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
Explanation: WhenUniversal Containersuses theDraft with Einsteinfeature inSales
Cloudto create a personalized email, the predefined adjustment toMake Less Formalis
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 Friendlinesswould make the tone more positive, but not necessarily more
casual.
Optimize for Clarityfocuses on making the draft clearer but doesn't adjust the tone.
For more details, seeSalesforce documentation on Einstein-generated email drafts
and tone adjustments.
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.
Explanation: When buildingprompt templates in Prompt Builder, it is essential to
consider how the Large Language Model (LLM) processes and generates outputs. Training
the LLM with variouswriting styles, such as differentword 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’sPrompt Builder documentationand LLM tuning
strategies.
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.
Explanation: WhenUniversal Containerscreates 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 shouldmanually add the necessary hyperparametersto the new
template.
Hyperparameters likeTemperature,Frequency Penalty, andPresence
Penaltydirectly 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 toPrompt Builder documentationon configuring
hyperparameters in custom templates.
When configuring a prompt template, an AI Specialist previews the results of the prompt
template they've written. They see two distinct text outputs: Resolution and Response.
Which information does the Resolution text provide?
A. It shows the full text that is sent to the Trust Layer.
B. It shows the response from the LLM based on the sample record.
C. It shows which sensitive data is masked before it is sent to the LLM.
Explanation: When previewing aprompt templatein Salesforce, theResolutiontext
provides theresponse from the LLM(Large Language Model) based on the data from a
sample record. This output shows what the AI model generated in response to the prompt,
giving the AI Specialist a chance to review and adjust the response before finalizing the
template.
Option Bis correct becauseResolutiondisplays the actual response generated by
the LLM.
Option Arefers to sending the text to theTrust Layer, but that’s not
whatResolutionrepresents.
Option Crelates to data masking, which is shown elsewhere, not underResolution.
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
Explanation: ForUniversal Containers (UC)to offer personalized service experiences and
reduce agent handling time using AI-generated responses grounded in theKnowledge
base, the best solution isEinstein Service Replies for Email. This capability leverages AI
to automatically generate responses to service-related emails based on historical data and
theKnowledge 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 Emailis the correct choice for grounded,
knowledge-based responses.
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.
Explanation: Since security and data privacy are critical, the best option for the AI Specialist is to integrate the fine-tunedLLM (Large Language Model)into Salesforce by
adding it toEinstein Studio Model Builder.Einstein Studioallows 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 Studioprovides the most secure and seamless way to integrate custom AI
models while maintaining control over data privacy and compliance. More details can be
found inSalesforce's Einstein Studio documentationon integrating external models.
The marketing team at Universal Containers is looking for a way personalize emails based
on customer behavior, preferences, and purchase history.
Why should the team use Einstein Copilot as the solution?
A. To generate relevant content when engaging with each customer
B. To analyze past campaign performance
C. To send automated emails to all customers
Explanation: Einstein Copilotis designed to assist in generating personalized, AI-driven
content based on customer data such as behavior, preferences, and purchase history. For
the marketing team atUniversal Containers, this is the perfect solution to create dynamic
and relevant email content. By leveragingEinstein Copilot, they can ensure that each
customer receives tailored communications, improving engagement and conversion rates.
Option Ais correct asEinstein Copilothelps generate real-time, personalized
content based on comprehensive data about the customer.
Option Brefers more to Einstein Analytics or Marketing Cloud Intelligence,
andOption Cdeals with automation, which isn't the primary focus ofEinstein
Copilot.
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.
Explanation: For improvingsales operations efficiency,Einstein Studiois ideal for
creating AI-powered models that can predict outcomes based on data. One of the most
valuable use cases is predictingcustomer 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 Bis the correct choice as predicting customer lifetime value is a wellestablished
use case for AI in sales.
Option A(customer sentiment) is typically handled through NLP models,
whileOption C(product popularity) is more of a marketing analysis use case.
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
Explanation: For analyzing voice and video call records to gain insights into competitor
mentions, coaching opportunities, and other key information,Call Exploreris the most
suitable feature.Call Explorer, a part ofEinstein 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, includingcompetitor
mentionsand moments for coaching. These insights are vital for improving sales
performance by providing a clear understanding of the interactions during calls.
Call Summariesoffer a quick overview of a call but do not delve deep into
competitor mentions or coaching insights.
Einstein Sales Insightsfocuses more on pipeline and forecasting insights rather
than call-based analysis.
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
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:
Knowledge Object:
Exclusion of Other Objects:
Why Options A and C are Incorrect:
Option A (Case, Knowledge, and Case Notes):
Option C (Case, Case Emails, and Knowledge):
An AI Specialist is creating a custom action in Einstein Copilot.
Which option is available for the AI Specialist to choose for the custom copilot action?
A. Apex trigger
B. SOQL
C. Flows
Explanation: When creating acustom actionin Einstein Copilot, one of the available
options is to useFlows. Flows are a powerful automation tool in Salesforce, allowing the AI
Specialist to define custom logic and actions within the Copilot system. This makes it easy
to extend Copilot's functionality without needing custom code.
WhileApex triggersandSOQLare important Salesforce tools,Flowsare the recommended
method for creating custom actions within Einstein Copilot because they are declarative
and highly adaptable.
For further guidance, refer toSalesforce Flow documentationandEinstein Copilot
customization resources.
Universal Containers implements Custom Copilot Actions to enhance its customer service
operations. The development team needs to understand the core components of a Custom
Copilot Action to ensure proper configuration and functionality.
What should the development team review in the Custom Copilot Action configuration to
identify one of the core components of a Custom Copilot Action?
A. Instructions
B. Output Types
C. Action Triggers
Explanation:
Universal Containers is enhancing its customer service operations with Custom Copilot
Actions. The development team needs to understand the core components of a Custom
Copilot Action to ensure proper configuration and functionality. One of these core
components is the Output Types.
Core Components of a Custom Copilot Action:
Focus on Output Types:
Why Output Types are a Core Component:
Integration with Copilot:
Data Consistency:
User Experience:
Why Other Options are Less Suitable:
Option A (Instructions):
Option C (Action Triggers):
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