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?
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 offering
actionable 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
How does the Einstein Trust Layer ensure that sensitive data is protected while generating useful and
meaningful responses?
A
Explanation:
The Einstein Trust Layer ensures that sensitive data is protected while generating useful and
meaningful responses by masking sensitive data before it is sent to the Large Language Model (LLM)
and then de-masking it during the response journey.
How It Works:
Data Masking in the Request Journey:
Sensitive Data Identification: Before sending the prompt to the LLM, the Einstein Trust Layer scans
the input for sensitive data, such as personally identifiable information (PII), confidential business
information, or any other data deemed sensitive.
Masking Sensitive Data: Identified sensitive data is replaced with placeholders or masks. This ensures
that the LLM does not receive any raw sensitive information, thereby protecting it from potential
exposure.
Processing by the LLM:
Masked Input: The LLM processes the masked prompt and generates a response based on the
masked data.
No Exposure of Sensitive Data: Since the LLM never receives the actual sensitive data, there is no risk
of it inadvertently including that data in its output.
De-masking in the Response Journey:
Re-insertion of Sensitive Data: After the LLM generates a response, the Einstein Trust Layer replaces
the placeholders in the response with the original sensitive data.
Providing Meaningful Responses: This de-masking process ensures that the final response is both
meaningful and complete, including the necessary sensitive information where appropriate.
Maintaining Data Security: At no point is the sensitive data exposed to the LLM or any unintended
recipients, maintaining data security and compliance.
Why Option A is Correct:
De-masking During Response Journey: The de-masking process occurs after the LLM has generated
its response, ensuring that sensitive data is only reintroduced into the output at the final stage,
securely and appropriately.
Balancing Security and Utility: This approach allows the system to generate useful and meaningful
responses that include necessary sensitive information without compromising data security.
Why Options B and C are Incorrect:
Option B (Masked data will be de-masked during request journey):
Incorrect Process: De-masking during the request journey would expose sensitive data before it
reaches the LLM, defeating the purpose of masking and compromising data security.
Option C (Responses that do not meet the relevance threshold will be automatically rejected):
Irrelevant to Data Protection: While the Einstein Trust Layer does enforce relevance thresholds to
filter out inappropriate or irrelevant responses, this mechanism does not directly relate to the
protection of sensitive data. It addresses response quality rather than data security.
Reference:
Salesforce AI Specialist Documentation -
Einstein Trust Layer Overview
:
Explains how the Trust Layer masks sensitive data in prompts and re-inserts it after LLM processing to
protect data privacy.
Salesforce Help -
Data Masking and De-masking Process
:
Details the masking of sensitive data before sending to the LLM and the de-masking process during
the response journey.
Salesforce AI Specialist Exam Guide -
Security and Compliance in AI
:
Outlines the importance of data protection mechanisms like the Einstein Trust Layer in AI
implementations.
Conclusion:
The Einstein Trust Layer ensures sensitive data is protected by masking it before sending any prompts
to the LLM and then de-masking it during the response journey. This process allows Salesforce to
generate useful and meaningful responses that include necessary sensitive information without
exposing that data during the AI processing, thereby maintaining data security and compliance.
Universal Containers (UC) wants to enable its sales team to get insights into product and competitor
names mentioned during calls.
How should UC meet this requirement?
C
Explanation:
To provide the sales team with insights into product and competitor names mentioned during calls,
Universal Containers should:
Enable Einstein Conversation Insights: Activates the feature that analyzes call recordings for valuable
insights.
Enable Sales Recording: Allows calls to be recorded within Salesforce without needing an external
recording provider.
Assign Permission Sets: Grants the necessary permissions to sales team members to access and
utilize conversation insights.
Customize Insights: Configure the system to track mentions of up to 50 products and 50 competitors,
providing tailored insights relevant to the organization's needs.
Option C accurately reflects these steps. Option A mentions defining recording managers but omits
enabling sales recording within Salesforce. Option B suggests connecting a recording provider and
limits customization to 25 products, which does not fully meet UC's requirements.
Reference:
Salesforce AI Specialist Documentation - Setting Up Einstein Conversation Insights: Provides
instructions on enabling conversation insights and sales recording.
Salesforce Help - Customizing Conversation Insights: Details how to customize insights with up to 50
products and competitors.
Salesforce AI Specialist Exam Guide: Outlines best practices for implementing AI features like Einstein
Conversation Insights in a sales context.
=========================
What is the role of the large language model (LLM) in executing an Einstein Copilot Action?
B
Explanation:
In Einstein Copilot, the role of the Large Language Model (LLM) is to analyze user inputs and identify
the best matching actions that need to be executed. It uses natural language understanding to break
down the user’s request and determine the correct sequence of actions that should be performed.
By doing so, the LLM ensures that the tasks and actions executed are contextually relevant and are
performed in the proper order. This process provides a seamless, AI-enhanced experience for users
by matching their requests to predefined Salesforce actions or flows.
The other options are incorrect because:
A mentions finding similar requests, which is not the primary role of the LLM in this context.
C focuses on access and sorting by priority, which is handled more by security models and
governance than by the LLM.
Reference:
Salesforce Einstein Documentation on Einstein Copilot Actions
Salesforce AI Documentation on Large Language Models
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?
C
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
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
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
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?
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
Universal Containers implemented Einstein Copilot for its users.
One user complains that Einstein Copilot is not deleting activities from the past 7 days.
What is the reason for this issue?
C
Explanation:
Einstein Copilot currently supports various actions like creating and updating records but does not
support the Delete Record action. Therefore, the user's request to delete activities from the past 7
days cannot be fulfilled using Einstein Copilot.
Unsupported Action: The inability to delete records is due to the current limitations of Einstein
Copilot's supported actions. It is designed to assist with tasks like data retrieval, creation, and
updates, but for security and data integrity reasons, it does not facilitate the deletion of records.
User Permissions: Even if the user has the necessary permissions to delete records within Salesforce,
Einstein Copilot itself does not have the capability to execute delete operations.
Reference:
Salesforce AI Specialist Documentation -
Einstein Copilot Supported Actions
:
Lists the actions that Einstein Copilot can perform, noting the absence of delete operations.
Salesforce Help -
Limitations of Einstein Copilot
:
Highlights current limitations, including unsupported actions like deleting records.
Where should the AI Specialist go to add/update actions assigned to a copilot?
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 wants to reduce overall agent handling time minimizing the time spent typing
routine answers for common questions in-chat, and reducing the post-chat analysis by suggesting
values for case fields.
Which combination of Einstein for Service features enables this effort?
C
Explanation:
Universal Containers aims to reduce overall agent handling time by minimizing the time agents
spend typing routine answers for common questions during chats and by reducing post-chat analysis
through suggesting values for case fields.
To achieve these objectives, the combination of Einstein Reply Recommendations and Case
Classification is the most appropriate solution.
1. Einstein Reply Recommendations:
Purpose: Helps agents respond faster during live chats by suggesting the best responses based on
historical chat data and common customer inquiries.
Functionality:
Real-Time Suggestions: Provides agents with a list of recommended replies during a chat session,
allowing them to quickly select the most appropriate response without typing it out manually.
Customization: Administrators can configure and train the model to ensure the recommendations are
relevant and accurate.
Benefit: Significantly reduces the time agents spend typing routine answers, thus improving
efficiency and reducing handling time.
2. Case Classification:
Purpose: Automatically suggests or populates values for case fields based on historical data and
patterns identified by AI.
Functionality:
Field Predictions: Predicts values for picklist fields, checkbox fields, and more when a new case is
created.
Automation: Can be set to auto-populate fields or provide suggestions for agents to approve.
Benefit: Reduces the time agents spend on post-chat analysis and data entry by automating the
classification and field population process.
Why Options A and B are Less Suitable:
Option A (Einstein Service Replies and Work Summaries):
Einstein Service Replies: Similar to Reply Recommendations but typically used for email and not live
chat.
Work Summaries: Provides summaries of customer interactions but does not assist in field value
suggestions.
Option B (Einstein Reply Recommendations and Case Summaries):
Case Summaries: Generates a summary of the case details but does not help in suggesting field
values.
Reference:
Salesforce AI Specialist Documentation -
Einstein Reply Recommendations
:
Details how Reply Recommendations assist agents in providing quick responses during live chats.
Salesforce AI Specialist Documentation -
Einstein Case Classification
:
Explains how Case Classification predicts and suggests field values to streamline case management.
Salesforce Trailhead -
Optimize Service with AI
:
Provides an overview of AI features that enhance service efficiency.