Salesforce-AI-Specialist Salesforce Certified AI Specialist Exam Questions and Answers

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

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.

Context of the Question

Universal Containers (UC) has generative AI–enabled fields that users can access in the desktop experience.

The AI Specialist needs these same fields to be visible and usable in the Salesforce Mobile App.

Why Dynamic Forms on Mobile?

Dynamic Formsallow you to configure record pages so that fields and sections can appear or be hidden based on certain criteria.

When you enable “Dynamic Forms for Mobile,” any generative AI–enabled fields placed on the dynamic layout become accessible in the Salesforce mobile experience.

There is no standard Setup option labeled “Enable Mobile Generative AI” or “Enable Mobile Prompt Responses” as a universal toggle; the existing official approach is to ensure dynamic forms (and the relevant fields) are supported on mobile.

Conclusion

Ensuring that these AI-driven fields are visible on mobile is accomplished by turning onDynamic Forms on Mobileand adding those fields to the dynamic layout. Therefore,Option Cis correct.

Salesforce AI Specialist References & Documents

Salesforce Documentation:Dynamic Forms OverviewExplains how to enable Dynamic Forms for both desktop and mobile UIs, allowing newly added fields (including generative AI–enabled ones) to display in the Salesforce Mobile App.

Salesforce AI Specialist Study GuideReiterates that to expose generative AI fields or components in mobile, you must configure dynamic forms and ensure compatibility on mobile layouts.

To ensure that content generated by a large language model (LLM) is appropriately screened for toxic language, the AI Specialist should create aTrust Layer audit reportwithinData Cloud. By using thetoxicity detector type filter, the report can displaytoxic responsesalong with their respective toxicity scores, allowingUniversal Containersto monitor and manage any toxic content generated with a high level of confidence.

Option Cis correct because it enables visibility into toxic language detection within theTrust Layerand allows for auditing responses for toxicity.

Option Asuggests checking a toxicity detection log, butSalesforceprovides more comprehensive options via the audit report.

Option Binvolves creating a flow, which is unnecessary for toxicity detection monitoring.

References:

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

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.

New customer support reps atUniversal Containerscan useEinstein Work Summariesto quickly understand the details of a case without reading through each case comment.Work Summariesleverage generative AI to provide a concise overview of ongoing cases, summarizing all relevant information in an easily digestible format.

Einstein Copilotcan assist with a variety of tasks but is not specifically designed for summarizing case details.

Einstein Sales Summariesare focused on summarizing sales-related activities, which is not applicable for support cases.

For more details, refer toSalesforce documentation on Einstein Work Summaries.

Agentforce Sales Agent is designed to let sales teams perform tasks via natural language commands, leveraging Salesforce data while adhering to policies. For example, agents can ask the AI to "update the opportunity stage to Closed Won" or "generate a quote," with the system enforcing validations and data security. This use case aligns with Salesforce’s vision of conversational AI streamlining workflows without compromising compliance.

Step-by-step guides (B) are typically handled by tools like Dynamic Forms or Guided Selling, not Agentforce.

Logging messages/emails (C) is managed by Email-to-Case or Service Cloud, not a sales-specific AI agent.

Universal Containersshould use aPredictive modelto decide which of the three emails to send based on the customer'slifetime value scoreandmarket segment. Predictive models analyze data to forecast outcomes, and in this case, it would predict the most appropriate email to send based on customer attributes. Additionally, predictive models can provideexplainabilityto show why a certain email was chosen, which is crucial for UC’s requirement to explain the decision-making process.

Generative modelsare typically used for content creation, not decision-making, and thus wouldn't be suitable for this requirement.

Predictive modelsoffer the ability to explain why a particular decision was made, which aligns with UC’s needs.

Refer toSalesforce’s Predictive AI model documentationfor more insights on how predictive models are used for segmentation and decision making.

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

To understand multiple real-world user utterances and ensure the correct action is selected before activating acustom copilot action, the recommended tool isCopilot Builder. This tool allows AI Specialists to design and test conversational actions in response to user inputs, helping ensure the copilot can accurately handle different user queries and phrases.Copilot Builderprovides the ability to test, refine, and improve actions based on real-world utterances.

Option Cis correct asCopilot Builderis designed for configuring and testing conversational actions.

Option A(Model Playground) is used for testing models, not user utterances.

Option B(Einstein Copilot) refers to the conversational interface but isn't the right tool for designing and testing actions.

References:

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

When an active standard email prompt template doesn’t meet the business requirements, the best approach is toclone the existing templateand modify it as needed. Cloning allows the AI Specialist to preserve the original template while making adjustments to fit specific business needs. This ensures that any customizations are applied without altering the original standard template.

Saving as a new versionis typically used for versioning changes in the same template, whileSave as New Templatecreates a brand-new template without linking to the existing one.Cloningprovides a balance, allowing modifications while retaining the original structure for future reference.

For more details, refer toSalesforce Prompt Builder documentationfor guidance on cloning and modifying templates.

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

Universal Containers (UC) wants to enable its sales team to leverage AI to recommend products from its catalog. The best option for this use case is aFlex prompt template.

AFlex prompt templateis designed to provide flexible, customizable AI-driven recommendations or responses based on specific data points, such as product information, customer needs, or sales history. This template type allows the AI to consider various inputs and parameters, making it ideal for generating product recommendations dynamically.

In contrast:

ARecord summary prompt template(Option A) is used to summarize data related to a specific record, such as generating a quick summary of a sales opportunity or account, but not for recommending products.

AnEmail generation prompt template(Option B) is tailored for crafting email content and is not suitable for suggesting products based on a catalog.

Given the need for dynamic recommendations that pull from a product catalog and potentially other sales data, theFlex prompt templateis the correct approach.

Salesforce References:

Salesforce Prompt Templates Overview:https://help.salesforce.com/s/articleView?id=000391407 &type=1

Flex Prompt Template Usage:https://developer.salesforce.com/docs/atlas.en-us.salesforce_ai.meta/salesforce_ai/prompt_flex_template

After modifying masked fields inEinstein's Trust Layer, the next important step is totest and confirmthat the responses generated by prompts utilizing the newly masked data still meet quality standards. This ensures that masking sensitive information does not negatively impact the usefulness or accuracy of the AI-generated content. Thorough testing helps identify any issues in prompt performance that could arise due to masking, and adjustments can be made if needed.

Option Bis correct because testing the effects of masking on AI responses is a critical step in ensuring AI continues to function as expected.

Option A(turning off and on the Einstein Trust Layer) is unnecessary after changing the masked fields.

Option C(turning on Einstein Feedback) allows for user feedback but is not a direct step following field masking modifications.

References:

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

To generate personalized emails using LLMs while reusing templates:

Sales Email Prompt Template Type (Option C): Designed specifically for generating dynamic email content by combining LLMs with structured templates. It allows admins to define placeholders (e.g., customer name, product details) and reuse templates across scenarios.

Option A: Standard email templates lack LLM integration and dynamic personalization.

Option B: "t field Generation" is not a valid Salesforce prompt template type.

References:

Salesforce Help: Sales Email Prompt Templates

Describes using Sales Email prompt templates to "generate targeted emails using dynamic data and LLMs."

To enable personalized product recommendations using generative AI, the foundational step for Universal Containers (UC) is collecting and analyzing browsing data (Option C). Personalized recommendations depend on understanding user behavior, which requires structured data about their browsing history. Without this data, the AI model lacks the context needed to generate relevant suggestions.

Data Collection: UC must first aggregate browsing data (e.g., pages visited, products viewed, session duration) to build a dataset that reflects user preferences.

Data Analysis: Analyzing this data identifies patterns (e.g., frequently viewed categories) that inform how prompts should be structured to retrieve relevant recommendations.

Grounding in Data: Salesforce’s Prompt Templates rely on grounding data to generate accurate outputs. Without analyzing browsing data, the prompt template cannot reference meaningful insights for personalization.

Options A and D are incorrect because:

Universal recommendations (A) ignore personalization, which is the core requirement.

Writing a response script (D) addresses chatbot interaction design, not the accuracy of recommendations.

References:

Salesforce AI Specialist Certification Guide: Highlights the importance of grounding prompts in relevant data sources to ensure accuracy.

Trailhead Module: "Einstein for Developers" emphasizes data preparation as a prerequisite for effective AI-driven personalization.

Salesforce Help Documentation: Recommends analyzing user behavior data to tailor generative AI outputs in commerce use cases.

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

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.

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

InModel Playground, an AI specialist working with a Salesforce-enabled foundational model has control over specific hyperparameters that can directly affect the behavior of the generative model:

Temperature: Controls the randomness of predictions. A higher temperature leads to more diverse outputs, while a lower temperature makes the model's responses more focused and deterministic.

Frequency Penalty: Reduces the likelihood of the model repeating the same phrases or outputs frequently.

Presence Penalty: Encourages the model to introduce new topics in its responses, rather than sticking with familiar, previously mentioned content.

These hyperparameters are adjustable to fine-tune the model’s responses, ensuring that it meets the desired behavior and use case requirements. Salesforce documentation confirms that these three are the key tunable hyperparameters in the Model Playground.

For more details, refer toSalesforce AI Model Playgroundguidance from Salesforce’s official documentation on foundational model adjustments.

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

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.

The Temperature hyperparameter controls the randomness of model outputs:

Low Temperature (e.g., 0.2): More deterministic, consistent responses.

High Temperature (e.g., 1.0): More creative, varied responses.

Presence Penalty (Option A): Discourages repetition of tokens, unrelated to randomness.

Variability (Option B): Not a standard hyperparameter in Einstein Studio.

References:

Einstein Studio Documentation: Model Hyperparameters

Explicitly states "Temperature adjusts the balance between predictable and random outputs."

Einstein Call Summaries is the best option for this scenario because it leverages Salesforce's AI capabilities to automatically summarize key details of video or voice calls. It includes details like deal terms, customer sentiments, follow-up tasks, and other crucial information. This feature is designed to help sales teams focus on their strategies rather than taking extensive manual notes during conversations.

Einstein Call Summaries:Automatically generates summaries for calls, identifying critical points such as next steps and follow-ups, enhancing efficiency and understanding of deal progression.

Einstein Conversation Insights:While it provides insights into customer sentiment and engagement, it is more suited for analyzing patterns across conversations rather than summarizing specific call details.

Einstein Video KPI:Focuses on analyzing key performance indicators within video calls but does not offer summarization features needed for deal terms or sentiment tracking.

This feature ensures actionable insights are delivered directly into the Salesforce CRM, allowing sales managers to gain a concise overview without manually reviewing long recordings.

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

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 to50 productsand50 competitors, providing tailored insights relevant to the organization's needs.

OptionCaccurately reflects these steps. OptionAmentions defining recording managers but omits enabling sales recording within Salesforce. OptionBsuggests connecting a recording provider and limits customization to 25 products, which does not fully meet UC's requirements.

References:

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.

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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.

When a prompt template version is immutable, it means that once the version is activated, it cannot be edited or modified. This ensures consistency in production environments where changes could disrupt workflows.

Option A is incorrect: Any version (not just the latest) can be activated, depending on the use case.

Option D is incorrect: Modifications require manually creating a new version; automatic versioning is not enforced.

Option C is correct: Activation locks the version, enforcing immutability.

References:

Salesforce Help: Prompt Template Versioning

States that "activated prompt template versions are immutable and cannot be edited."

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

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.

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 grounding a prompt template with a related list, the AI must be explicitly configured to include the related list’s data. If the "related list prompt template option" is not enabled, the AI ignores the related list, leading to incomplete or incorrect responses.

Option A: Page layout visibility affects user interface display but does not restrict data access for AI grounding.

Option B: Read-only settings prevent edits but not data retrieval.

Option C: Enabling the related list in the prompt template configuration is mandatory for the AI to use its data.

References:

Salesforce Help: Prompt Template Grounding Settings

States that "related lists must be enabled in the prompt template’s grounding settings to include their data in AI responses."

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.

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.

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 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

For a sales rep who may miss key details during long sales calls, the AI Specialist should recommend theCall Summaryfeature.Call SummaryusesEinstein Generative AIto automatically generate a concise summary of important points discussed during the call, helping the rep quickly review the key information they might have missed.

Call Exploreris designed for manually searching through call data but doesn't summarize.

Sales Summaryis focused more on summarizing overall sales activity, not call-specific content.

For more details, refer toSalesforce's Call Summary documentationon how AI-generated summaries can improve sales rep productivity.

Agentforce Sales Agent provides real-time insights and AI-powered recommendations, which are designed tostreamline the sales processand help sales representatives focus on key tasks toincrease conversion rates. It offers features like lead scoring, opportunity prioritization, and proactive recommendations, ensuring that sales teams can interact with customers efficiently and close deals faster.

Option A: While tracking customer interactions is beneficial, it is only part of the broader capabilities offered by Agentforce Sales Agent and is not the primary objective for improving real-time productivity.

Option B: Agentforce Sales Agent does not automate the entire sales process but provides actionable recommendations to assist the sales team.

Option C: This aligns with the tool's core purpose of enhancing productivity and driving sales success.

When troubleshooting acopilot custom actionusing flow as the reference action type, enablingdynamic debuggingwithinCopilot Builder's Dynamic Panelis the most effective way to identify the root cause. By turning on dynamic debugging, the AI Specialist can see detailed logs showing both theinputs and outputsof 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 References:To explore more about dynamic debugging in Copilot Builder, see:https://help.salesforce.com/s/articleView?id=sf.copilot_custom_action_debugging.htm

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 ofEinstein Reply RecommendationsandCase Classificationis 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.

References:

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.

To integrate web activity data from Data Cloud into a prompt template, the correct approach is to enrich the Contact object with the activity records as a related list and use related list grounding (Option B). Here’s why:

Data Cloud Integration: Data Cloud unifies web activity data and associates it with the unified Contact record. By adding these activities as a related list to the Contact, the data becomes accessible to the prompt template.

Prompt Template Grounding: Salesforce prompt templates support grounding on related records. When the Contact is passed to the prompt template, the template can reference the related web activity records (via the related list) to extract insights.

Structured Data Handling: This method aligns with Salesforce best practices for grounding, ensuring the large language model (LLM) receives structured, context-rich data without overwhelming it with raw activity lists.

Why Other Options Are Incorrect:

A. Calling the prompt directly from Data Cloud: Prompt templates are invoked within Salesforce, not directly from Data Cloud. Grounding requires associating data with Salesforce objects, not ad-hoc web activity inclusion.

C. Passing a list of activity records as input: While technically possible, this bypasses Salesforce’s grounding framework, which relies on object relationships. It also risks exceeding LLM input limits and lacks scalability.

References:

Salesforce Data Cloud Implementation Guide: Explains how to enrich standard/custom objects with related data for AI use cases.

Prompt Template Documentation: Highlights grounding on related lists to leverage contextual data for LLM prompts.

Trailhead Module: "Einstein Prompt Builder Basics" demonstrates grounding techniques using related records.

To accessexternally-hosted models, such as a large language model (LLM) hosted on AWS, theModel Builderin Salesforce is the appropriate tool.Model Builderallows teams to integrate and deploy external AI models into the Salesforce platform, making it possible to leverage models hosted outside of Salesforce infrastructure while still benefiting from the platform's native AI capabilities.

Option B, App Builder, is primarily used to build and configure applications in Salesforce, not to integrate AI models.

Option C, Copilot Builder, focuses on building assistant-like tools rather than integrating external AI models.

Model Builder enables seamless integration with external systems and models, allowing Salesforce users to use external LLMs for generating AI-driven insights and automation.

Salesforce AI Specialist References:For more details, check the Model Builder guide here:https://help.salesforce.com/s/articleView?id=sf.model_builder_external_models.htm