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

When generating a Work Summary, Einstein leverages multiple sources of information to provide a comprehensive and accurate case summary for agents.

Conversation Context:

Einstein analyzes the details of the customer interaction, including chat or email threads, to extract relevant information for the summary.

Knowledge Articles:

It considers linked Knowledge Articles or articles referred to during the case resolution process, ensuring the summary incorporates accurate resolutions or additional resources provided to the customer.

Cases:

Einstein also examines historical cases and related case records to ground the summary in context from past resolutions or interactions.

Option Ais correct as it includes all three: conversation context, Knowledge articles, and cases.

Option Bis incorrect because it limits the grounding to conversation context only, excluding other critical elements.

Option Cis incorrect because it omits case data, which Einstein considers for more accurate and contextually rich summaries.

Context of the Question

Universal Containers (UC) has a strict change management process that requires all possible configuration be completed in a sandbox and deployed to Production.

Einstein Generative AI is already enabled in Production, and the “Einstein Work Summaries” permission set is already available in Production.

The AI Specialist needs to configureWork Summaries for Enhanced Messagingin the sandbox.

What Can Actually Be Deployed from Sandbox to Production?

Custom Fields: Metadata that is easily created in sandbox and then deployed.

Quick Actions: Also metadata-based and can be deployed from sandbox to production.

Layout Components: Page layout changes (such as adding the Wrap Up component) can be added to a change set or deployment package.

Why Option C is Correct

No Need to Turn on Einstein in Sandbox for Deployment: Einstein Generative AI is already enabled in Production; turning it on in the sandbox is typically a manual step if you want to test, but that step itself is not “deployable” in the sense of metadata.

Permission Set Assignments(as in Option A) are not deployable metadata. You can deploy the Permission Set itself but not the specific user assignments. Since the question specifically asks “Which other configuration steps should be takenin the sandboxthatcanbe deployed to the production org?”, user assignment is not one of them.

Why Not Option A or B?

Option A: Mentions creating permission set assignments for agents. This cannot be directly deployed from sandbox to Production, as permission set assignments are user-specific and considered “data,” not metadata.

Option B: Mentions “Turn on Einstein.” But Einstein Generative AI is already enabled in Production. Additionally, “Turning on Einstein” is typically an org-level setting, not a deployable metadata item.

ConclusionThe main deployable items you can reliably create and test in a sandbox, and then migrate to Production, are:

Custom Fields(Issue, Resolution, Summary).

A Quick Actionthat updates those fields.

Page Layout Changeto include the Wrap Up component.

Therefore,Option Cis correct and focuses on actions that are truly deployable as metadata from a sandbox to Production.

Salesforce AI Specialist References & Documents

Salesforce Trailhead:Work Summaries with Einstein GPTProvides an overview of how to configure Work Summaries, including the need for custom fields, quick actions, and UI components.

Salesforce Documentation:Deploying Metadata Between OrgsExplains what can and cannot be deployed via change sets (e.g., custom fields, page layouts, quick actions vs. user permission set assignments).

Salesforce AI Specialist Study GuideOutlines which Einstein Generative AI and Work Summaries configurations are deployable as metadata.

Prompt Builderis designed to help organizations create and configure reusable prompts for large language models (LLMs). By integratinggenerative AIresponses into workflows,Prompt Builderenables customization of AI prompts that interact with Salesforce data and automate complex processes. This tool is especially useful for creating tailored and consistent AI-generated content in various business contexts, including customer service and sales.

It is not a tool forApex programming(as in option A).

It is also not limited to real-time suggestions as mentioned in option C. Instead, it provides a flexible way for companies to manage and customize how AI-driven responses are generated and used in their workflows.

References:

Salesforce Prompt Builder Overview:https://help.salesforce.com/s/articleView?id=sf.prompt_builder.htm

The correct answer isField Generationbecause this template type is designed to dynamically populate form fields with content generated by a large language model (LLM). In this scenario, leadership wants a dynamic form field that contains a summary or description generated by AI to aid customer interactions. Additionally, they want to keep a human in the loop, meaning the generated content will likely be reviewed or edited by a person before it's finalized, which aligns with theField Generationprompt template.

Field Generation: This prompt type allows you to generate content for specific fields in Salesforce, leveraging large language models to create dynamic and contextual information. It ensures that AI content is available within the record where needed, but it allows human oversight or review, supporting the "human-in-the-loop" strategy.

Sales Email: This prompt type is mainly used for generating email content for outreach or responses, which doesn't align directly with populating fields in a form.

Record Summary: While this option might seem close, it is typically used to summarize entire records for high-level insights rather than filling specific fields with dynamic content based on AI generation.

Salesforce AI Specialist References:

You can explore more about these prompt templates and AI capabilities through Salesforce documentation and official resources on Prompt Builder:https://help.salesforce.com/s/articleView?id=sf.prompt_builder_templates_overview.htm

When using related list merge fields in a prompt template associated with the Account object inPrompt Builder, theActivities related listis not supported due to it being apolymorphic field. Polymorphic fields can reference multiple different types of objects, which makes them incompatible with some merge field operations in prompt generation.

Option Bis incorrect because person accounts do not limit the availability of merge fields for the Account object.

Option Cis 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 toSalesforce documentationon supported fields and limitations inPrompt Builder.

Context of the QuestionUniversal Containers (UC) wants to automatically populate the Description field on the Account object. The AI-driven solution must generate textual data and write it directly into a field.

Field Generation Prompt Template

Primary Use Case: A Field Generation prompt template is specifically designed to create or fill in fields on a record with AI-generated text.

Auto-population: By configuring a Field Generation prompt template, admins can define the instructions, data inputs, and desired output for the AI. The resulting text then populates the specified field, such as the Account Description.

Why Not Flex or Sales Email Prompt Templates?

Flex Prompt Template: Used to combine or manipulate data across objects, merges, or references from multiple sources in more advanced, flexible prompts. Typically not the go-to for straightforward text generation on a single field.

Sales Email Prompt Template: Focused on drafting or summarizing emails for sales reps (like crafting outreach or follow-up messages). This template is not specifically built to populate a field on a record.

ConclusionFor automatically populating the Description field with AI-generated content, theField Generation prompt template(Option A) is the correct choice.

Salesforce AI Specialist References & Documents

Salesforce Documentation:Prompt Template TypesExplains various template types (Field Generation, Flex, Email, etc.) and their typical use cases.

Salesforce AI Specialist Study GuideHighlights Field Generation prompt templates for populating or updating record fields with AI-generated text.

Thestandard Einstein Copilot actionthat would be used in response to the user utterance, “Show me all the customers in New York,” isQuery Records. This action is responsible for retrieving a set of records from Salesforce based on a specified condition — in this case, filtering customers by location (New York).

Query Recordsis the action that fetches relevant data based on the criteria provided in the user's input.

Select Recordsis more about picking specific records from an already presented list.

Fetch Recordsis not a standard term used in this context for the action.

Refer toEinstein Copilot documentationon how Copilot actions work with natural language queries and data retrieval.

Einstein Studio (Model Builder) allows organizations to connect and utilize external foundational models while fine-tuning them with company-specific data. This capability is particularly suited to businesses like Universal Containers (UC) that require customization of foundational models to better align with their unique data and use cases.

Option A: Adjusting model temperature is a parameter-level setting for controlling randomness in AI-generated responses but does not necessitate connecting to an external foundational model.

Option B: This is the correct answer because Einstein Studio supports fine-tuning external models with proprietary company data, enabling a tailored and more accurate AI solution for UC.

Option C: Changing frequency penalties is another parameter-level adjustment and does not require external foundational models or Einstein Studio.

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.

To enableUniversal Containersservice agents to query the current fulfillment status of an order using natural language and leverage an existing auto-launched flow that queries Oracle ERP, the best solution is tocreate a custom copilot action that calls the flow. This action will allowEinstein Copilotto interact with the flow and retrieve the required order fulfillment information seamlessly. Custom copilot actions can be tailored to call various backend systems or flows in response to user requests.

Option Bis correct because it enables integration betweenEinstein Copilotand the flow that connects to Oracle ERP.

Option A(Flex prompt template) is more suited for static responses and not for invoking flows.

Option C(Integration Flow Standard Action) is not directly related to creating a specific copilot action for this use case.

References:

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

To use the Record Snapshots grounding feature in a prompt template, you must create a field set that includes all fields required for grounding. Field sets define which fields from an object are accessible to the AI model, ensuring the prompt template has structured data to generate contextually accurate responses. Salesforce documentation emphasizes that grounding relies on explicitly defined field sets to avoid exposing unintended data and to comply with security policies.

Page Layout configuration (A) controls UI organization but does not directly enable grounding.

Dynamic Forms (C) customize record pages dynamically but are unrelated to data grounding for prompts.

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)

Context of the QuestionUniversal Containers (UC) uses Agentforce, a specialized AI-driven assistant for Salesforce. A user reports that an Agent is unable to delete recent activities.

Why Agentforce Cannot Delete Records

Agentforce’s Standard Actions: Agentforce typically has predefined or “standard” actions like Create, Update, or Summarize records. However, a standard Delete Record action is not part of the default set of Agentforce actions.

Implication: If Agentforce has no built-in delete functionality, it cannot remove activities—even if the user has permission to delete them in the Salesforce UI.

Why Other Options Are Incorrect

Option A – Permission to Delete the User’s Records: Standard Salesforce user permissions do not automatically extend to Agentforce’s capabilities. Even if the user can delete records, that doesn’t grant Agentforce a new action.

Option B – Agentforce Delete Record Action Permission: There is no separate “Delete Record Action permission” for Agentforce to be toggled. The relevant issue is that the standard Delete Record action does not exist within Agentforce out of the box.

ConclusionThe core reason for the issue is thatAgentforce does not support a standard Delete Record action(Choice C).

Salesforce AI Specialist References & Documents

Salesforce Official Documentation – Agentforce(Note: Agentforce may be a pilot or specialized feature; check pilot release notes or official docs for standard actions.)

Salesforce AI Specialist Study GuideCovers the limitations of certain AI-enabled features regarding record operations.

Service Replies: This generative AI feature automates and assists in generating accurate, contextual, and efficient replies for customer service agents. It uses past interactions, case data, and the context of the conversation to provide draft responses, thereby enhancing productivity and reducing response times.

Case Summaries: Summarizes case information but does not assist directly in replying to customer chats.

Case Escalation: Refers to moving cases to higher-level support teams but does not address the need to improve chat response productivity.

Thus,Service Repliesis the best feature for this requirement as it directly aligns with improving agent efficiency in replying to chats.

Context of the Question

Universal Container (UC) has used prompt templates to update summary fields on record pages.

Now, the admin wants to incorporate similar generative AI functionality within a Flow for automation purposes.

How to Call a Prompt Template Within a Flow

Flow Action: Salesforce provides a standard way to invoke generative AI templates or prompts within a Flow step. From the Flow Builder, you can add an “Action” that references the prompt template you created in Prompt Builder.

Other Options:

Invocable Apex: Possible fallback if there’s no out-of-the-box Flow Action available. However, Salesforce is releasing native Flow integration for AI prompts, making custom Apex less necessary.

Einstein for Flow: A broad label for Salesforce’s generative AI features within Flow. Under the hood, you typically use a “Flow Action” that points to your prompt.

Conclusion

The easiest out-of-the-box solution is to use aFlow Actionreferencing the prompt template. Hence,Option Bis correct.

Salesforce AI Specialist References & Documents

Salesforce Trailhead:Use Prompt Templates in FlowDemonstrates how to add an Action in Flow that calls a prompt template.

Salesforce Documentation:Einstein GPT for FlowExplains standard flow actions to invoke and handle generative AI responses.

Einstein Copilot currently supports various actions like creating and updating records but does not support theDelete Recordaction. 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.

References:

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.

When deploying Einstein Agent (formerly Agentforce) from Sales to Service Cloud:

Agent Topics and Actions are context-specific. Service Cloud use cases (e.g., case resolution, knowledge retrieval) require validation of existing topics/actions to ensure alignment with service workflows.

Option A: Permissions like "Agentforce for Service" are necessary but secondary to functional compatibility.

Option B: Standard service actions must be mapped to Agentforce, but testing ensures they function as intended.

References:

Salesforce Help: Einstein Agent Setup

Emphasizes reviewing "topics and actions for different user groups (Sales vs. Service)."

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.htm

To enable Universal Containers' sales team with automatic post-call visibility into mentions ofcompetitors, products, and custom phrases, the AI Specialist should set upCall Insights.Call Insightsanalyzes voice and video calls for key phrases, topics, and mentions, providing insights into critical aspects of the conversation. This feature automatically surfaces key details such as competitor mentions, product discussions, and custom phrases specified by the sales team.

Call Summariesprovide a general overview of the call but do not specifically highlight keywords or topics.

Call Exploreris a tool for navigating through call data but does not focus on automatic insights.

For more information, refer toSalesforce's Call Insights documentationregarding the analysis of call content and extracting actionable information.

In Salesforce, Generative AI capabilities are controlled by specific permission sets. To use features such as generating summaries with AI, users need to have the correct permission sets that allow access to these functionalities.

Generative AI User Permission Set: This is a key permission set required to enable the generative AI capabilities for a user. In this case, the missingGenerative AI Userpermission set prevents the user from seeing the generative AI-enabled field icon. Without this permission, the generative AI feature in the Competitor Analysis custom object won't be accessible.

Why not A?ThePrompt Template Userpermission set relates specifically to users who need access to prompt templates for interacting with Einstein GPT, but it's not directly related to the visibility of AI-enabled field icons.

Why not B?While a prompt template might need to be activated, this is not the primary issue here. The question states that other users with the same profile can see the icon, so the problem is more likely to be permissions-based for this particular user.

For more detailed information, you can review Salesforce documentation onpermission setsrelated to AI capabilities atSalesforce AI DocumentationandEinstein GPTpermissioning guidelines.

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

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:

Bmisses the Copilot Action Library, which is crucial for managing actions.

Cincludes the Copilot Detail page, which isn't the primary place for action management.

References:

Salesforce Documentation onManaging Copilot Actions

Salesforce AI Specialist Guide onCopilot Action Management

Instructions:This is a core component of Custom Copilot Actions. Instructions tell the AI model what the action should do and how it should be executed. Clear and concise instructions are crucial for the action to function correctly and provide the expected outcome.

Let's look at why the other options are not the primary core component:

Output Types:While important for defining the kind of data the action produces, it's not the core defining element of the action itself.

Action Triggers:These determine when the action is initiated, but they don't define the core functionality of the action.

When usingEinstein Generative AIto create personalized emails, if placeholders appear in the draft email where data from a recipient'sContactrecord should be, the most likely reason is that theuser 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 Bis 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.

References:

Salesforce Email Template and Permissions Documentation:https://help.salesforce.com/s/articleView?id=sf.email_templates_field_permissions.htm

Dynamic Grounding in the Einstein Trust Layer architecture ensures that large language model (LLM) prompts are enriched with organization-specific data (e.g., Salesforce records, Knowledge articles) to generate accurate and relevant responses. By dynamically injecting contextual data into prompts, it reduces hallucinations and aligns outputs with trusted business data.

Prompt Defense (A) focuses on blocking malicious inputs or prompt injections but does not enhance responses with organizational data.

Data Masking (B) redacts sensitive information but does not contribute to grounding responses in business context.

For no-code implementations, Prompt Builder allows AI Specialists to create prompt templates that dynamically ground responses in Salesforce CRM data (e.g., past purchases) and Data Cloud insights (e.g., product interests) without custom coding. The standard foundation model (e.g., Einstein GPT) can synthesize this data into summaries, leveraging structured and unstructured sources (e.g., email/phone transcripts). Fine-tuning (B) or custom models (C) require code and are unnecessary here, as the use case does not involve unique data patterns requiring model retraining.

Universal Containers is considering the use of the Einstein Trust Layer along with Einstein Generative AI Audit Data. The Einstein Trust Layer provides a secure and compliant way to use AI by offering features like data masking and toxicity assessment.

The audit data available through the Einstein Trust Layer includes information aboutmasked data—which ensures sensitive information is not exposed—and thetoxicity score, which evaluates the generated content for inappropriate or harmful language.

References:

Salesforce AI Specialist Documentation - Einstein Trust Layer:Details the auditing capabilities, including logging of masked data and evaluation of generated responses for toxicity to maintain compliance and trust.

To begin validating that the correct fields are being masked inEinstein Trust Layer, the AI Specialist should request theEinstein Generative AI Audit Datafrom theSecurity sectionof 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 Bis correct because it allows for the retrieval of audit data that can be used to validate data masking.

Option A(Flow Debugger) andOption C(Einstein Feedback) do not relate to validating field masking in the context of theEinstein Trust Layer.

References:

Salesforce Einstein Trust Layer Documentation:https://help.salesforce.com/s/articleView?id=sf.einstein_trust_layer_audit.htm

Prompt Execute Template permission set:This permission set is specifically designed to allow users to execute existing prompt templates. This is exactly what Universal Containers needs for its sales reps.

Prompt Template user permission set:This permission set likely grants broader access, including potentially creating or modifying templates, which is not required in this scenario.

Prompt Template Manager permission set:This permission set likely grants even more extensive administrative access to prompt templates, going beyond what the sales reps need.

By granting sales reps the "Prompt Execute Template" permission set, you ensure they have the necessary access to use prompt templates without granting unnecessary permissions that could potentially lead to unintended changes or security risks.

In Salesforce, when creating a prompt template for the sales team, you can include data from related objects such as Opportunities that are linked to an Account. The best method to ground the AI model and provide relevant information from related records, like Opportunities, is by usingmerge fields.

Merge fields in Salesforce allow you to dynamically reference data from a record or related records, like Opportunities for a given Account. In this scenario, the AI Specialist needs to pull data from thedefault related list of Opportunitiesassociated with the Account. This is achieved by using merge fields, which pull in data from the standard relationship Salesforce creates between Accounts and Opportunities.

Option A (referencing a custom related list) and Option C (using formula fields with Einstein-related lists) do not align with the standard, practical grounding method for this task. Custom lists would require additional configurations not typically necessary for a basic use case, and formula fields are typically not used to directly fetch related list data for prompt generation in templates. The standard and straightforward method is using merge fields tied to the default related list of opportunities.

Salesforce References:

Merge Fields in Templates:https://help.salesforce.com/s/articleView?id=000387601 &type=1

Grounding Data in Prompts:https://developer.salesforce.com/docs/atlas.en-us.salesforce_ai.meta/salesforce_ai/grounding_data_prompts

Knowledge Replies is the Einstein Generative AI capability that generates recommended responses for service agents by grounding responses in the organization’s Knowledge articles. This reduces handling time by providing contextually accurate suggestions sourced directly from approved content. Salesforce documentation states that Knowledge Replies leverage natural language processing (NLP) to match customer inquiries with relevant articles and draft replies, ensuring consistency and compliance.

Case Replies (A) focus on generating responses based on case data (e.g., case fields, history) but do not explicitly ground responses in Knowledge articles.

Service Replies (C) is not a standard Einstein capability tied to Knowledge-driven responses.

Secure Data Retrieval enforces Salesforce’s security model by dynamically grounding data access in the permissions of the user executing the prompt. This ensures compliance with CRUD (Create, Read, Update, Delete) and FLS (Field-Level Security) settings, preventing unauthorized access to sensitive data. For example, if a user lacks access to a specific object or field, the AI model cannot retrieve it for dynamic grounding.

"Run As" user permissions (A) would bypass user-specific security, posing a compliance risk.

Prompt template permissions (C) are not a Salesforce security mechanism; access is always tied to the user’s profile and sharing settings.

To allow a data scientist to view and manage models inEinstein Studioand create prompt templates inPrompt Builder, the AI Specialist should assign theData Cloud AdminandPrompt Template Managerpermission sets.

Data Cloud Adminprovides access to manage and oversee models withinEinstein Studio.

Prompt Template Managergives the user the ability to create and manage prompt templates withinPrompt Builder.

Option Ais correct because it assigns the necessary permissions for both managing models and creating prompt templates.

Option BandOption Care incorrect as they do not provide the correct combination of permissions for managing models and building prompts.

References:

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

When usingPrompt Builderin a Salesforce org, the correct process involves several important steps:

Select the appropriate prompt template typebased on the use case.

Develop the promptwithin theprompt workspace, where the template is created and customized.

Select CRM-derived grounding datato be dynamically inserted into the prompt, ensuring that the AI-generated responses are based on accurate and relevant data.

Pick the model to usefor generating responses, either using Salesforce's built-in models or custom ones.

Test and validatethe generated responses to ensure accuracy and effectiveness.

Option Bis correct as it follows the proper steps for usingPrompt Builder.

Option AandOption Cdo not capture the full process correctly.

References:

Salesforce Prompt Builder Documentation:https://help.salesforce.com/s/articleView?id=sf.prompt_builder_overview.htm

The use case that necessitates the use ofPrompt Builderiscreating a draft of a support bulletin postfor new product patches.Prompt Builderallows 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 details, refer toSalesforce’s Prompt Builder documentationfor generative AI content creation.

The key reason for implementingEinstein Copilotis its ability tostreamline workflowsandautomate repetitive tasks. By leveraging AI, Einstein Copilot can assist users in handling mundane, repetitive processes, such as automatically generating insights, completing actions, and guiding users through complex processes, all of which significantly improve operational efficiency.

Option A(Improving data entry and cleansing) is not the primary purpose of Einstein Copilot, as its focus is on guiding and assisting users through workflows.

Option B(Allowing AI to perform tasks without user interaction) does not accurately describe the role of Einstein Copilot, which operates interactively to assist users in real time.

Salesforce AI Specialist References:More details can be found in the Salesforce documentation:https://help.salesforce.com/s/articleView?id=sf.einstein_copilot_overview.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.

Context of the Question

Einstein Prompt Builder is a Salesforce feature that helps generate text (summaries, email content, responses) using AI models.

The question presents three potential use cases, asking which one best fits the capabilities of Einstein Prompt Builder.

Einstein Prompt Builder Typical Use Cases

Text Generation & Summaries: Great for writing or summarizing content, like responding to an email or generating text for a record field.

Why Not Forecast Future Sales Trends or Identify Potential High-Value Leads?

(Option A) Forecasting trends typically involves predictive analytics and modeling capabilities found in Einstein Discovery or standard reporting, not generative text solutions.

(Option B) Identifying leads for marketing campaigns involves lead scoring or analytics, again an Einstein Discovery or Lead Scoring scenario.

Sending a Personalized RFP Email(Option C) is a classic example of using generative AI to compose well-structured, context-aware text.

ConclusionOption C(Send reply to a request for proposal via a personalized email) is the best match for Einstein Prompt Builder’s generative text functionality.

Salesforce AI Specialist References & Documents

Salesforce Documentation:Einstein Prompt Builder OverviewHighlights how to use Prompt Builder to create and customize text-based responses, especially for email or record fields.

Salesforce AI Specialist Study GuideExplains that generative AI features in Salesforce are designed for creating or summarizing text, not for advanced predictive use cases (like forecasting or lead scoring).

In order to access and create custom prompt templates inPrompt Builder, the AI Specialist must have thePrompt Template Managerpermission set assigned. Without this permission, they will not be able to accessPrompt Builderin the Setup menu, even thoughEinstein Generative AIis enabled.

Option Bis correct because thePrompt Template Managerpermission set is required to usePrompt 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 accessPrompt Builder.

References:

Salesforce Prompt Builder Permissions:https://help.salesforce.com/s/articleView?id=sf.prompt_builder_permissions.htm

Service AI Grounding retrieves data from Salesforce objects to ground AI-generated responses. Key considerations:

Field Types: Standard Service AI Grounding supports String and Text Area fields. Custom rich text fields (e.g., RichTextArea) are not supported, making Option B correct.

Objects: While Service AI Grounding primarily uses Case and Knowledge objects (Option A), the limitation here is the field type, not the object.

Visibility: Service AI Grounding respects user permissions and sharing settings unless overridden (Option C is incorrect).

References:

Salesforce Help: Service AI Grounding Requirements

Explicitly states support for "Text Area and String fields" only.

To meetUniversal Containers'goal of improving efficiency and reducing agent handling time with AI-generated responses, the best approach is to enableService Replies,Service AI Grounding, andGrounding with Knowledge.

Service Repliesgenerates responses automatically.

Service AI Groundingensures that the AI is using relevant case data.

Grounding with Knowledgeensures that responses are backed by Salesforce Knowledge articles, allowing agents to identify whether a response is coming from theLLMorSalesforce Knowledge.

Option Cdoes not includeService Replies, which is necessary for generating AI responses.

Option Alacks theGrounding with Knowledge, which is essential for identifying response sources.

For more details, refer toSalesforce Service AI documentationon grounding and service replies.