Salesforce-AI-Specialist Questions and Answers

The primary function of theplanner servicein theEinstein Copilotsystem is toidentify copilot actionsthat 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 theEinstein Copilotshould perform based on user input.

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 can achieve the request to explore similar opportunities by using thestandard Copilot action.Einstein Copilothas 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 toEinstein Copilot for Sales documentationregarding standard actions and natural language processing.

An AI Specialist wants to ground a new prompt template with theUser related list. Grounding in prompt templates involves using data from related lists to provide context or additional information to the Large Language Model (LLM) when generating responses.

Key Consideration:

Unsupported Related Lists in Prompt Templates:

Limitation:TheUser related listisnot supportedin prompt templates for grounding purposes.

Reason:Salesforce restricts certain objects and related lists from being used in prompt templates to maintain data security and integrity. The User object often contains sensitive information and is subject to strict access controls.

Impact:Attempting to use the User related list in a prompt template will not work as expected because the system does not support it.

Why Options A and B are Incorrect:

Option A (The User related list should have View All access):

Incorrect:Even with View All access, the User related list is still not supported in prompt templates.

Security Concerns:Granting View All access to the User object is a significant security risk and not a recommended practice.

Option B (The User related list needs to be included on the record page):

Incorrect:Including the User related list on the record page does not affect its availability in prompt templates.

Irrelevance:The placement of the related list on the record page does not change the system's ability to access it in a prompt template.

References:

Salesforce AI Specialist Documentation -Prompt Templates Limitations:

Details the objects and related lists that are not supported in prompt templates.

Salesforce Help -Data Access in Prompt Templates:

Explains how data access and security considerations affect the use of objects in prompt templates.

Salesforce Trailhead -Understanding Prompt Template Grounding:

Provides insights into grounding prompt templates and the limitations involved.

Conclusion:

Since the User related list is not supported in prompt templates, the AI Specialist must consider alternative approaches. They might need to redesign the prompt template to use supported objects or related lists, or explore other methods to incorporate necessary user information while adhering to Salesforce's data access policies.

To gather the necessary data for populating theLatest Opportunities Summarycustom field on theAccountobject with information from the three most recently opened opportunities, the AI Specialist shouldcreate a flow. Aflowcan be configured to query and retrieve the required opportunity records based on criteria such as their open date. Once the flow has gathered the necessary data, it can be used in aprompt templateor other automation processes to populate the custom field on the Account record.

Option Ais correct because creating a flow allows for dynamic data retrieval and control over the logic for selecting the most recent opportunities.

Option BandOption Cdo not provide sufficient control or data retrieval capabilities needed for this scenario.

References:

Salesforce Flow Documentation:https://help.salesforce.com/s/articleView?id=sf.flow.htm

In this scenario, theEinstein Copilotcapability that best helps the agent is its ability toexecute tasks based on available actionsandanswer questionsusing 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:

Brefers to invoking a flow to create a Knowledge article, which is unrelated to the task of retrieving existing Knowledge articles.

Cfocuses on generating Knowledge articles, which is not the immediate need for this situation where the agent requires guidance on existing procedures.

References:

Salesforce Documentation onEinstein Copilot

Trailhead Module onEinstein for Service

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 aFlex 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.

References:

Salesforce AI Specialist Documentation - Flex Templates:Explains how Flex templates can be utilized to incorporate data from multiple sources, providing a flexible solution for complex data requirements in prompt templates.

Customer churn predictionis the best use case forModel Builderin addressingUniversal Containers'concerns about increasing customer contract cancellations. By implementing a model that predicts customer churn,UCcan 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 Bis correct becausecustomer churn predictionaligns withUC'sneed 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.

References:

Salesforce Model Builder Use Case Overview:https://help.salesforce.com/s/articleView?id=sf.model_builder_use_cases.htm

Bring Your Own Large Language Model (BYO-LLM)functionality inEinstein Studioallows organizations to integrate and use external large language models (LLMs) within the Salesforce ecosystem.Universal Containerscan 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 Bis the correct choice asEinstein Studioprovides a built-in feature to work with external models.

Option Asuggests using Apex, butBYO-LLMfunctionality offers a more streamlined solution.

Option Cfocuses onFlow 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

Universal Containers (UC)has concerns about data privacy when usingSalesforce's generative AIfeatures, particularly around preventing third-party LLMs from accessing or retaining their data. TheZero-Data Retention Policyin theEinstein Trust Layeris designed to address these concerns by ensuring that:

No data is used for trainingor product improvements by third-party LLMs.

No data is retainedoutside 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 DefenseandData Maskingare 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

When acustomer chat is initiated,Einstein Service Repliesprovidesgenerative AI replies or draft emailsbased on recommendedKnowledge articles. This feature uses the information from theSalesforce Knowledge baseto generate responses that are relevant to the customer's query, improving the efficiency and accuracy of customer support interactions.

Option Bis correct becauseEinstein Service Repliesis responsible for generating AI-driven responses based on knowledge articles.

Option A(Einstein Reply Recommendations) is focused on recommending replies but does not generate them.

Option C(Einstein Grounding) refers to grounding responses in data but is not directly related to drafting replies.

References:

Einstein Service Replies Overview:https://help.salesforce.com/s/articleView?id=sf.einstein_service_replies.htm

ForUniversal Containers (UC)to refine itsGenerative AIprompt design strategy and improve the accuracy of the generated summaries for the custom objectGuest, the best practice is to focus on craftingconcise, clear, and consistent prompt templates. This includes:

Effective grounding: Ensuring the prompt pulls data from the correct sources.

Contextual role-playing: Providing the AI with a clear understanding of its role in generating the summary.

Clear instructions: Giving unambiguous directions on what to include in the response.

Iterative feedback: Regularly testing and adjusting prompts based on user feedback.

Option Bis correct because it follows industry best practices for refining prompt design.

Option A(prompt test mode) is useful but less relevant for refining prompt design itself.

Option C(prompt review case with Salesforce) would be more appropriate for technical issues or complex prompt errors, not general design refinement.

References:

Salesforce Prompt Design Best Practices:https://help.salesforce.com/s/articleView?id=sf.prompt_design_best_practices.htm

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 draftsand tone adjustments.

The"Create Close Plan"feature is designed to help sales reps by providingAI-generated strategies and stepsspecifically 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 movedeals 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 References:For more information on using AI for sales, visit:https://help.salesforce.com/s/articleView?id=sf.einstein_for_sales_overview.htm

For Northern Trail Outfitters (NTO) to configure theEinstein Trust Layer, theEinstein Generative AIfeature must be enabled. The Einstein Trust Layer is closely tied to generative AI capabilities, ensuring that AI-generated content complies with data privacy, security, and trust standards.

Option A(Turning on Einstein Copilot) is unrelated to the setup of the Einstein Trust Layer, which focuses more on generative AI interactions and data handling.

Option C(Turning on Prompt Builder) is used for configuring and building AI-driven prompts, but it does not enable the Einstein Trust Layer.

Salesforce AI Specialist References:For more details on the Einstein Trust Layer and setup steps:https://help.salesforce.com/s/articleView?id=sf.einstein_trust_layer_overview.htm

The reason behind the token limit errors lies in the dynamic nature of the prompt template used in Field Generation. In Salesforce's AI generative models, each prompt and its corresponding output are subject to a token limit, which encompasses both the input and output of the large language model (LLM). Since the prompt template dynamically adjusts based on the specific data of each record, the number of tokens varies per record. Some records may generate longer outputs based on their data attributes, pushing the token count beyond the allowable limit for the LLM, resulting in token limit errors.

This behavior explains why users experience random failures—it is dependent on the specific data used in each case. For certain records, the combined input and output may fall within the token limit, while for others, it may exceed it. This variation is intrinsic to how dynamic templates interact with large language models.

Salesforce provides guidance in their documentation, stating that prompt template design should take into account token limits and suggests testing with varied records to avoid such random errors. It does not mention switching to Flex template type as a solution, nor does it suggest that token limits fluctuate with user demand. Token limits are a constant defined by the model itself, independent of external user load.

References:

Salesforce Developer Documentation onToken Limits for Generative AI Models

Salesforce AI Best Practices on Prompt Design (Trailhead or Salesforce blog resources)

Einstein Copilot is designed to enhance user interaction within Salesforce by leveraging Large Language Models (LLMs) to process and respond to user inquiries. When a user submits a request, Einstein Copilot analyzes the input using natural language processing techniques. It then utilizes LLM technology to generate an appropriate and contextually relevant response, which is displayed directly to the user within the Salesforce interface.

OptionCaccurately describes this process. Einstein Copilot does not necessarily trigger a flow (Option A) or perform an HTTP callout to an LLM provider (Option B) for each user request. Instead, it integrates LLM capabilities to provide immediate and intelligent responses, facilitating a broad range of user requests.

References:

Salesforce AI Specialist Documentation - Einstein Copilot Overview:Details how Einstein Copilot employs LLMs to interpret user inputs and generate responses within the Salesforce ecosystem.

Salesforce Help - How Einstein Copilot Works:Explains the underlying mechanisms of how Einstein Copilot processes user requests using AI technologies.

To customize thecase summary responsefromEinstein Copilot, the AI Specialist should create acustom Record Summary prompt templatefor theCase object. This allows Universal Containers to tailor the way case data is summarized, ensuring the output aligns with specific business requirements or user preferences.

Option A(customizing the standard Record Summary template) does not provide the flexibility required for deep customization.

Option B(standard Copilot action) won’t allow customization; it will only use default settings.

Refer toSalesforce Prompt Builder documentationfor guidance on creating custom templates for record summaries.

Salesforce Einstein Copilotis 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 anAI-powered assistantthat 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 Ais correct becauseEinstein Copilotbrings a conversational interface that caters to a wide range of users.

Option BandOption Care more focused on developing and training AI models, which are not the primary functions ofEinstein Copilot.

References:

Salesforce Einstein Copilot Overview:https://help.salesforce.com/s/articleView?id=einstein_copilot_overview.htm

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.

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.

ForEinstein Sales Emailsto 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 isGround 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 Fieldsensure the generated emails are highly personalized using data like lead name, company, or other Salesforce fields directly from the records.

Apex Merge Fieldsare typically more suited for advanced, custom logic-driven scenarios but are not the most straightforward for this use case.

Automatic grounding using Draft with Einsteinis a different feature where Einstein automatically drafts the email, but it does not specifically ground the content with record-specific data likeRecord Merge Fields.

References:

Salesforce Einstein Sales Emails Documentation:https://help.salesforce.com/s/articleView?id=release-notes.rn_einstein_sales_emails.htm