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?
Correct Answer:B
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.
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?
Correct Answer:B
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):
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.
What is the role of the large language model (LLM) in executing an Einstein Copilot Action?
Correct Answer:B
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.
References:
Salesforce Einstein Documentation on Einstein Copilot Actions Salesforce AI Documentation on Large Language Models
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 beapplicable for Einstein AI todraft accurate email responses.
Which solution should UC use to ensure Einstein AI can draft responsesfrom a defined data source?
Correct Answer:A
Service AI Groundingis the solution thatUniversal Containersshould use to ensureEinstein AIdrafts 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 asKnowledge articlesorcases.
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 SummariesandService Repliesare useful features but do not address the need for grounding AI outputs in specific, current data sources likeService AI Groundingdoes.
For more details, you can refer to Salesforce??sService AI Grounding documentationfor
managing AI-generated content based on accurate data sources.
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?
Correct Answer:A
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.
References:
✑ Salesforce Einstein Copilot Overview:https://help.salesforce.com/s/articleView?id=einstein_copilot_overview.ht m