference:

When generating a Work Summary, Einstein leverages multiple sources of information to provide a

comprehensive and accurate case summary for agents.

Conversation Context:

Einstein analyzes the details of the customer interaction, including chat or email threads, to

extract relevant information for the summary.

Knowledge Articles:

It considers linked Knowledge Articles or articles referred to during the case resolution process,

ensuring the summary incorporates accurate resolutions or additional resources provided to the

customer.

Cases:

Einstein also examines historical cases and related case records to ground the summary in context

from past resolutions or interactions.

Option Ais correct as it includes all three: conversation context, Knowledge articles, and cases.

Option Bis incorrect because it limits the grounding to conversation context only, excluding other

critical elements.

Option Cis incorrect because it omits case data, which Einstein considers for more accurate and

contextually rich summaries.

QUESTION: 9

Universal Containers wants to implement a solution in Salesforce with a custom UX that allows users to enter
a sales order number. Subsequently, the system will invoke a custom prompt template to create and display
a summary of the sales order header and sales order details. Which solution should an Agentforce Specialist
implement to meet this requirement?

Option A :

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 Create an autolaunched flow and invoke the prompt template using the standard "Prompt Template" flow
action.

Option B :

Create a template-triggered prompt flow and invoke the prompt template using the standard "Prompt
Template" flow action.

Option C :

Create a screen flow to collect the sales order number and invoke the prompt template using the
standard "Prompt Template" flow action.

Correct Answer: C

Explanation/Reference:

Comprehensive and Detailed In-Depth Explanation:Universal Containers (UC) requires a solution with a

custom UXfor users to input a sales order number, followed by invoking a custom prompt template to generate

and display a summary. Let’s evaluate each option based on this requirement and Salesforce Agentforce

capabilities.

Option A: Create an autolaunched flow and invoke the prompt template using the standard

"Prompt Template" flow action.An autolaunched flow is a background process that runs without user

interaction, triggered by events like record updates or platform events. While it can invoke a prompt

template using the "Prompt Template" flow action (available in Flow Builder to integrate Agentforce

prompts), it lacks a user interface. Since UC explicitly needs acustom UXfor users to enter a sales order

number, an autolaunched flow cannot meet this requirement, as it doesn’t provide a way for users to

input data directly.

Option B: Create a template-triggered prompt flow and invoke the prompt template using the

standard "Prompt Template" flow action.There’s no such thing as a "template-triggered prompt

flow" in Salesforce terminology. This appears to be a misnomer or typo in the original question. Prompt

templates in Agentforce are reusable configurations that define how an AI processes input data, but they

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are not a type of flow. Flows (like autolaunched or screen flows) can invoke prompt templates, but

"template-triggered" is not a recognized flow type in Salesforce documentation. This option is invalid

due to its inaccurate framing Option C: Create a screen flow to collect the sales order number and invoke the prompt
template

using the standard "Prompt Template" flow action.A screen flow provides a customizable user

interface within Salesforce, allowing users to input data (e.g., a sales order number) via input fields.

The "Prompt Template" flow action, available in Flow Builder, enables integration with Agentforce by

passing user input (the sales order number) to a custom prompt template. The prompt template can then

query related data (e.g., sales order header and details) and generate a summary, which can be displayed

back to the user on a subsequent screen. This solution meets UC’s need for a custom UX and seamless

integration with Agentforce prompts, making it the best fit.

Why Option C is Correct:Screen flows are ideal for scenarios requiring user interaction and custom

interfaces, as outlined in Salesforce Flow documentation. The "Prompt Template" flow action enables

Agentforce’s AI capabilities within the flow, allowing UC to collect the sales order number, process it via a

prompt template, and display the result—all within a single, user-friendly solution. This aligns with

Agentforce best practices for integrating AI-driven summaries into user workflows.

References:

Salesforce Help: Flow Builder > Prompt Template Action– Describes how to use the "Prompt

Template" action in flows to invoke Agentforce prompts.

Trailhead: Build Flows with Prompt Templates– Highlights screen flows for user-driven AI interactions.

Agentforce Studio Documentation: Prompt Templates– Explains how prompt templates process input

data for summaries.

QUESTION: 10

Universal Container (UC) has effectively utilized prompt templates to update summary fields on Lightning

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record pages. An admin now wishes to incorporate similar functionality into UC's automation process using
Flow. How can the admin get a response from this prompt template from within a flow to use as part of UC's
automation?

Option A : Invocable Apex

Option B : Flow Action
Option C : Einstein for Flow

Correct Answer: C

Explanation/Reference:

1.Context of the Question oUniversal Container (UC) has used prompt templates to update summary fields on record pages.

oNow, the admin wants to incorporate similar generative AI functionality within a Flow for automation purposes. 2.How to Call a

Prompt Template Within a Flow oFlow Action: Salesforce provides a standard way to invoke generative AI templates or prompts

within a Flow step. From the Flow Builder, you can add an “Action” that references the prompt template you created in Prompt

Builder. oOther Options: Invocable Apex: Possible fallback if there’s no out-of-the-box Flow Action available. However,

Salesforce is releasing native Flow integration for AI prompts, making custom Apex less necessary. Einstein for Flow: A broad

label for Salesforce’s generative AI features within Flow. Under the hood, you typically use a “Flow Action” that points to your

prompt. 3.Conclusion oThe easiest out-of-the-box solution is to use a Flow Action referencing the prompt template. Hence,

Option B is correct. SalesforceAgentforce SpecialistReferences & Documents •Salesforce Trailhead: Use Prompt Templates in

Flow Demonstrates how to add an Action in Flow that calls a prompt template. •Salesforce Documentation: Einstein GPT for

Flow

QUESTION: 11

Universal Containers (UC) noticed an increase in customer contract cancellations in the last few months. UC
is seeking ways to address this issue by implementing a proactive outreach program to customers before
they cancel their contracts and is asking the Salesforce team to provide suggestions. Which use case
functionality of Model Builder aligns with UC's request?

Option A : Product recommendation prediction

Option B : Customer churn prediction
Option C : Contract Renewal Date prediction

Correct Answer: B

Explanation/Reference:

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Customer churn predictionis the best use case forModel Builderin addressingUniversal Containers'

concerns about increasing customer contract cancellations. By implementing a model that predicts customer

churn,UCcan proactively identify customers who are at risk of canceling and take action to retain them before

they decide to terminate their contracts. This functionality allows the business to forecast churn probability

based on historical data and initiate timely outreach programs.

Option Bis correct becausecustomer churn predictionaligns withUC'sneed to reduce cancellations

through proactive measures.

Option A(product recommendation prediction) is unrelated to contract cancellations.

Option C(contract renewal date prediction) addresses timing but does not focus on predicting potential

cancellations.

References:

Salesforce Model Builder Use Case Overview:https://help.salesforce.com/s/articleView?id=sf.

model_builder_use_cases.htm

QUESTION: 12

How does an Agent respond when it can’t understand the request or find any requested information?

Option A : With a preconfigured message, based on the action type.

Option B : With a general message asking the user to rephrase the request.
Option C : With a generated error message.

Correct Answer: B

Explanation/Reference:

Comprehensive and Detailed In-Depth Explanation:Agentforce Agents are designed to handle situations

where they cannot interpret a request or retrieve requested data gracefully. Let’s assess the options based on

Agentforce behavior.

Option A: With a preconfigured message, based on the action type.While Agentforce allows

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customization of responses, there’s no specific mechanism tying preconfigured messages to action

types for unhandled requests. Fallback responses are more general, not action-specific, making this

incorrect.

Option B: With a general message asking the user to rephrase the request.When an Agentforce

Agent fails to understand a request or find information, it defaults to a general fallback response,

typically asking the user to rephrase or clarify their input (e.g., “I didn’t quite get that—could you try

asking again?”). This is configurable in Agent Builder but defaults to a user-friendly prompt to

encourage retry, aligning with Salesforce’s focus on conversational UX. This is the correct answer per

documentation.

Option C: With a generated error message.Agentforce Agents prioritize user experience over

technical error messages. While errors might log internally (e.g., in Event Logs), the user-facing

response avoids jargon and focuses on retry prompts, making this incorrect.

Why Option B is Correct:The default behavior of asking users to rephrase aligns with Agentforce’s

conversational design principles, ensuring a helpful response when comprehension fails, as noted in official

resources.

References:

Salesforce Agentforce Documentation: Agent Builder > Fallback Responses– Describes general retry

messages.

Trailhead: Build Agents with Agentforce– Covers handling ununderstood requests.

Salesforce Help: Agentforce Interaction Design– Confirms user-friendly fallback behavior.

QUESTION: 13

Universal Containers (UC) uses a file upload-based data library and custom prompt to support AI-driven
training content. However, users report that the AI frequently returns outdated documents. Which corrective
action should UC implement to improve content relevancy?

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 Option A :

Switch the data library source from file uploads to a Knowledge-based data library, because Salesforce
Knowledge bases automatically manage document recency, ensuring current documents are returned.

Option B :

Configure a custom retriever that includes a filter condition limiting retrieval to documents updated within
a defined recent period, ensuring that only current content is used for AI responses.

Option C :

Continue using the default retriever without filters, because periodic re-uploads will eventually phase out
outdated documents without further configuration or the need for custom retrievers.

Correct Answer: B

Explanation/Reference:

Comprehensive and Detailed In-Depth Explanation:UC’s issue is that theirfile upload-based Data Library

(where PDFs or documents are uploaded and indexed into Data Cloud’s vector database) is returning outdated

training content in AI responses. To improve relevancy by ensuring only current documents are retrieved, the

most effective solution is toconfigure a custom retriever with a filter(Option B). In Agentforce, a custom

retriever allows UC to define specific conditions—such as a filter on a "Last Modified Date" or similar

timestamp field—to limit retrieval to documents updated within a recent period (e.g., last 6 months). This

ensures the AI grounds its responses in the most current content, directly addressing the problem of outdated

documents without requiring a complete overhaul of the data source.

Option A: Switching to aKnowledge-based Data Library(using Salesforce Knowledge articles) could

work, as Knowledge articles have versioning and expiration features to manage recency. However, this

assumes UC’s training content is already in Knowledge articles (not PDFs) and requires migrating all

uploaded files, which is a significant shift not justified by the question’s context. File-based libraries are

still viable with proper filtering.

Option B: This is the best corrective action. A custom retriever with a date filter leverages the existing

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file-based library, refining retrieval without changing the data source, making it practical and targeted.

Option C: Relying on periodic re-uploads with the default retriever is passive andinefficient. It doesn’t

guarantee recency (old files remain indexed until manually removed) and requires ongoing manual

effort, failing to proactively solve the issue.

Option B provides a precise, scalable solution to ensure content relevancy in UC’s AI-driven training system.

References:

Salesforce Agentforce Documentation: "Custom Retrievers for Data Libraries" (Salesforce Help:

https://help.salesforce.com/s/articleView?id=sf.agentforce_custom_retrievers.htm&type=5)

Salesforce Data Cloud Documentation: "Filter Retrieval for AI" (https://help.salesforce.com/s

/articleView?id=sf.data_cloud_retrieval_filters.htm&type=5)

Trailhead: "Manage Data Libraries in Agentforce" (https://trailhead.salesforce.com/content/learn

/modules/agentforce-data-libraries)

QUESTION: 14

An Agentforce implements Einstein Sales Emails for a sales team. The team wants to send personalized
follow-up emails to leads based on their interactions and data stored in Salesforce. The Agentforce Specialist
needs to configure the system to use the most accurate and up-to-date information for email generation.
Which grounding technique should the Agentforce Specialist use?

Option A : Ground with Apex Merge Fields

Option B : Ground with Record Merge Fields
Option C : Automatic grounding using Draft with Einstein feature

Correct Answer: C

Explanation/Reference:

For Einstein Sales Emails to generate personalized follow-up emails, it is crucial to ground the email content

with the most up-to-date and accurate information. Grounding refers to connecting the AI model with realtime data.
The most appropriate technique in this case is Ground with Record Merge Fields. This method

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ensures that the content in the emails pulls dynamic and accurate data directly from Salesforce records, such

as lead or contact information, ensuring the follow-up is relevant and customized based on the specific record.

Record Merge Fields ensure the generated emails are highly personalized using data like lead name,

company, or other Salesforce fields directly from the records.

Apex Merge Fields are typically more suited for advanced, custom logic-driven scenarios but are not

the most straightforward for this use case.

Automatic grounding using Draft with Einstein is a different feature where Einstein automatically

drafts the email, but it does not specifically ground the content with record-specific data like Record

Merge Fields.

References:

Salesforce Einstein Sales Emails Documentation:

https://help.salesforce.com/s/articleView?id=releasenotes.rn_einstein_sales_emails.htm

QUESTION: 15

An Agentforce is considering using a Field Generation prompt template type. What should theAgentforce
Specialistcheck before creating the Field Generation prompt to ensure it is possible for the field to be
enabled for generative AI?

Option A : That the field chosen must be a rich text field with 255 characters or more.
Option B : That the org is set to API version 59 or higher
Option C : That the Lightning page layout where the field will reside has been upgraded to Dynamic
Forms

Correct Answer: B

Explanation/Reference:

Before creating aField Generation prompt template, theAgentforce Specialistmust ensure that the Salesforce

org is set to API version 59 or higher. This version of the API introduces support for advanced generative

AI features, such as enabling fields for generative AI outputs. This is a critical technical requirement for the

Field Generation prompt template to function correctly.

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Option A(rich text field requirement) is not necessary for generative AI functionality.

Option C(Dynamic Forms) does not impact the ability of a field to be generative AI-enabled, although

it might enhance the user interface.

For more information, refer toSalesforce documentation on API versioningandField Generation templates.

QUESTION: 16

Universal Containers (UC) plans to implement prompt templates that utilize the standard foundation models.
What should UC consider when building prompt templates in Prompt Builder?

Option A : Include multiple-choice questions within the prompt to test the LLM’s understanding of the
context.
Option B : Ask it to role-play as a character in the prompt template to provide more context to the LLM.
Option C :

Train LLM with data using different writing styles including word choice, intensifiers, emojis, and
punctuation.

Correct Answer: B

Explanation/Reference:

Comprehensive and Detailed In-Depth Explanation:UC is using Prompt Builder with standard foundation

models (e.g., via Atlas Reasoning Engine). Let’s assess best practices for prompt design.

Option A: Include multiple-choice questions within the prompt to test the LLM’s understanding

of the context.Prompt templates are designed to generate responses, not to test the LLM with multiplechoice
questions. This approach is impractical and not supported by Prompt Builder’s purpose, making

it incorrect.

Option B: Ask it to role-play as a character in the prompt template to provide more context to the

LLM.A key consideration in Prompt Builder is crafting clear, context-rich prompts. Instructing the

LLM to adopt a role (e.g., “Act as a sales expert”) enhances context and tailors responses to UC’s

needs, especially with standard models. This is a documented best practice for improving output

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relevance, making it the correct answer.

Option C: Train LLM with data using different writing styles including word choice, intensifiers,

emojis, and punctuation.Standard foundation models in Agentforce are pretrained and not usertrainable. Prompt
Builder users refine prompts, not the LLM itself, making this incorrect.

Why Option B is Correct:Role-playing enhances context for standard models, a recommended technique in

Prompt Builder for effective outputs, as per Salesforce guidelines.

References:

Salesforce Agentforce Documentation: Prompt Builder > Best Practices– Recommends role-based

context.

Trailhead: Build Prompt Templates in Agentforce– Highlights role-playing for clarity.

Salesforce Help: Prompt Design Tips– Suggests contextual roles.

QUESTION: 17

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?

Option A : Analyze the data mapping between source fields and Data Cloud object fields.
Option B : Check that the agent has the PDF file field permission access for the data library.
Option C : Verify the retriever's filter criteria and data source connection.

Correct Answer: C

Explanation/Reference:

Why is "Verify the retriever's filter criteria and data source connection" the correct answer? If agent answers are not

satisfactory when using PDF files as a knowledge source, the issue is likely

caused by:

Retriever misconfiguration

If filters are too broad or too restrictive, AI may fail to find relevant information.

Checking filter logic and retrieval scope helps improve accuracy.

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Incorrect data source connection

If the retriever is not properly linked to the PDF storage location, it may fail to retrieve

relevant information.

Ensuring a stable connection between Salesforce Data Cloud and the retriever prevents

retrieval failures.

Parsing Issues with PDF Files

If PDFs are not properly indexed, AI may struggle to extract relevant content.

Ensuring structured document formatting improves AI comprehension.

Why Not the Other Options?

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

Incorrect because data mapping issues primarily affect structured CRM data, not PDF-based

knowledge sources.

The issue likely stems from retrieval settings, not field mapping.

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

Incorrect because permission access issues would prevent AI from accessing PDFs entirely rather

than causing poor response quality.

AI can still generate responses, even if they are inaccurate, which means the issue lies in retriever

settings, not permissions.

Agentforce Specialist References

Salesforce AI Specialist Material details how retriever filters and data sources impact AIgenerated answers.

Salesforce Certification Guide mentions the importance of verifying retriever configurations for

accurate knowledge retrieval.

QUESTION: 18

An account manager is preparing for an upcoming customer call and wishes to get a snapshot of key data
points from accounts, contacts, leads, and opportunities in Salesforce. Which feature provides this?

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 Option A : Sales Summaries
Option B : Sales Insight Summary
Option C : Work Summaries

Correct Answer: B

Explanation/Reference:

Sales Insight Summary aggregates key data points from multiple Salesforce objects (accounts, contacts, leads,

opportunities) into a consolidated view, enabling account managers to quickly access relevant information for customer calls.

Option A (Sales Summaries): Typically refers to Einstein-generated summaries of specific

interactions (e.g., emails, calls), not multi-object snapshots.

Option C (Work Summaries): Focuses on summarizing customer service interactions (e.g., chat transcripts), not sales data.

Option B (Sales Insight Summary): Directly provides a holistic snapshot of sales-related objects, aligning with the scenario.

References:

Salesforce Help: Sales Insight Overview

Describes Sales Insight Summary as "a unified view of account, contact, and opportunity data for sales readiness."

QUESTION: 19

Universal Containers (UC) plans to automatically populate the Description field on the Account object. Which
type of prompt template should UC use?

Option A : Field Generation prompt template

Option B : Flex Prompt template
Option C : Sales Email prompt template

Correct Answer: A

Explanation/Reference:

Context of the QuestionUniversal Containers (UC) wants to automatically populate the Description

field on the Account object. The AI-driven solution must generate textual data and write it directly into

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a field.

Field Generation Prompt Template

Primary Use Case: A Field Generation prompt template is specifically designed to create or fill

in fields on a record with AI-generated text.

Auto-population: By configuring a Field Generation prompt template, admins can define the

instructions, data inputs, and desired output for the AI. The resulting text then populates the

specified field, such as the Account Description.

Why Not Flex or Sales Email Prompt Templates?

Flex Prompt Template: Used to combine or manipulate data across objects, merges, or

references from multiple sources in more advanced, flexible prompts. Typically not the go-to for

straightforward text generation on a single field.

Sales Email Prompt Template: Focused on drafting or summarizing emails for sales reps (like

crafting outreach or follow-up messages). This template is not specifically built to populate a

field on a record.

ConclusionFor automatically populating the Description field with AI-generated content, theField

Generation prompt template(Option A) is the correct choice.

SalesforceAgentforce SpecialistReferences & Documents

Salesforce Documentation:Prompt Template TypesExplains various template types (Field

Generation, Flex, Email, etc.) and their typical use cases.

SalesforceAgentforce SpecialistStudy GuideHighlights Field Generation prompt templates for

populating or updating record fields with AI-generated text.

QUESTION: 20

Universal Containers (UC) wants to improve the productivity of its sales team with generative AI technology.
However, UC is concerned that public AI virtual assistants lack adequate company data to general useful
responses. Which solution should UC consider?

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 Option A : fine-tune the Einstein AI model with CBM data.
Option B : Build Al model with Einstein discovery and deploy to sales users.
Option C : Enable Agentforce and deploy to sales users.

Correct Answer: A

Explanation/Reference:

Context of the Question: Universal Containers (UC) wants to harness generative AI to boost sales

productivity. They are wary of public AI virtual assistants (like generic chatbots) that lack sufficient

UC-specific data to generate useful business responses.

Why Fine-Tune an Einstein AI Model with CRM Data?

Company-Specific Relevance: By fine-tuning Einstein AI with UC’s CRM data (accounts,

opportunities, products, and historical interactions), the model learns the enterprise-specific

context. This ensures that the generative outputs are accurate and tailored to UC’s sales scenarios.

Security and Compliance: Using Salesforce Einstein within the Salesforce ecosystem keeps data

under UC’s control, aligning with trust, security, and compliance requirements.

Better Predictions: Einstein AI can produce more relevant insights (e.g., recommended next

steps, content suggestions, or AI-generated email responses) when it has been trained on real,

high-quality internal data.

Why Not Build an AI Model with Einstein Discovery (Option B)?

Einstein Discovery Use Case: Einstein Discovery is best suited for predictive and prescriptive

analytics (e.g., analyzing large data sets for patterns, scoring leads, or predicting churn). While it

provides advanced analytics, it is not primarily designed for generative text-based interactions for

end-user consumption in a conversational format.

Why Not Enable Agentforce (Option C)?

Agentforce Overview: “Agentforce” (sometimes referencing a pilot or non-mainstream name)

typically focuses on interactive help or workforce collaboration. It does not inherently solve the

problem of large-scale generative AI using internal CRM data. Moreover, you still need a robust

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generative engine fine-tuned on company data.

Outcome: Fine-tuning the Einstein AI model with UC’s CRM data (Answer A) is the most direct,

Salesforce-native solution to provide generative AI responses that are aligned with UC’s context,

driving productivity gains and ensuring data privacy.

SalesforceAgentforce SpecialistReferences & Documents

Salesforce Official: Einstein GPT Overview

Discusses how Einstein GPT can be fine-tuned with specific CRM data to deliver contextually

relevant, generative AI responses.

Salesforce Trailhead:Get Started with Salesforce Einstein

Explains the fundamentals of AI within the Salesforce platform, including training and

optimizing Einstein models.

Salesforce Documentation: Einstein Discovery

Details how Einstein Discovery is primarily used for advanced analytics and predictions, not

direct generative text solutions.

SalesforceAgentforce SpecialistStudy Guide

Provides the official outline of Einstein AI capabilities, referencing how to configure and finetune models for specialized

enterprise use cases.

QUESTION: 21

Which scenario best demonstrates when an Agentforce Data Library is most useful for improving an AI agent’
s response accuracy?

Option A :

When the AI agent must provide answers based on a curated set of policy documents that are stored,
regularly updated, and indexed in the data library.

Option B :

When the AI agent needs to combine data from disparate sources based on mutually common data, such

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 as Customer Id and Product Id for grounding.

Option C : When data is being retrieved from Snowflake using zero-copy for vectorization and retrieval.

Correct Answer: A

Explanation/Reference:

Comprehensive and Detailed In-Depth Explanation:The Agentforce Data Library enhances AI accuracy

by grounding responses in curated, indexed data. Let’s assess the scenarios.

Option A: When the AI agent must provide answers based on a curated set of policy documents

that are stored, regularly updated, and indexed in the data library.The Data Library is designed to

store and index structured content (e.g., Knowledge articles, policy documents) for semantic search and

grounding. It excels when an agent needs accurate, up-to-date responses from a managed corpus, like

policy documents, ensuring relevance and reducing hallucinations. This is a prime use case per

Salesforce documentation, making it the correct answer.

Option B: When the AI agent needs to combine data from disparate sources based on mutually

common data, such as Customer Id and Product Id for grounding.Combining disparate sources is

more suited to Data Cloud’s ingestion and harmonization capabilities, not the Data Library, which

focuses on indexed content retrieval. This scenario is less aligned, making it incorrect.

Option C: When data is being retrieved from Snowflake using zero-copy for vectorization and

retrieval.Zero-copy integration with Snowflake is a Data Cloud feature, but the Data Library isn’t

specifically tied to this process—it’s about indexed libraries, not direct external retrieval. This is a

different context, making it incorrect.

Why Option A is Correct:The Data Library shines in curated, indexed content scenarios like policy

documents, improving agent accuracy, as per Salesforce guidelines.

References:

Salesforce Agentforce Documentation: Data Library > Use Cases– Highlights curated content

grounding.

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Trailhead: Ground Your Agentforce Prompts– Describes Data Library accuracy benefits.

Salesforce Help: Agentforce Data Library– Confirms policy document scenario.

QUESTION: 22

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?

Option A : Record summary prompt template

Option B : Email generation prompt template
Option C : Flex prompt template

Correct Answer: C

Explanation/Reference:

Universal Containers (UC) wants to enable its sales team to leverage AI to recommend products from its

catalog. The best option for this use case is aFlex prompt template.

AFlex prompt templateis designed to provide flexible, customizable AI-driven recommendations or

responses based on specific data points, such as product information, customer needs, or sales history. This

template type allows the AI to consider various inputs and parameters, making it ideal for generating product

recommendations dynamically.

In contrast:

ARecord summary prompt template(Option A) is used to summarize data related to a specific record,

such as generating a quick summary of a sales opportunity or account, but not for recommending

products.

AnEmail generation prompt template(Option B) is tailored for crafting email content and is not

suitable for suggesting products based on a catalog.

Given the need for dynamic recommendations that pull from a product catalog and potentially other sales

data, theFlex prompt templateis the correct approach.

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Salesforce References:

Salesforce Prompt Templates Overview:https://help.salesforce.com/s/articleView?

id=000391407&type=1

Flex Prompt Template Usage:https://developer.salesforce.com/docs/atlas.en-us.salesforce_ai.meta

/salesforce_ai/prompt_flex_template

QUESTION: 23

Universal Containers wants support agents to use Agentforce to ask questions about its product tutorials and
product guides. What should theAgentforce Specialistdo to meet this requirement?

Option A : Create a prompt template for product tutorials and guides.

Option B : Add an Answer Questions custom field in the product object for tutorial instructions.
Option C : Publish product tutorials and guides as Knowledge articles.

Correct Answer: C

Explanation/Reference:

Context of the QuestionUniversal Containers (UC) wants its support agents to use Agentforce to ask

questions about product tutorials and product guides. Agentforce typically references knowledge

sources to provide accurate and contextual responses.

Why Knowledge Articles?

Centralized Repository: Publishing product tutorials and guides as Knowledge articles in

Salesforce ensures that the information is readily available and searchable by Agentforce.

AI Integration: Salesforce’s AI solutions, including Agentforce, can often be configured to pull

content directly from Salesforce Knowledge articles, giving users on-demand answers without

manual data duplication.

Maintenance & Updates: Storing content in Salesforce Knowledge simplifies content updates,

versioning, and user permissions.

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Why Not the Other Options?

Option A (Create a Prompt Template): Creating a prompt template alone does not solve how

the underlying content (tutorials, guides) is stored or accessed by Agentforce. Prompt templates

shape the queries/responses but do not provide the knowledge base.

Option B (Add an Answer Questions Custom Field): A single field on the product object is

insufficient for the depth of information found in tutorials and guides. It also lacks the robust

search and user-friendly interface that Knowledge articles provide.

ConclusionTo ensure Agentforce can effectively retrieve and deliver accurate information about

products,publishing product tutorials and guides as Knowledge articlesis the recommended

approach.

SalesforceAgentforce SpecialistReferences & Documents

Salesforce Documentation:Set Up Salesforce KnowledgeDiscusses how to publish articles for easy

access

by AI-driven assistants and support teams.

SalesforceAgentforce SpecialistStudy GuideExplains best practices for feeding knowledge sources to

generative AI and Agentforce.

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