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Exam : Agentforce-Specialist

Title : Salesforce Certified

Agentforce Specialist

Vendor : Salesforce

Version : DEMO

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NO.1 What is automatically created when a custom search index is created in Data Cloud?
A. A retriever that shares the name of the custom search index.
B. A dynamic retriever to allow runtime selection of retriever parameters without manual
configuration.
C. A predefined Apex retriever class that can be edited by a developer to meet specific needs.
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation:In Salesforce Data Cloud, a custom search index is
created to enable efficient retrieval of data (e.g., documents, records) for AI-driven processes, such
as grounding Agentforce responses. Let's evaluate the options based on Data Cloud's functionality.
* Option A: A retriever that shares the name of the custom search index.When a custom search index
is created in Data Cloud, a corresponding retriever is automatically generated with the same name as
the index. This retriever leverages the index to perform contextual searches (e.g., vector-based
lookups) and fetch relevant data for AI applications, such as Agentforce prompt templates. The
retriever is tied to the indexed data and is ready to use without additional configuration, aligning with
Data Cloud's streamlined approach to AI integration. This is explicitly documented in Salesforce
resources and is the correct answer.
* Option B: A dynamic retriever to allow runtime selection of retriever parameters without manual
configuration.While dynamic behavior sounds appealing, there's no concept of a "dynamic retriever"
in Data Cloud that adjusts parameters at runtime without configuration. Retrievers are tied to
specific indexes and operate based on predefined settings established during index creation. This
option is not supported by official documentation and is incorrect.
* Option C: A predefined Apex retriever class that can be edited by a developer to meet specific
needs.Data Cloud does not generate Apex classes for retrievers. Retrievers are managed within the
Data Cloud platform as part of its native AI retrieval system, not as customizable Apex code. While
developers can extend functionality via Apex for other purposes, this is not an automatic outcome of
creating a search index, making this option incorrect.
Why Option A is Correct:The automatic creation of a retriever named after the custom search index is
a core feature of Data Cloud's search and retrieval system. It ensures seamless integration with AI
tools like Agentforce by providing a ready-to-use mechanism for data retrieval, as confirmed in
official documentation.
References:
* Salesforce Data Cloud Documentation: Custom Search Indexes - States that a retriever is auto
-created with the same name as the index.
* Trailhead: Data Cloud for Agentforce - Explains retriever creation in the context of search indexes.
* Salesforce Help: Set Up Search Indexes in Data Cloud - Confirms the retriever-index relationship.

NO.2 Universal Containers is evaluating Einstein Generative AI features to improve the productivity
of the service center operation.
Which features should the Agentforce Specialist recommend?
A. Service Replies and Case Summaries
B. Service Replies and Work Summaries
C. Reply Recommendations and Sales Summaries
Answer: A
Explanation:

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To improve the productivity of the service center, the Agentforce Specialist should recommend the
Service Replies and Case Summaries features.
* Service Replies helps agents by automatically generating suggested responses to customer
inquiries, reducing response time and improving efficiency.
* Case Summaries provide a quick overview of case details, allowing agents to get up to speed faster
on customer issues.
* Work Summaries are not as relevant for direct customer service operations, and Sales Summaries
are focused on sales processes, not service center productivity.
For more information, see Salesforce's Einstein Service Cloud documentation on the use of
generative AI to assist customer service teams.

NO.3 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?
A. Model Builder
B. Agent Builder
C. Prompt Builder
Answer: C
Explanation:
Comprehensive and Detailed In-Depth Explanation:The hotel resort team needs an AI-generated
guest summary with recommendations, displayed exclusively on the contact record page. Let's assess
the options.
* Option A: Model BuilderModel Builder in Salesforce creates custom predictive AI models (e.g., for
scoring or classification) using Data Cloud or Einstein Platform data. It's not designed for generating
text summaries or embedding them on record pages, making it incorrect.
* Option B: Agent BuilderAgent Builder in Agentforce Studio creates autonomous AI agents for tasks
like lead qualification or customer service. While agents can provide summaries, they operate in
conversational interfaces (e.g., chat), not as static content on a record page. This doesn't meet the
location-specific requirement, making it incorrect.
* Option C: Prompt BuilderEinstein Prompt Builder allows creation of prompt templates that
generate text (e.g., summaries, recommendations) using Generative AI. The template can pull data
from contact records (e.g., activity preferences) and be embedded as a Lightning component on the
contact record page via a Flow or Lightning App Builder. This ensures the summary is available only
where specified, meeting the team's needs perfectly and making it the correct answer.
Why Option C is Correct:Prompt Builder's ability to generate contextual summaries and integrate
them into specific record pages via Lightning components aligns with the team's requirements, as
supported by Salesforce documentation.
References:
* Salesforce Agentforce Documentation: Prompt Builder > Embedding Prompts - Details placement on
record pages.
* Trailhead: Build Prompt Templates in Agentforce - Covers summaries from object data.
* Salesforce Help: Customize Record Pages with AI - Confirms Prompt Builder integration.

NO.4 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?

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A. Apex Trigger
B. SOQL
C. Flows
Answer: C
Explanation:
Comprehensive and Detailed In-Depth 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). Let's evaluate the options.
* Option A: Apex TriggerApex Triggers are event-driven scripts, not selectable actions in Agent
Builder. While Apex can be invoked via other means (e.g., Flows), it's not a direct option for custom
agent actions, making this incorrect.
* Option B: SOQLSOQL (Salesforce Object Query Language) is a query language, not an executable
action type in Agent Builder. While actions can use queries internally, SOQL isn't a standalone option,
making this incorrect.
* Option C: FlowsIn Agentforce Studio's Agent Builder, custom actions can be created using
Salesforce Flows. Flows allow complex logic (e.g., data retrieval, updates, or integrations) and are
explicitly supported as a custom action type. The specialist can select an existing Flow or create one,
making this the correct answer.
* Option D: JavaScriptJavaScript isn't an option for defining agent actions in Agent Builder. It's used in
Lightning Web Components, not agent configuration, making this incorrect.
Why Option C is Correct:Flows are a native, flexible option for custom actions in Agentforce, enabling
tailored functionality for agents, as per official documentation.
References:
* Salesforce Agentforce Documentation: Agent Builder > Custom Actions - Lists Flows as a supported
action type.
* Trailhead: Build Agents with Agentforce - Details Flow-based actions.
* Salesforce Help: Configure Agent Actions - Confirms Flows integration.

NO.5 Universal Containers (UC) would like to implement the Sales Development Representative
(SDR) Agent.
Which channel consideration should UC be aware of while implementing it?
A. SDR Agent must be deployed in the Messaging channel.
B. SDR Agent only works in the Email channel.
C. SDR Agent must also be deployed on the company website.
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation:Universal Containers (UC) is implementing the
Agentforce Sales Development Representative (SDR) Agent, a prebuilt AI agent designed to qualify
leads and schedule meetings. Channel considerations are critical for deployment. Let's evaluate the
options based on official Salesforce documentation.
* Option A: SDR Agent must be deployed in the Messaging channel.The Agentforce SDR Agent is
designed to engage prospects in real-time conversations, primarily through the Messaging channel
(e.g., Salesforce Messaging for in-app or web chat). This aligns with its purpose of qualifying leads
interactively and scheduling meetings, as outlined in Agentforce for Sales documentation. While it
may leverage email for follow-ups, its core deployment and interaction occur via Messaging, making

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this a key consideration UC must be aware of. This is the correct answer.
* Option B: SDR Agent only works in the Email channel.The SDR Agent is not limited to email.
While it can send emails (e.g., follow-ups after lead qualification), its primary function-real-time lead
engagement-relies on Messaging. Stating it "only works in the Email channel" is inaccurate and
contradicts its documented capabilities, making this incorrect.
* Option C: SDR Agent must also be deployed on the company website.While the SDR Agent can be
embedded on a company website via Messaging (e.g., as a chat widget), this is an implementation
choice, not a mandatory requirement. The agent's deployment is channel-specific (Messaging), and
website integration is optional, not a "must." This option overstates the requirement, making it
incorrect.
Why Option A is Correct:The SDR Agent's primary deployment in the Messaging channel is a
documented consideration for its real-time lead qualification capabilities. UC must plan for this
channel to ensure effective implementation, as per Salesforce guidelines.
References:
* Salesforce Agentforce Documentation: SDR Agent Setup > Channels - Specifies Messaging as the
primary channel.
* Trailhead: Explore Agentforce Sales Agents - Notes SDR Agent's Messaging focus for lead
engagement.
* Salesforce Help: Agentforce for Sales > SDR Agent - Confirms Messaging deployment requirement.

NO.6 Which object stores the conversation transcript between the customer and the agent?
A. Messaging End User
B. Messaging Session
C. Case
Answer: B
Explanation:
Why is "Messaging Session" the correct answer?
In Agentforce, the Messaging Session object stores the conversation transcript between the customer
and the agent.
Key Features of the Messaging Session Object:
* Stores the Entire Customer-Agent Conversation
* The Messaging Session object maintains a record of the full chat history, including timestamps,
messages, and interactions.
* This ensures that past interactions can be referenced during follow-ups.
* Supports AI-Powered Work Summaries
* Einstein AI uses Messaging Sessions to generate summaries of chat interactions for agents.
* These summaries are stored and accessible for later reference.
* Links with Service Cloud for Case Resolution
* If a conversation escalates into a case, the Messaging Session object can be linked to it.
* This allows support teams to review the conversation history without switching contexts.
Why Not the Other Options?
# A. Messaging End User
* Incorrect because this object stores details about the customer (e.g., name, contact details) but not
the conversation transcript.
# C. Case
* Incorrect because Cases store structured service requests but do not contain raw conversation

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transcripts.
* Instead, cases may reference the Messaging Session object.
Agentforce Specialist References
* Salesforce AI Specialist Material confirms that Messaging Sessions store chat conversations and
support Einstein Work Summaries.

NO.7 A Salesforce Agentforce Specialist is reviewing the feedback from a customer about the
ineffectiveness of the prompt template.
What should the Agentforce Specialist do to ensure the prompt template's effectiveness?
A. Monitor and refine the template based on user feedback.
B. Use the Prompt Builder Scorecard to help monitor.
C. Periodically change the templates grounding object.
Answer: B
Explanation:
To address the ineffectiveness of a prompt template reported by a customer, the Salesforce
Agentforce Specialist should use the Prompt Builder Scorecard (Option B). This tool is explicitly
designed to evaluate and monitor prompt templates against key criteria such as relevance, accuracy,
safety, and grounding. By leveraging the scorecard, the specialist can systematically identify
weaknesses in the template and make data- driven refinements. While monitoring and refining based
on user feedback (Option A) is a general best practice, the Prompt Builder Scorecard is Salesforce's
recommended tool for structured evaluation, aligning with documented processes for maintaining
prompt effectiveness. Changing the grounding object (Option C) without proper evaluation is reactive
and does not address the root cause.
References:
* Salesforce Einstein Agentforce Specialist Certification Guide: Emphasizes using the Prompt Builder
Scorecard to evaluate prompts and iterate based on results.
* Trailhead Module: "Einstein for Developers" highlights the scorecard as a critical tool for assessing
prompt performance.
* Salesforce Help Documentation: Details the Scorecard's role in evaluating prompts against
predefined criteria.

NO.8 Universal Containers implements three custom actions to get three distinct types of sales
summaries for its users. Users are complaining that they are not getting the right summary based on
their utterances. What should the Agentforce Specialist investigate as the root cause?
A. Review that the custom action Is assigned to an Agent.
B. Review the action Instructions to ensure they are unique.
C. Ensure the input and output types are correctly chosen.
Answer: B
Explanation:
The root cause of users receiving incorrect sales summaries lies in non-unique action instructions
(Option B). In Einstein Bots, custom actions are triggered based on how well user utterances align
with the action instructions defined for each action. If the instructions for the three custom actions
overlap or lack specificity, the bot's natural language processing (NLP) cannot reliably distinguish
between them, leading to mismatched responses.
Steps to Investigate:

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* Review Action Instructions: Ensure each custom action has distinct, context-specific instructions.
For example:
* Action 1: "Summarize quarterly sales by region."
* Action 2: "Generate a product-wise sales breakdown for the current fiscal year."
* Action 3: "Provide a comparison of sales performance between online and in-store channels."
Ambiguous or overlapping instructions (e.g., "Get sales summary") cause confusion.
* Test Utterance Matching: Use Einstein Bot's training tools to validate if user utterances map to the
correct action. Overlap indicates instruction ambiguity.
* Refine Instructions: Incorporate keywords or phrases unique to each sales summary type to
improve intent detection.
Why Other Options Are Incorrect:
* A. Assigning actions to an agent is irrelevant, as custom actions are automated bot components.
* C. Input/output types relate to data formatting, not intent routing. While important for execution,
they don't resolve utterance mismatches.
References:
* Einstein Bot Developer Guide: Stresses the need for unique action instructions to avoid intent
conflicts.
* Trailhead Module: "Build AI-Powered Bots with Einstein" highlights instruction specificity for
accurate action triggering.
* Salesforce Help Documentation: Recommends testing and refining action instructions to ensure
clarity in utterance mapping.

NO.9 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?
A. Product recommendation prediction
B. Customer churn prediction
C. Contract Renewal Date prediction
Answer: B
Explanation:
Customer churn prediction is the best use case for Model Builder in addressing Universal Containers'
concerns about increasing customer contract cancellations. By implementing a model that predicts
customer churn, UC can 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 B is correct because customer churn prediction aligns with UC's need 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

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NO.10 Universal Containers (UC) is implementing Einstein Generative AI to improve customer

insights and interactions. UC needs audit and feedback data to be accessible for reporting purposes.
What is a consideration for this requirement?
A. Storing this data requires Data Cloud to be provisioned.
B. Storing this data requires a custom object for data to be configured.
C. Storing this data requires Salesforce big objects.
Answer: A
Explanation:
When implementing Einstein Generative AI for improved customer insights and interactions, the Data
Cloud is a key consideration for storing and managing large-scale audit and feedback data. The
Salesforce Data Cloud (formerly known as Customer 360 Audiences) is designed to handle and unify
massive datasets from various sources, making it ideal for storing data required for AI-powered
insights and reporting. By provisioning Data Cloud, organizations like Universal Containers (UC) can
gain real-time access to customer data, making it a central repository for unified reporting across
various systems.
* Audit and feedback data generated by Einstein Generative AI needs to be stored in a scalable and
accessible environment, and the Data Cloud provides this capability, ensuring that data can be easily
accessed for reporting, analytics, and further model improvement.
* Custom objects or Salesforce Big Objects are not designed for the scale or the specific type of real-
time, unified data processing required in such AI-driven interactions. Big Objects are more suited for
archival data, whereas Data Cloud ensures more robust processing, segmentation, and analysis
capabilities.
References:
* Salesforce Data Cloud Documentation: https://www.salesforce.com/products/data-
cloud/overview/
* Salesforce Einstein AI Overview: https://www.salesforce.com/products/einstein/overview/

NO.11 How does Secure Data Retrieval ensure that only authorized users can access necessary
Salesforce data for dynamic grounding?
A. Retrieves Salesforce data based on the 'Run As" users permissions.
B. Retrieves Salesforce data based on the user's permissions executing the prompt.
C. Retrieves Salesforces data based on the Prompt template's object permissions.
Answer: B
Explanation:
Secure Data Retrieval enforces Salesforce's security model by dynamically grounding data access in
the permissions of the user executing the prompt. This ensures compliance with CRUD (Create, Read,
Update, Delete) and FLS (Field-Level Security) settings, preventing unauthorized access to sensitive
data. For example, if a user lacks access to a specific object or field, the AI model cannot retrieve it
for dynamic grounding.
* "Run As" user permissions (A) would bypass user-specific security, posing a compliance risk.
* Prompt template permissions (C) are not a Salesforce security mechanism; access is always tied to
the user's profile and sharing settings.

NO.12 Universal Containers (UC) wants to create a new Sales Email prompt template in Prompt
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"Save As" function. However, UC notices that the new template produces different results compared
to the standard Sales Email prompt due to missing hyperparameters.
What should UC do to ensure the new prompt template produces results comparable to the standard
Sales Email prompts?
A. Use Model Playground to create a model configuration with the specified parameters.
B. Manually add the hyperparameters to the new template.
C. Revert to using the standard template without modifications.
Answer: B
Explanation:
When Universal Containers creates a new Sales Email prompt template using the "Save As" function,
missing hyperparameters can result in different outputs. To ensure the new prompt produces
comparable results to the standard Sales Email prompt, the Agentforce Specialist should manually
add the necessary hyperparameters to the new template.
* Hyperparameters like Temperature, Frequency Penalty, and Presence Penalty directly affect how
the AI generates responses. Ensuring that these are consistent with the standard template will result
in similar outputs.
* Option A (Model Playground) is not necessary here, as it focuses on fine-tuning models, not
adjusting templates directly.
* Option C (Reverting to the standard template) does not solve the issue of customizing the prompt
template.
For more information, refer to Prompt Builder documentation on configuring hyperparameters in
custom templates.

NO.13 Universal Containers implemented Agentforce for its users. One user complains that an Agent
is not deleting activities from the past 7 days. What is the reason for this issue?
A. Agentforce does not have the permission to delete the user's records.
B. Agentforce Delete Record Action permission is not associated to the user.
C. Agentforce does not have a standard Delete Record action.
Answer: C
Explanation:
* Context of the QuestionUniversal Containers (UC) uses Agentforce, a specialized AI-driven assistant
for Salesforce. A user reports that an Agent is unable to delete recent activities.
* Why Agentforce Cannot Delete Records
* Agentforce's Standard Actions: Agentforce typically has predefined or "standard" actions like
Create, Update, or Summarize records. However, a standard Delete Record action is not part of the
default set of Agentforce actions.
* Implication: If Agentforce has no built-in delete functionality, it cannot remove activities-even if the
user has permission to delete them in the Salesforce UI.
* Why Other Options Are Incorrect
* Option A - Permission to Delete the User's Records: Standard Salesforce user permissions do not
automatically extend to Agentforce's capabilities. Even if the user can delete records, that doesn't
grant Agentforce a new action.
* Option B - Agentforce Delete Record Action Permission: There is no separate "Delete Record Action
permission" for Agentforce to be toggled. The relevant issue is that the standard Delete Record
action does not exist within Agentforce out of the box.

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* ConclusionThe core reason for the issue is that Agentforce does not support a standard Delete
Record action (Choice C).
Salesforce Agentforce Specialist References & Documents
* Salesforce Official Documentation - Agentforce(Note: Agentforce may be a pilot or specialized
feature; check pilot release notes or official docs for standard actions.)
* Salesforce Agentforce Specialist Study GuideCovers the limitations of certain AI-enabled features
regarding record operations.

NO.14 Universal Container (UC) has effectively utilized prompt templates to update summary fields
on Lightning 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?
A. Invocable Apex
B. Flow Action
C. Einstein for Flow
Answer: C
Explanation:
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.
Salesforce Agentforce Specialist References & 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

NO.15 How is Data Cloud leveraged by the Answer Questions with Knowledge action in Agentforce?
A. Data Cloud is not required; the articles can be accessed directly from the CRM by the agent.
B. Data Cloud stores and manages the Indexed Knowledge articles.
C. Data Cloud provides the real-time data streams that update the Knowledge articles.
Answer: B
Explanation:

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How Does Data Cloud Support "Answer Questions with Knowledge" in Agentforce?
The Answer Questions with Knowledge action in Agentforce leverages Salesforce Data Cloud to store,
manage, and index Knowledge articles used for AI-powered responses.
* Data Cloud as the Central Storage for Knowledge Articles
* Indexed Knowledge articles are stored and retrieved in real-time from Data Cloud.
* The AI system queries Data Cloud to fetch relevant articles when a service agent or customer needs
an answer.
* Ensuring Up-to-Date Responses
* Data Cloud continuously updates Knowledge articles based on new insights, user interactions, and
feedback.
* The AI can pull the latest, most relevant information from the Knowledge base.
* Enhancing AI-Driven Customer Service
* AI-generated responses are grounded in real customer service interactions.
* Service agents benefit from fast, context-aware answers, improving resolution times and customer
satisfaction.
Why Not the Other Options?
# A. Data Cloud is not required; the articles can be accessed directly from the CRM by the agent.
* Incorrect because Data Cloud is the primary system for storing and indexing Knowledge articles.
* Without Data Cloud, Einstein AI cannot efficiently retrieve and rank articles dynamically.
# C. Data Cloud provides the real-time data streams that update the Knowledge articles.
* Incorrect because while Data Cloud stores and manages articles, real-time updates are not its
primary function.
* The Knowledge Management system within Salesforce handles article creation and updates.
Agentforce Specialist References
* Salesforce AI Specialist Material highlights that Data Cloud is the core storage system for AI- driven
Knowledge management.
* Salesforce Instructions for Certification confirm the central role of Data Cloud in managing indexed
Knowledge articles for AI-powered responses.

NO.16 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. Access the Toxicity Detection log in Setup and export all entries where isToxicityDetected is true.
B. Create a flow that sends an email to a specified address each time the toxicity score from the
response exceeds a predefined threshold.
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.
* Option C is correct because it enables visibility into toxic language detection within the Trust Layer

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and allows for auditing responses for toxicity.

* Option A suggests checking a toxicity detection log, but Salesforce provides more comprehensive
options via the audit report.
* Option B involves creating a flow, which is unnecessary for toxicity detection monitoring.
References:
* Salesforce Trust Layer Documentation: https://help.salesforce.com/s/articleView?id=sf.
einstein_trust_layer_audit.htm

NO.17 How does an Agent respond when it can't understand the request or find any requested
information?
A. With a preconfigured message, based on the action type.
B. With a general message asking the user to rephrase the request.
C. With a generated error message.
Answer: B
Explanation:
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
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.

NO.18 Universal Containers plans to enhance the customer support team's productivity using AI.
Which specific use case necessitates the use of Prompt Builder?
A. Creating a draft of a support bulletin post for new product patches
B. Creating an Al-generated customer support agent performance score
C. Estimating support ticket volume based on historical data and seasonal trends

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Answer: A
Explanation:
The use case that necessitates the use of Prompt Builder is creating a draft of a support bulletin post
for new product patches. Prompt Builder allows the Agentforce Specialist to create and refine
prompts that generate specific, relevant outputs, such as drafting support communication based on
product information and patch details.
* Option B (agent performance score) would likely involve predictive modeling, not prompt
generation.
* Option C (estimating support ticket volume) would require data analysis and predictive tools, not
prompt building.
For more details, refer to Salesforce's Prompt Builder documentation for generative AI content
creation.

NO.19 Universal Containers is rolling out a new generative AI initiative.

Which Prompt Builder limitations should the Agentforce Specialist be aware of?
A. Rich text area fields are only supported in Flex template types.
B. Creations or updates to the prompt templates are not recorded in the Setup Audit Trail.
C. Custom objects are supported only for Flex template types.
Answer: C
Explanation:
The Prompt Builder in Salesforce has some specific limitations, one of which is that custom objects
are supported only for Flex template types. This means that users must rely on Flex templates to
integrate custom objects into their prompts.
* Option A: While rich text area fields have certain restrictions, this does not pertain to the core
limitation of integrating custom objects.
* Option B: Updates and creations for prompt templates are indeed recorded in the Setup Audit Trail,
so this statement is incorrect.
* Option C: This is the correct answer as it reflects a documented limitation of the Prompt Builder.

NO.20 For an Agentforce Data Library that contains uploaded files, what occurs once it is created
and configured?
A. Indexes the uploaded files in a location specified by the user
B. Indexes the uploaded files into Data Cloud
C. Indexes the uploaded files in Salesforce File Storage
Answer: B
Explanation:
Comprehensive and Detailed In-Depth Explanation:In Salesforce Agentforce, a Data Library is a
feature that allows organizations to upload files (e.g., PDFs, documents) to be used as grounding data
for AI-driven agents. Once the Data Library is created and configured, the uploaded files are indexed
to make their content searchable and usable by the AI (e.g., for retrieval-augmented generation or
prompt enhancement). The key question is where this indexing occurs. Salesforce Agentforce
integrates tightly with Data Cloud, a unified data platform that includes a vector database optimized
for storing and indexing unstructured data like uploaded files. When a Data Library is set up, the files
are ingested and indexed into Data Cloud's vector database, enabling the AI to efficiently retrieve
relevant information from them during conversations or actions.

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* Option A: Indexing files in a "location specified by the user" is not a feature of Agentforce Data
Libraries. The indexing process is managed by Salesforce infrastructure, not a user-defined location.
* Option B: This is correct. Data Cloud handles the indexing of uploaded files, storing them in its
vector database to support AI capabilities like semantic search and content retrieval.
* Option C: Salesforce File Storage (e.g., where ContentVersion records are stored) is used for general
file storage, but it does not inherently index files for AI use. Agentforce relies on Data Cloud for
indexing, not basic file storage.
Thus, Option B accurately reflects the process after a Data Library is created and configured in
Agentforce.
References:
* Salesforce Agentforce Documentation: "Set Up a Data Library" (Salesforce Help:
https://help.salesforce.
com/s/articleView?id=sf.agentforce_data_library.htm&type=5)
* Salesforce Data Cloud Documentation: "Vector Database for AI" (https://help.salesforce.com/s
/articleView?id=sf.data_cloud_vector_database.htm&type=5)

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