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NO.1 Universal Containers (UC) wants to assess Salesforce's generative features but has concerns
over its company data being exposed to third- party large language models (LLMs). Specifically, UC
wants the following capabilities to be part of Einstein's generative AI service.
No data is used for LLM training or product improvements by third- party LLMs.
No data is retained outside of UC's Salesforce org.
The data sent cannot be accessed by the LLM provider.
Which property of the Einstein Trust Layer should the AI Specialist highlight to UC that addresses
these requirements?
A. Prompt Defense
B. Zero-Data Retention Policy
C. Data Masking
Answer: B
Explanation:
Universal Containers (UC) has concerns about data privacy when using Salesforce's generative AI
features, particularly around preventing third-party LLMs from accessing or retaining their data. The
Zero- Data Retention Policy in the Einstein Trust Layer is designed to address these concerns by
ensuring that:
* No data is used for training or product improvements by third-party LLMs.
* No data is retained outside of the customer's Salesforce organization.
* The LLM provider cannot access any customer data.
This policy aligns perfectly with UC's requirements for keeping their data safe while leveraging
generative AI capabilities.
* Prompt Defense and Data Masking are also security features, but they do not directly address the
concerns related to third-party data access and retention.
References:
* Salesforce Einstein Trust Layer Documentation: https://help.salesforce.com/s/articleView?id=sf.
einstein_trust_layer.htm

NO.2 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

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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.
* ConclusionThe core reason for the issue is that Agentforce does not support a standard Delete
Record action (Choice C).
Salesforce AI 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 AI Specialist Study GuideCovers the limitations of certain AI-enabled features regarding
record operations.

NO.3 Based on the user utterance, "Show me all the customers in New York", which standard
Einstein Copilot action will the planner service use?
A. Query Records
B. Select Records
C. Fetch Records
Answer: A
Explanation:
The standard Einstein Copilot action that would be used in response to the user utterance, "Show me
all the customers in New York," is Query Records. This action is responsible for retrieving a set of
records from Salesforce based on a specified condition - in this case, filtering customers by location
(New York).
* Query Records is the action that fetches relevant data based on the criteria provided in the user's
input.
* Select Records is more about picking specific records from an already presented list.
* Fetch Records is not a standard term used in this context for the action.
Refer to Einstein Copilot documentation on how Copilot actions work with natural language queries
and data retrieval.

NO.4 Universal Containers wants to use an external large language model (LLM) in Prompt Builder.
What should an AI Specialist recommend?
A. Use Apex to connect to an external LLM and ground the prompt.
B. Use BYO-LLM functionality in Einstein Studio.
C. Use Flow and External Services to bring data from an external LLM.
Answer: B
Explanation:
Bring Your Own Large Language Model (BYO-LLM) functionality in Einstein Studio allows
organizations to integrate and use external large language models (LLMs) within the Salesforce
ecosystem.
Universal Containers can leverage this feature to connect and ground prompts with external LLMs,
allowing for custom AI model use cases and seamless integration with Salesforce data.
* Option B is the correct choice as Einstein Studio provides a built-in feature to work with external
models.

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* Option A suggests using Apex, but BYO-LLM functionality offers a more streamlined solution.
* Option C focuses on Flow and External Services, which is more about data integration and isn't ideal
for working with LLMs.
References:
* Salesforce Einstein Studio BYO-LLM Documentation:
https://help.salesforce.com/s/articleView?id=sf.
einstein_studio_llm.htm

NO.5 An AI Specialist is tasked to optimize a business process flow by assigning actions to agents
within the Salesforce Agentforce Platform.
What is the correct method for the AI Specialist to assign actions to an Agent?
A. Assign the action to a Topic First in Agent Builder.
B. Assign the action to a Topic first on the Agent Actions detail page.
C. Assign the action to a Topic first on Action Builder.
Answer: C
* Action Builder is the central place in Salesforce Agentforce where you define and manage actions
that your AI agents can perform. This includes connecting actions to various tools and systems.
* Topics in Agentforce represent the different tasks or intents that an AI agent can handle. By
assigning an action to a Topic in Action Builder, you're essentially telling the agent, "When you
encounter this type of request or situation, perform this action."

NO.6 Universal Containers (UC) is Implementing Service AI Grounding to enhance its customer
service operations.
UC wants to ensure that its AI- generated responses are grounded in the most relevant data sources.
The team needs to configure the system to include all supported objects for grounding.
Which objects should UC select to configure Service AI Grounding?
A. Case, Knowledge, and Case Notes
B. Case and Knowledge
C. Case, Case Emails, and Knowledge
Answer: B
Explanation:
Universal Containers (UC) is implementing Service AI Grounding to enhance its customer service
operations.
They aim to ensure that AI-generated responses are grounded in the most relevant data sources and
need to configure the system to include all supported objects for grounding.
Supported Objects for Service AI Grounding:
* Case
* Knowledge
* Case Object:
* Role in Grounding: Provides contextual data about customer inquiries, including case details, status,
and history.
* Benefit: Grounding AI responses in case data ensures that the information provided is relevant to
the specific customer issue being addressed.
* Knowledge Object:
* Role in Grounding: Contains articles and documentation that offer solutions and information

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related to common issues.

* Benefit: Utilizing Knowledge articles helps the AI provide accurate and helpful responses based on
verified information.
* Exclusion of Other Objects:
* Case Notes and Case Emails:
* Not Supported for Grounding: While useful for internal reference, these objects are not included in
the supported objects for Service AI Grounding.
* Reason: They may contain sensitive or unstructured data that is not suitable for AI grounding
purposes.
Why Options A and C are Incorrect:
* Option A (Case, Knowledge, and Case Notes):
* Case Notes Not Supported: Case Notes are not among the supported objects for grounding in
Service AI.
* Option C (Case, Case Emails, and Knowledge):
* Case Emails Not Supported: Case Emails are also not included in the list of supported objects for
grounding.
References:
* Salesforce AI Specialist Documentation - Service AI Grounding Configuration: Details the objects
supported for grounding AI responses in Service Cloud.
* Salesforce Help - Implementing Service AI Grounding: Provides guidance on setting up grounding
with Case and Knowledge objects.
* Salesforce Trailhead - Enhance Service with AI Grounding: Offers an interactive learning path on
using AI grounding in service scenarios.

NO.7 Which business requirement presents a good use case for leveraging Einstein Prompt Builder?
A. Forecast future sales trends based on historical data.
B. Identify potential high-value leads for targeted marketing campaigns.
C. Send reply to a request for proposal via a personalized email.
Answer: C
Explanation:
* Context of the Question
* Einstein Prompt Builder is a Salesforce feature that helps generate text (summaries, email content,
responses) using AI models.
* The question presents three potential use cases, asking which one best fits the capabilities of
Einstein Prompt Builder.
* Einstein Prompt Builder Typical Use Cases
* Text Generation & Summaries: Great for writing or summarizing content, like responding to an
email or generating text for a record field.
* Why Not Forecast Future Sales Trends or Identify Potential High-Value Leads?
* (Option A) Forecasting trends typically involves predictive analytics and modeling capabilities found
in Einstein Discovery or standard reporting, not generative text solutions.
* (Option B) Identifying leads for marketing campaigns involves lead scoring or analytics, again an
Einstein Discovery or Lead Scoring scenario.
* Sending a Personalized RFP Email (Option C) is a classic example of using generative AI to compose
well-structured, context-aware text.
* ConclusionOption C (Send reply to a request for proposal via a personalized email) is the best match

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for Einstein Prompt Builder's generative text functionality.

Salesforce AI Specialist References & Documents
* Salesforce Documentation: Einstein Prompt Builder OverviewHighlights how to use Prompt Builder
to create and customize text-based responses, especially for email or record fields.
* Salesforce AI Specialist Study GuideExplains that generative AI features in Salesforce are designed
for creating or summarizing text, not for advanced predictive use cases (like forecasting or lead
scoring).

NO.8 Universal Containers is evaluating Einstein Generative AI features to improve the productivity
of the service center operation.
Which features should the AI Specialist recommend?
A. Service Replies and Case Summaries
B. Service Replies and Work Summaries
C. Reply Recommendations and Sales Summaries
Answer: A
Explanation:
To improve the productivity of the service center, the AI 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.9 Which configuration must an AI Specialist complete for users to access generative Al-enabled
fields in the Salesforce mobile app?
A. Enable Mobile Generative AI.
B. Enable Mobile Prompt Responses.
C. Enable Dynamic Forms on Mobile.
Answer: A
Explanation:
* Context of the Question
* Universal Containers (UC) has generative AI-enabled fields that users can access in the desktop
experience.
* The AI Specialist needs these same fields to be visible and usable in the Salesforce Mobile App.
* Why Dynamic Forms on Mobile?
* Dynamic Forms allow you to configure record pages so that fields and sections can appear or be
hidden based on certain criteria.
* When you enable "Dynamic Forms for Mobile," any generative AI-enabled fields placed on the
dynamic layout become accessible in the Salesforce mobile experience.
* There is no standard Setup option labeled "Enable Mobile Generative AI" or "Enable Mobile Prompt
Responses" as a universal toggle; the existing official approach is to ensure dynamic forms (and the

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relevant fields) are supported on mobile.

* Conclusion
* Ensuring that these AI-driven fields are visible on mobile is accomplished by turning on Dynamic
Forms on Mobile and adding those fields to the dynamic layout. Therefore, Option C is correct.
Salesforce AI Specialist References & Documents
* Salesforce Documentation: Dynamic Forms OverviewExplains how to enable Dynamic Forms for
both desktop and mobile UIs, allowing newly added fields (including generative AI-enabled ones) to
display in the Salesforce Mobile App.
* Salesforce AI Specialist Study GuideReiterates that to expose generative AI fields or components in
mobile, you must configure dynamic forms and ensure compatibility on mobile layouts.

NO.10 Universal Containers (UC) wants to create a new Sales Email prompt template in Prompt
Builder using the
"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 AI 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.11 Universal Containers (UC) has a mature Salesforce org with a lot of data in cases and
Knowledge articles. UC is concerned that there are many legacy fields, with data that might not be
applicable for Einstein AI to draft accurate email responses.
Which solution should UC use to ensure Einstein AI can draft responses from a defined data source?
A. Service AI Grounding
B. Work Summaries
C. Service Replies
Answer: A
Explanation:

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Service AI Grounding is the solution that Universal Containers should use to ensure Einstein AI drafts
responses based on a well-defined data source. Service AI Grounding allows the AI model to be
anchored in specific, relevant data sources, ensuring that any AI-generated responses (e.g., email
replies) are accurate, relevant, and drawn from up-to-date information, such as Knowledge articles or
cases.
Given that UC has legacy fields and outdated data, Service AI Grounding ensures that only the valid
and applicable data is used by Einstein AI to craft responses. This helps improve the relevance of
responses and avoids inaccuracies caused by outdated or irrelevant fields.
Work Summaries and Service Replies are useful features but do not address the need for grounding
AI outputs in specific, current data sources like Service AI Grounding does.
For more details, you can refer to Salesforce's Service AI Grounding documentation for managing AI-
generated content based on accurate data sources.

NO.12 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?
A. fine-tune the Einstein AI model with CBM data.
B. Build Al model with Einstein discovery and deploy to sales users.
C. Enable Agentforce and deploy to sales users.
Answer: A
Explanation:
* 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
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,

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Salesforce-native solution to provide generative AI responses that are aligned with UC's context,
driving productivity gains and ensuring data privacy.
Salesforce AI Specialist References & 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.
* Salesforce AI Specialist Study Guide
* Provides the official outline of Einstein AI capabilities, referencing how to configure and fine- tune
models for specialized enterprise use cases.

NO.13 Universal Containers (UC) wants to use Flow to bring data from unified Data Cloud objects to
prompt templates.
Which type of flow should UC use?
A. Data Cloud-triggered flow
B. Template-triggered prompt flow
C. Unified-object linking flow
Answer: B
Explanation:
In this scenario, Universal Containers wants to bring data from unified Data Cloud objects into
prompt templates, and the best way to do that is through a Data Cloud-triggered flow. This type of
flow is specifically designed to trigger actions based on data changes within Salesforce Data Cloud
objects.
Data Cloud-triggered flows can listen for changes in the unified data model and automatically bring
relevant data into the system, making it available for prompt templates. This ensures that the data is
both real-time and up-to-date when used in generative AI contexts.
For more detailed guidance, refer to Salesforce documentation on Data Cloud-triggered flows and
Data Cloud integrations with generative AI solutions.

NO.14 An AI Specialist wants to use the related lists from an account in a custom prompt template.
What should the AI Specialist consider when configuring the prompt template?
A. The text encoding (for example, UTF-8, ASCII) option
B. The maximum number of related list merge fields
C. The choice between XML and JSON rendering formats for the list
Answer: B
Explanation:
When configuring a custom prompt template to use related lists, the AI Specialist must be aware of
the maximum number of related list merge fields that can be included. Salesforce enforces limits to
ensure prompt templates perform efficiently and do not overload the system with too much data. As
a best practice, it's important to monitor and optimize the number of merge fields used.

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* Option B is correct because there is a limit on how many related list merge fields can be included in
a prompt template.
* Option A (text encoding) and Option C (XML/JSON rendering) are not key considerations in this
context.
References:
* Salesforce Prompt Builder Documentation: https://help.salesforce.com/s/articleView?id=sf.
prompt_builder.htm

NO.15 Universal Containers tests out a new Einstein Generative AI feature for its sales team to
create personalized and contextualized emails for its customers. Sometimes, users find that the draft
email contains placeholders for attributes that could have been derived from the recipient's contact
record.
What is the most likely explanation for why the draft email shows these placeholders?
A. The user does not have Einstein Sales Emails permission assigned.
B. The user does not have permission to access the fields.
C. The user's locale language is not supported by Prompt Builder.
Answer: B
Explanation:
When using Einstein Generative AI to create personalized emails, if placeholders appear in the draft
email where data from a recipient's Contact record should be, the most likely reason is that the user
lacks permission to access the necessary fields. Salesforce's field-level security may prevent users
from viewing or utilizing certain data fields, resulting in placeholders being shown instead of the
actual values.
* Option B is correct because missing field permissions will cause placeholders in email drafts.
* Option A (missing Einstein Sales Emails permission) is unlikely, as this would prevent email
generation altogether, not just placeholders.
* Option C (locale language issues) would more likely affect language-specific issues, not field
placeholders.
References:
* Salesforce Email Template and Permissions Documentation:
https://help.salesforce.com/s/articleView?
id=sf.email_templates_field_permissions.htm

NO.16 Universal Containers (UC) uses Salesforce Service Cloud to support its customers and agents
handling cases.
UC is considering implementing Einstein Copilot and extending Service Cloud to mobile users.
When would Einstein Copilot implementation be most advantageous?
A. When the goal is to streamline customer support processes and improve response times
B. When the main objective is to enhance data security and compliance measures
C. When the focus is on optimizing marketing campaigns and strategies
Answer: A
Explanation:
Einstein Copilot implementation would be most advantageous in Salesforce Service Cloud when the
goal is to streamline customer support processes and improve response times. Einstein Copilot can
assist agents by providing real-time suggestions, automating repetitive tasks, and generating

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contextual responses, thus enhancing service efficiency.

* Option B (data security) is not the primary focus of Einstein Copilot, which is more about improving
operational efficiency.
* Option C (marketing campaigns) falls outside the scope of Service Cloud and Einstein Copilot's
primary benefits, which are aimed at improving customer service and case management.
For further reading, refer to Salesforce documentation on Einstein Copilot for Service Cloud and how
it improves support processes.

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