AI Specialist Cert Flashcards
An AI Specialist 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
AI Specialist needs to configure the system to use the most accurate and up-to-date information for
email generation.
Which grounding technique should the AI Specialist use?
A. Ground with Apex Merge Fields
B. Ground with Record Merge Fields
C. Automatic grounding using Draft with Einstein feature
Answer B.
Explanation:
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 real-time data. The most appropriate technique in this case is Ground with Record Merge
Fields. This method 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.
Reference:
Salesforce Einstein Sales Emails Documentation:
https://help.salesforce.com/s/articleView?id=release-notes.rn_einstein_sales_emails.htm
Universal Containers Is Interested In Improving the sales operation efficiency by analyzing their
data using Al-powered predictions in Einstein Studio.
Which use case works for this scenario?
A. Predict customer sentiment toward a promotion message.
B. Predict customer lifetime value of an account.
C. Predict most popular products from new product catalog.
Answer: B. Predict customer lifetime value of an account.
Explanation:
For improving sales operations efficiency, Einstein Studio is ideal for creating AI-powered models that
can predict outcomes based on data. One of the most valuable use cases is predicting customer
lifetime value, which helps sales teams focus on high-value accounts and make more informed
decisions. Customer lifetime value (CLV) predictions can optimize strategies around customer
retention, cross-selling, and long-term engagement.
Option B is the correct choice as predicting customer lifetime value is a well-established use case for
AI in sales.
Option A (customer sentiment) is typically handled through NLP models, while Option C (product
popularity) is more of a marketing analysis use case.
Reference:
Salesforce Einstein Studio Use Case Overview:
https://help.salesforce.com/s/articleView?id=sf.einstein_studio_overview
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. The maximum number of related list merge fields
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.
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.
Reference:
Salesforce Prompt Builder Documentation:
https://help.salesforce.com/s/articleView?id=sf.prompt_builder.htm
What is the correct process to leverage Prompt Builder in a Salesforce org?
A. Select the appropriate prompt template type to use, select one of Salesforce’s standard prompts,
determine the object to associate the prompt, select a record to validate against, and associate the
prompt to an action.
B. Select the appropriate prompt template type to use, develop the prompt within the prompt
workspace, select resources to dynamically insert CRM-derived grounding data, pick the model to
use, and test and validate the generated responses.
C. Enable the target object for generative prompting, develop the prompt within the prompt
workspace, select records to fine-tune and ground the response, enable the Trust Layer, and
associate the prompt to an action.
Answer B. Select the appropriate prompt template type to use, develop the prompt within the prompt
workspace, select resources to dynamically insert CRM-derived grounding data, pick the model to
use, and test and validate the generated responses.
Explanation:
When using Prompt Builder in a Salesforce org, the correct process involves several important steps:
Select the appropriate prompt template type based on the use case.
Develop the prompt within the prompt workspace, where the template is created and customized.
Select CRM-derived grounding data to be dynamically inserted into the prompt, ensuring that the AI-
generated responses are based on accurate and relevant data.
Pick the model to use for generating responses, either using Salesforce’s built-in models or custom
ones.
Test and validate the generated responses to ensure accuracy and effectiveness.
Option B is correct as it follows the proper steps for using Prompt Builder.
Option A and Option C do not capture the full process correctly.
Reference:
Salesforce Prompt Builder Documentation:
https://help.salesforce.com/s/articleView?id=sf.prompt_builder_overview.htm
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 AI Specialist implement to meet this requirement?
A. Create a screen flow to collect sales order number and invoke the prompt template using the
standard “Prompt Template” flow action.
B. Create a template-triggered prompt flow and invoke the prompt template using the standard
“Prompt Template” flow action.
C. Create an autolaunched flow and invoke the prompt template using the standard “Prompt
Template” flow action.
Answer: A. Create a screen flow to collect sales order number and invoke the prompt template using the
standard “Prompt Template” flow action.
Explanation:
To implement a solution where users enter a sales order number and the system generates a
summary, the AI Specialist should create a screen flow to collect the sales order number and invoke
the prompt template. The standard “Prompt Template” flow action can then be used to trigger the
custom prompt, providing a summary of the sales order header and details.
Option B, creating a template-triggered prompt flow, is not necessary for this scenario because the
requirement is to directly collect input through a screen flow.
Option C, using an autolaunched flow, would be inappropriate here because the solution requires
user interaction (entering a sales order number), which is best suited to a screen flow.
Salesforce AI Specialist Reference:
For further guidance on creating prompt templates with flows:
https://help.salesforce.com/s/articleView?id=sf.prompt_template_flow_integration.htm
What should an AI Specialist consider when using related list merge fields in a prompt
template associated with an Account object in Prompt Builder?
A. The Activities related list on the Account object is not supported because it is a polymorphic field.
B. If person accounts have been enabled, merge fields will not be available for the Account object.
C. Prompt generation will yield no response when there is no related list associated with an Account
in runtime.
Answer A. The Activities related list on the Account object is not supported because it is a polymorphic field.
Explanation:
When using related list merge fields in a prompt template associated with the Account object in
Prompt Builder, the Activities related list is not supported due to it being a polymorphic field.
Polymorphic fields can reference multiple different types of objects, which makes them incompatible
with some merge field operations in prompt generation.
Option B is incorrect because person accounts do not limit the availability of merge fields for the
Account object.
Option C is irrelevant since even if no related lists are available at runtime, the prompt can still
generate based on other available data fields.
For more information, refer to Salesforce documentation on supported fields and limitations in Prompt Builder
An Al Specialist is tasked with configuring a generative model to create personalized sales
emails using customer data stored in Salesforce. The AI Specialist has already fine-tuned a large
language model (LLM) on the OpenAI platform. Security and data privacy are critical concerns for the
client.
How should the AI Specialist integrate the custom LLM into Salesforce?
A. Create an application of the custom LLM and embed it in Sales Cloud via iFrame.
B. Add the fine-tuned LLM in Einstein Studio Model Builder.
C. Enable model endpoint on OpenAl and make callouts to the model to generate emails.
Answer: B. Add the fine-tuned LLM in Einstein Studio Model Builder.
Explanation:
Since security and data privacy are critical, the best option for the AI Specialist is to integrate the fine-
tuned LLM (Large Language Model) into Salesforce by adding it to Einstein Studio Model Builder.
Einstein Studio allows organizations to bring their own AI models (BYOM), ensuring the model is
securely managed within Salesforce’s environment, adhering to data privacy standards.
Option A (embedding via iFrame) is less secure and doesn’t integrate deeply with Salesforce’s data
and security models.
Option C (making callouts to OpenAI) raises concerns about data privacy, as sensitive Salesforce data
would be sent to an external system.
Einstein Studio provides the most secure and seamless way to integrate custom AI models while
maintaining control over data privacy and compliance. More details can be found in Salesforce’s
Einstein Studio documentation on integrating external models.
NO.8 Universal Containers (UC) wants to enable its sales reps to explore opportunities that are
similar to previously won opportunities by entering the utterance, “Show me other opportunities like
this one.” How should UC achieve this in Einstein Copilot?
A. Use the standard Copilot action.
B. Create a custom Copilot action calling a flow.
C. Create a custom Copilot action calling an Apex class.
Answer: A
Explanation:
Universal Containers can achieve the request to explore similar opportunities by using the standard
Copilot action. Einstein Copilot has built-in actions to handle natural language queries, such as “Show
me other opportunities like this one.” The standard action will process the query and return results
based on predefined matching criteria like opportunity details and past Closed Won deals.
This approach avoids the need to create custom flows or Apex classes, leveraging out-of-the-box
functionality.
For further details, refer to Einstein Copilot for Sales documentation regarding standard actions and natural language processing.
Universal Containers (UC) wants to offer personalized service experiences and reduce agent
handling time with Al-generated email responses, grounded in Knowledge base.
Which AI capability should UC use?
A. Einstein Email Replies
B. Einstein Service Replies for Email
C. Einstein Generative Service Replies for Email
Answer: B
Explanation:
For Universal Containers (UC) to offer personalized service experiences and reduce agent handling
time using AI-generated responses grounded in the Knowledge base, the best solution is Einstein
Service Replies for Email. This capability leverages AI to automatically generate responses to service-
related emails based on historical data and the Knowledge base, ensuring accuracy and relevance
while saving time for service agents.
Einstein Email Replies (option A) is more suited for sales use cases.
Einstein Generative Service Replies for Email (option C) could be a future offering, but as of now,
Einstein Service Replies for Email is the correct choice for grounded, knowledge-based responses.
Reference:
Einstein Service Replies Overview:
https://help.salesforce.com/s/articleView?id=sf.einstein_service_replies.htm
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
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 AI 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 det
Universal Containers wants to utilize Einstein for Sales to help sales reps reach their sales
quotas by providing Al-generated plans containing guidance and steps for closing deals.
Which feature should the AI Specialist recommend to the sales team?
A. Find Similar Deals
B. Create Account Plan
C. Create Close Plan
Answer: C
Explanation:
The “Create Close Plan” feature is designed to help sales reps by providing AI-generated strategies
and steps specifically focused on closing deals. This feature leverages AI to analyze the current state
of opportunities and generate a plan that outlines the actions, timelines, and key steps required to
move deals toward closure. It aligns directly with the sales team’s need to meet quotas by offeringactionable insights and structured plans.
Find Similar Deals (Option A) helps sales reps discover opportunities similar to their current deals but
doesn’t offer a plan for closing.
Create Account Plan (Option B) focuses on long-term strategies for managing accounts, which might
include customer engagement and retention, but doesn’t focus on deal closure.
Salesforce AI Specialist Reference:
For more information on using AI for sales, visit:
https://help.salesforce.com/s/articleView?id=sf.einstein_for_sales_overview.htm
Universal Containers wants to make a sales proposal and directly use data from multiple
unrelated objects (standard and custom) in a prompt template.
What should the AI Specialist recommend?
A. Create a Flex template to add resources with standard and custom objects as inputs.
B. Create a prompt template passing in a special custom object that connects the records
temporarily,
C. Create a prompt template-triggered flow to access the data from standard and custom objects.
Answer: A
Explanation:
Universal Containers needs to generate a sales proposal using data from multiple unrelated standard
and custom objects within a prompt template. The most effective way to achieve this is by using a
Flex template.
Flex templates in Salesforce allow AI specialists to create prompt templates that can accept inputs
from multiple sources, including various standard and custom objects. This flexibility enables the
direct use of data from unrelated objects without the need to create intermediary custom objects or
complex flows.
Reference:
Salesforce AI Specialist Documentation - Flex Templates: Explains how Flex templates can be utilized
to incorporate data from multiple sour
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.
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.
Reference:
Salesforce Einstein Studio BYO-LLM Documentation:
https://help.salesforce.com/s/articleView?id=sf.einstein_studio_llm.htm
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.
Reference:
Salesforce Model Builder Use Case Overview:
https://help.salesforce.com/s/articleView?id=sf.model_builder_use_cases.htm
Which use case is best supported by Salesforce Einstein Copilot’s capabilities?
A. Bring together a conversational interface for interacting with AI for all Salesforce users, such as
developers and ecommerce retailers.
B. Enable Salesforce admin users to create and train custom large language models (LLMs) using CRM
data.
C. Enable data scientists to train predictive AI models with historical CRM data using built-in machine
learning capabilities
Answer: A
Explanation:
Salesforce Einstein Copilot is designed to provide a conversational AI interface that can be utilized by
different types of Salesforce users, such as developers, sales agents, and retailers. It acts as an AI-
powered assistant that facilitates natural interactions with the system, enabling users to perform
tasks and access data easily. This includes tasks like pulling reports, updating records, and generating
personalized responses in real time.
Option A is correct because Einstein Copilot brings a conversational interface that caters to a wide
range of users.
An AI Specialist needs to create a Sales Email with a custom prompt template. They need to
ground on the following data.
Opportunity Products Events near the customer Tone and voice examples
How should the AI Specialist obtain related items?
A. Call prompt initiated flow to fetch and ground the required data.
B. Create a flex template that takes the records in question as inputs.
C. Utilize a standard email template and manually insert the required data fields.
Answer: A
Explanation:
To ground a sales email on Opportunity Products, Events near the customer, and Tone and voice
examples, the AI Specialist should use a prompt-initiated flow. This flow can dynamically fetch the
necessary data from related records in Salesforce and ground the generative AI output with
contextually accurate information.
Option B (flex template) does not provide the ability to fetch dynamic data from Salesforce records
automatically.
Option C (manual insertion) would not allow for the dynamic and automated grounding of data
required for custom prompts.
Refer to Salesforce documen
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.
Reference:
Salesforce Email Template and Permissions Documentation:
https://help.salesforce.com/s/articleView?id=sf.email_templates_field_permissions.htm
Universal Containers plans to implement prompt templates that utilize the standard
foundation models.
What should the AI Specialist consider when building prompt templates in Prompt Builder?
A. Include multiple-choice questions within the prompt to test the LLM’s understanding of the
context.
B. Ask it to role-play as a character in the prompt template to provide more context to the LLM.
C. Train LLM with data using different writing styles including word choice, intensifiers, emojis, and
punctuation.
Answer: C
Explanation:
When building prompt templates in Prompt Builder, it is essential to consider how the Large
Language Model (LLM) processes and generates outputs. Training the LLM with various writing styles,
such as different word choices, intensifiers, emojis, and punctuation, helps the model better
understand diverse writing patterns and produce more contextually appropriate responses.
This approach enhances the flexibility and accuracy of the LLM when generating outputs for different
use cases, as it is trained to recognize various writing conventions and styles. The prompt template
should focus on providing rich context, and this stylistic variety helps improve the model’s
adaptability.
Options A and B are less relevant because adding multiple-choice questions or role-playing scenarios
doesn’t contribute significantly to improving the AI’s output generation quality within standard
business contexts.
For more details, refer to Salesforce’s Prompt Builder documentation and LLM tuning strategies.
Universal Containers (UC) plans to send one of three different emails to its customers based
on the customer’s lifetime value score and their market segment.
Considering that UC are required to explain why an e-mail was selected, which AI model should UC
use to achieve this?
A. Predictive model and generative model
B. Generative model
C. Predictive model
Answer: C
Explanation:
Universal Containers should use a Predictive model to decide which of the three emails to send based
on the customer’s lifetime value score and market segment. Predictive models analyze data to
forecast outcomes, and in this case, it would predict the most appropriate email to send based on
customer attributes. Additionally, predictive models can provide explainability to show why a certain
email was chosen, which is crucial for UC’s requirement to explain the decision-making process.
Generative models are typically used for content creation, not decision-making, and thus wouldn’t be
suitable for this requirement.
Predictive models offer the ability to explain why a particular decision was made, which aligns with UC’s needs.
Refer to Salesforce’s Predictive AI model documentation for more insights on how predictive models
are used for segmentation and decision making.
An AI Specialist has created a copilot custom action using flow as the reference action type.
However, it is not delivering the expected results to the conversation preview, and therefore needs
troubleshooting.
What should the AI Specialist do to identify the root cause of the problem?
A. In Copilot Builder within the Dynamic Panel, turn on dynamic debugging to show the inputs and
outputs.
B. Copilot Builder within the Dynamic Panel, confirm selected action and observe the values in Input
and Output sections.
C. In Copilot Builder, verify the utterance entered by the user and review session event logs for
debug information.
Answer: A
Explanation:
When troubleshooting a copilot custom action using flow as the reference action type, enabling
dynamic debugging within Copilot Builder’s Dynamic Panel is the most effective way to identify the
root cause. By turning on dynamic debugging, the AI Specialist can see detailed logs showing both the
inputs and outputs of the flow, which helps identify where the action might be failing or not
delivering the expected results.
Option B, confirming selected actions and observing the Input and Output sections, is useful for
monitoring flow configuration but does not provide the deep diagnostic details available with
dynamic debugging.
Option C, verifying the user utterance and reviewing session event logs, could provide helpful
context, but dynamic debugging is the primary tool for identifying issues with inputs and outputs in
real time.
Salesforce AI Specialist Reference:
To explore more about dynamic debugging in Copilot Builder, see:
https://help.salesforce.com/s/articleView?id=sf.copilot_custom_action_debugging.htm
How should an organization use the Einstein Trust layer to audit, track, and view masked
data?
A. Utilize the audit trail that captures and stores all LLM submitted prompts in Data Cloud.
B. In Setup, use Prompt Builder to send a prompt to the LLM requesting for the masked data.
C. Access the audit trail in Setup and export all user-generated prompts.
Answer: A
Explanation:
The Einstein Trust Layer is 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: The audit trail captures 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. The Data Cloud provides the infrastructure for managing andaccessing this audit data.
Why not B? Using Prompt Builder in 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 the Salesforce AI Trust Layer
documentation, which outlines how organizations can manage and track generative AI interactions
securely.
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
Explanation:
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.”
An administrator is responsible for ensuring the security and reliability of Universal
Containers’ (UC) CRM dat a. UC needs enhanced data protection and up-to-date AI capabilities. UC
also needs to include relevant information from a Salesforce record to be merged with the prompt.
Which feature in the Einstein Trust Layer best supports UC’s need?
A. Data masking
B. Dynamic grounding with secure data retrieval
C. Zero-data retention policy
Answer: B
Explanation:
Dynamic grounding with secure data retrieval is a key feature in Salesforce’s Einstein Trust Layer,
which provides enhanced data protection and ensures that AI-generated outputs are both accurate
and securely sourced. This feature allows relevant Salesforce data to be merged into the AI-
generated responses, ensuring that the AI outputs are contextually aware and aligned with real-time
CRM data.
Dynamic grounding means that AI models are dynamically retrieving relevant information from
Salesforce records (such as customer records, case data, or custom object data) in a secure manner.
This ensures that any sensitive data is protected during AI processing and that the AI model’s outputs
are trustworthy and reliable for business use.
The other options are less aligned with the requirement:
Data masking refers to obscuring sensitive data for privacy purposes and is not related to merging
Salesforce records into prompts.
Zero-data retention policy ensures that AI processes do not store any user data after processing, but
this does not address the need to merge Salesforce record information into a prompt.
Reference:
Salesforce Developer Documentation on Einstein Trust Layer
Salesforce Security Documentation for AI and Data Privacy
A service agent is looking at a custom object that stores travel information. They recently
received a weather alert and now need to cancel flights for the customers that are related with this
itinerary. The service agent needs to review the Knowledge articles about canceling and rebooking
the customer flights.
Which Einstein Copilot capability helps the agent accomplish this?
A. Execute tasks based on available actions, answering questions using information from accessible
Knowledge articles.
B. Invoke a flow which makes a call to external data to create a Knowledge article.
C. Generate a Knowledge article based off the prompts that the agent enters to create steps to
cancel flights.
Answer: A
Explanation:
In this scenario, the Einstein Copilot capability that best helps the agent is its ability to execute tasks
based on available actions and answer questions using data from Knowledge articles. Einstein Copilot
can assist the service agent by providing relevant Knowledge articles on canceling and rebooking
flights, ensuring that the agent has access to the correct steps and procedures directly within the
workflow.
This feature leverages the agent’s existing context (the travel itinerary) and provides actionable
insights or next steps from the relevant Knowledge articles to help the agent quickly resolve the
customer’s needs. The other options are incorrect:
B refers to invoking a flow to create a Knowledge article, which is unrelated to the task of retrieving
existing Knowledge articles.
C focuses on generating Knowledge articles, which is not the immediate need for this situation where
the agent requires guidance on existing procedures.
Reference:
Salesforce Documentation on Einstein Copilot
Trailhead Module on Einstein for Service
Universal Containers needs a tool that can analyze voice and video call records to provide
insights on competitor mentions, coaching opportunities, and other key information. The goal is to
enhance the team’s performance by identifying areas for improvement and competitive intelligence.
Which feature provides insights about competitor mentions and coaching opportunities?
A. Call Summaries
B. Einstein Sales Insights
C. Call Explorer
Answer: C
Explanation:
For analyzing voice and video call records to gain insights into competitor mentions, coaching
opportunities, and other key information, Call Explorer is the most suitable feature. Call Explorer, a
part of Einstein Conversation Insights, enables sales teams to analyze calls, detect patterns, and
identify areas where improvements can be made. It uses natural language processing (NLP) to extract
insights, including competitor mentions and moments for coaching. These insights are vital for
improving sales performance by providing a clear understanding of the interactions during calls.
Call Summaries offer a quick overview of a call but do not delve deep into competitor mentions or
coaching insights.
Einstein Sales Insights focuses more on pipeline and forecasting insights rather than call-based
analysis.
Reference:
Salesforce Einstein Conversation Insights Documentation:
https://help.salesforce.com/s/articleView?id=einstein_conversation_insights.htm
What is the primary function of the planner service in the Einstein Copilot system?
A. Generating record queries based on conversation history
B. Offering real-time language translation during conversations
C. Identifying copilot actions to respond to user utterances
Answer: C
Explanation:
The primary function of the planner service in the Einstein Copilot system is to identify copilot actions
that should be taken in response to user utterances. This service is responsible for analyzing the
conversation and determining the appropriate actions (such as querying records, generating a response, or taking another action) that the Einstein Copilot should perform based on user input.
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.
Reference:
Salesforce Einstein Trust Layer Documentation:
https://help.salesforce.com/s/articleView?id=sf.einstein_trust_layer.htm
Where should the AI Specialist go to add/update actions assigned to a copilot?
A. Copilot Actions page, the record page for the copilot action, or the Copilot Action Library tab
B. Copilot Actions page or Global Actions
C. Copilot Detail page, Global Actions, or the record page for the copilot action
Answer: A
Explanation:
To add or update actions assigned to a copilot, an AI Specialist can manage this through several
areas:
Copilot Actions Page: This is the central location where copilot actions are managed and configured.
Record Page for the Copilot Action: From the record page, individual copilot actions can be updated
or modified.
Copilot Action Library Tab: This tab serves as a repository where predefined or custom actions for
Copilot can be accessed and modified.
These areas provide flexibility in managing and updating the actions assigned to Copilot, ensuring
that the AI assistant remains aligned with business requirements and processes.
The other options are incorrect:
B misses the Copilot Action Library, which is crucial for managing actions.
C includes the Copilot Detail page, which isn’t the primary place for action management.
Reference:
Salesforce Documentation on Managing Copilot Actions
Salesforce AI Specialist Guide on Copilot Action Management
Universal Containers (UC) wants to use the Draft with Einstein feature in Sales Cloud to create
a personalized introduction email.
After creating a proposed draft email, which predefined adjustment should UC choose to revise the
draft with a more casual tone?
A. Make Less Formal
B. Enhance Friendliness
C. Optimize for Clarity
Answer: A
Explanation:
When Universal Containers uses the Draft with Einstein feature in Sales Cloud to create a
personalized email, the predefined adjustment to Make Less Formal is the correct option to revise
the draft with a more casual tone. This option adjusts the wording of the draft to sound less formal,
making the communication more approachable while still maintaining professionalism.
Enhance Friendliness would make the tone more positive, but not necessarily more casual.
Optimize for Clarity focuses on making the draft clearer but doesn’t adjust the tone.
Universal Containers’ data science team is hosting a generative large language model (LLM)
on Amazon Web Services (AWS).
What should the team use to access externally-hosted models in the Salesforce Platform?
A. Model Builder
B. App Builder
C. Copilot Builder
Answer: A
Explanation:
To access externally-hosted models, such as a large language model (LLM) hosted on AWS, the Model
Builder in Salesforce is the appropriate tool. Model Builder allows teams to integrate and deploy
external AI models into the Salesforce platform, making it possible to leverage models hosted outside
of Salesforce infrastructure while still benefiting from the platform’s native AI capabilities.
Option B, App Builder, is primarily used to build and configure applications in Salesforce, not to
integrate AI models.
Option C, Copilot Builder, focuses on building assistant-like tools rather than integrating external AI
models.
Model Builder enables seamless integration with external systems and models, allowing Salesforce
users to use external LLMs for generating AI-driven insights and automation.
Salesforce AI Specialist Reference:
For more details, check the Model Builder guide here:
https://help.salesforce.com/s/articleView?id=sf.model_builder_external_models.htm
Universal Containers (UC) wants to improve the efficiency of addressing customer questions
and reduce agent handling time with AI- generated responses. The agents should be able to leverage
their existing knowledge base and identify whether the responses are coming from the large
language model (LLM) or from Salesforce Knowledge.
Which step should UC take to meet this requirement?
A. Turn on Service AI Grounding, Grounding with Case, and Service Replies.
B. Turn on Service Replies, Service AI Grounding, and Grounding with Knowledge.
C. Turn on Service AI Grounding and Grounding with Knowledge.
Answer: B
Explanation:
To meet Universal Containers’ goal of improving efficiency and reducing agent handling time with AI-
generated responses, the best approach is to enable Service Replies, Service AI Grounding, and
Grounding with Knowledge.
Service Replies generates responses automatically.
Service AI Grounding ensures that the AI is using relevant case data.
Grounding with Knowledge ensures that responses are backed by Salesforce Knowledge articles,
allowing agents to identify whether a response is coming from the LLM or Salesforce Knowledge.
Option C does not include Service Replies, which is necessary for generating AI responses.
Option A lacks the Grounding with Knowledge, which is essential for identifying response sources.
For more details, refer to Salesforce Service AI documentation on grounding and service replies.
Universal Containers (UC) has a legacy system that needs to integrate with Salesforce. UC
wishes to create a digest of account action plans using the generative API feature.
Which API service should UC use to meet this requirement?
A. REST API
B. Metadata API
C. SOAP API
Answer: A
Explanation:
To create a digest of account action plans using the generative API feature, Universal Containers
should use the REST API. The REST API is ideal for integrating Salesforce with external systems and
enabling interaction with Salesforce data, including generative capabilities like creating summaries or
digests. It supports modern web standards and is suitable for flexible, lightweight interactions
between Salesforce and legacy systems.
Metadata API is used for retrieving and deploying metadata, not for data operations like generating
summaries.
SOAP API is an older API used for integration but is less flexible compared to REST for this specific use
case.
For more details, refer to Salesforce REST API documentation regarding using REST for data
integration and generating content.
An AI Specialist turned on Einstein Generative AI in Setup. Now, the AI Specialist would like to
create custom prompt templates in Prompt Builder. However, they cannot access Prompt Builder in
the Setup menu.
What is causing the problem?
A. The Prompt Template User permission set was not assigned correctly.
B. The Prompt Template Manager permission set was not assigned correctly.
C. The large language model (LLM) was not configured correctly in Data Cloud.
Answer: B
Explanation:
In order to access and create custom prompt templates in Prompt Builder, the AI Specialist must have
the Prompt Template Manager permission set assigned. Without this permission, they will not be
able to access Prompt Builder in the Setup menu, even though Einstein Generative AI is enabled.
Option B is correct because the Prompt Template Manager permission set is required to use Prompt
Builder.
Option A (Prompt Template User permission set) is incorrect because this permission allows users to
use prompts, but not create or manage them.
Option C (LLM configuration in Data Cloud) is unrelated to the ability to access Prompt Builder.
Reference:
Salesforce Prompt Builder Permissions:
https://help.salesforce.com/s/articleView?id=sf.prompt_builder_permissions.htm
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 Salesforce big objects.
C. Storing this data requires a custom object for data to be configured.
Answer A. Storing this data requires Data Cloud to be provisioned.
Before activating a custom copilot action, an AI Specialist would like is to understand multiple
real-world user utterances to ensure the action being selected appropriately.
Which tool should the AI Specialist recommend?
A. Model Playground
B. Einstein Copilot
C. Copilot Builder
Answer: C
Explanation:
To understand multiple real-world user utterances and ensure the correct action is selected before
activating a custom copilot action, the recommended tool is Copilot Builder. This tool allows AI
Specialists to design and test conversational actions in response to user inputs, helping ensure the
copilot can accurately handle different user queries and phrases. Copilot Builder provides the ability
to test, refine, and improve actions based on real-world utterances.
Option C is correct as Copilot Builder is designed for configuring and testing conversational actions.
Option A (Model Playground) is used for testing models, not user utterances.
Option B (Einstein Copilot) refers to the conversational interface but isn’t the right tool for designing
and testing actions.
Reference:
Salesforce Copilot Builder Overview:
https://help.salesforce.com/s/articleView?id=sf.einstein_copilot_builder.htm
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?
A. Record summary prompt template
B. Email generation prompt template
C. Flex prompt template
Answer: C
Explanation:
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 a Flex prompt template.
A Flex prompt template is 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:
A Record 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.
An Email 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, the Flex prompt template is the correct approach.
Salesforce Reference:
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
A support team handles a high volume of chat interactions and needs a solution to provide
quick, relevant responses to customer inquiries.
Responses must be grounded in the organization’s knowledge base to maintain consistency and
accuracy.
Which feature in Einstein for Service should the support team use?
A. Einstein Service Replies
B. Einstein Reply Recommendations
C. Einstein Knowledge Recommendations
Answer: B
Explanation:
The support team should use Einstein Reply Recommendations to provide quick, relevant responses
to customer inquiries that are grounded in the organization’s knowledge base. This feature leverages
AI to recommend accurate and consistent replies based on historical interactions and the knowledge
stored in the system, ensuring that responses are aligned with organizational standards.
Einstein Service Replies (Option A) is focused on generating replies but doesn’t have the same
emphasis on grounding responses in the knowledge base.
Einstein Knowledge Recommendations (Option C) suggests knowledge articles to agents, which is
more about assisting the agent in finding relevant articles than providing automated or AI-generated
responses to customers.
Salesforce AI Specialist Reference:
For more information on Einstein Reply Recommendations:
https://help.salesforce.com/s/articleView?id=sf.einstein_reply_recommendations_overview.htm
An AI Specialist configured Data Masking within the Einstein Trust Layer.
How should the AI Specialist begin validating that the correct fields are being masked?
A. Use a Flow-based resource in Prompt Builder to debug the fields’ merge values using Flow
Debugger.
B. Request the Einstein Generative AI Audit Data from the Security section of the Setup menu.
C. Enable the collection and storage of Einstein Generative AI Audit Data on the Einstein Feedback
setup page.
Answer: B
Explanation:
To begin validating that the correct fields are being masked in Einstein Trust Layer, the AI Specialist
should request the Einstein Generative AI Audit Data from the Security section of the Salesforce
Setup menu. This audit data allows the AI Specialist to see how data is being processed, including
which fields are being masked, providing transparency and validation that the configuration is
working as expected. Option B is correct because it allows for the retrieval of audit data that can be used to validate data
masking.
Option A (Flow Debugger) and Option C (Einstein Feedback) do not relate to validating field masking
in the context of the Einstein Trust Layer.
Reference:
Salesforce Einstein Trust Layer Documentation:
https://help.salesforce.com/s/articleView?id=sf.einstein_trust_layer_audit.htm
The AI Specialist of Northern Trail Outfitters reviewed the organization’s data masking
settings within the Configure Data Masking menu within Setup. Upon assessing all of the fields, a few
additional fields were deemed sensitive and have been masked within Einstein’s Trust Layer.
Which steps should the AI Specialist take upon modifying the masked fields?
A. Turn off the Einstein Trust Layer and turn it on again.
B. Test and confirm that the responses generated from prompts that utilize the data and masked
data do not adversely affect the quality of the generated response
C. Turn on Einstein Feedback so that end users can report if there are any negative side effects on AI
features.
Answer: B
Explanation:
After modifying masked fields in Einstein’s Trust Layer, the next important step is to test and confirm
that the responses generated by prompts utilizing the newly masked data still meet quality
standards. This ensures that masking sensitive information does not negatively impact the usefulness
or accuracy of the AI-generated content. Thorough testing helps identify any issues in prompt
performance that could arise due to masking, and adjustments can be made if needed.
Option B is correct because testing the effects of masking on AI responses is a critical step in ensuring
AI continues to function as expected.
Option A (turning off and on the Einstein Trust Layer) is unnecessary after changing the masked
fields.
Option C (turning on Einstein Feedback) allows for user feedback but is not a direct step following
field masking modifications.
Reference:
Salesforce Einstein Trust Layer Overview:
https://help.salesforce.com/s/articleView?id=sf.einstein_trust_layer.htm
