Salesforce Salesforce-AI-Specialist Salesforce Certified AI Specialist Exam Exam Practice Test

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."

Prompt Builder is explicitly required to create AI-generated summary fields via prompt templates. These fields use natural language instructions to extract or synthesize information (e.g., summarizing contract terms). Time-to-close estimations (A) and lead scores (C) are typically handled by predictive AI (e.g., Einstein Opportunity Scoring) or analytics tools, which do not require Prompt Builder.

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.

WhenUniversal Containerscreates a new Sales Email prompt template using the"Save As"function, missing hyperparameters can result in different outputs. To ensure the new prompt produces comparable results to the standard Sales Email prompt, the AI Specialist shouldmanually add the necessary hyperparametersto the new template.

Hyperparameters likeTemperature,Frequency Penalty, andPresence Penaltydirectly affect how the AI generates responses. Ensuring that these are consistent with the standard template will result in similar outputs.

Option A (Model Playground)is not necessary here, as it focuses on fine-tuning models, not adjusting templates directly.

Option C (Reverting to the standard template)does not solve the issue of customizing the prompt template.

For more information, refer toPrompt Builder documentationon configuring hyperparameters in custom templates.

When 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."

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

To addresssecurity complianceconcerns and provide visibility into theprompt text sent to the LLM, how it ismasked, and themasked response, the AI Specialist should recommend enabling theaudit trail in the Einstein Trust Layer. This feature captures and logs the prompts sent to the large language model (LLM) along with the masking of sensitive information and the AI's response. This audit trail ensures full transparency and compliance with security requirements.

Option A (Einstein Shield Event logs)is focused on system events rather than specific AI prompt data.

Option B (debug logs)would not provide the necessary insight into AI prompt masking or responses.

For further details, refer toSalesforce's Einstein Trust Layer documentationabout auditing and security measures.

For Field Generation prompt templates to display the Sparkle icon (indicating AI-generated content), the target field must be explicitly associated with the prompt template on the Lightning page layout. Even if the prompt template is activated, failing to add the field to the page layout and link it to the templatewill result in the field behaving as a standard field. Salesforce documentation emphasizes that page layout configuration is mandatory to enable AI-driven field interactions.

Reactivating the layout (A) is unnecessary unless the layout itself was modified after activation.

Case objects are supported for Field Generation (B is incorrect).

To enable Sales Email prompt templates:

Permission Set: Assign the Prompt Template User permission set to the sales team to grant access to use pre-built templates.

Feature Activation: Enable Sales Emails in Salesforce Setup to activate the integration between prompt templates and email workflows.

Option B (Manager permission set): Required for creating/modifying templates, not for usage.

Option C (Data Cloud Admin): Unrelated to prompt template access.

References:

Salesforce Help: Prompt Template Permissions

Specifies that "Prompt Template User" is required to leverage templates in workflows.

Sales Email Setup outlines enabling the feature in Setup.

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.

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.

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.

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.

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.

Context of the QuestionUniversal Containers (UC) wants to include details from a related list on the Account object in a prompt template. This is typically done via Prompt Builder in Salesforce’s generative AI setup.

Prompt Builder Behavior

Selecting a Related List: Within Prompt Builder, you can navigate to the object (Account) and choose which related list (e.g., Contacts, Opportunities) you want to reference.

Field Picker: Once a related list is chosen, Prompt Builder provides a field picker interface, allowing you to select specific fields from that related list. These fields then become available for merge fields or dynamic insertion within your prompt.

Why Option A is Correct

Direct Alignment with the Standard Process: The recommended approach in Salesforce’s documentation is to select a related list and then use the field picker to add the necessary fields into your AI prompt. This ensures the prompt has exactly the data you need from that related list.

Why Not Option B (JSON Formatting)

No Mandatory JSON Requirement: Although you can structure data as JSON if you desire advanced formatting, Prompt Builder does not require you to manually assign thefields from the related list in JSON. The platform automatically handles how the data is passed along in the background.

Why Not Option C (Default Page Layout)

Independent of Page Layout: Prompt Builder does not rely strictly on the default page layout for fields. You can configure the fields you want from the related list, independent of how the user’s page layout is set up in the UI.

ConclusionSince the official Salesforce approach involves selecting a related list and then using the field picker to insert merge fields,Option Ais the correct and verified answer.

Salesforce AI Specialist References & Documents

Salesforce Official Documentation:Prompt Builder BasicsExplains how to reference objects and related lists when building AI prompts.

Salesforce Trailhead:Get Started with Prompt BuilderProvides hands-on exercises demonstrating how to pick fields from related objects or lists.

Salesforce AI Specialist Study GuideOutlines best practices for referencing related records and fields in generative AI prompts.

ThePrompt Builderin Salesforce has some specific limitations, one of which is thatcustom objectsaresupportedonly for Flex template types. This means that users must rely on Flex templates to integrate custom objects into their prompts.

Option A: While rich text area fields have certain restrictions, this does not pertain to the core limitation of integrating custom objects.

Option B: Updates and creations for prompt templates are indeed recorded in the Setup Audit Trail, so this statement is incorrect.

Option C: This is the correct answer as it reflects a documented limitation of the Prompt Builder.

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.

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.

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.

In the Einstein Trust Layer, the Salesforce AI Specialist can configure privacy data entities to be masked (Option C). This ensures sensitive or personally identifiable information (PII) is obfuscated when processed by AI models.

Data Masking Configuration:

The AI Specialist defines which fields or data types (e.g., email, phone number, Social Security Number) should be masked. For example, masking the Email field in a prompt response to protect user privacy.

This is done through declarative settings in Salesforce, where entities (standard or custom fields) are flagged for masking.

Why Other Options Are Incorrect:

A. Profiles exempt from masking: Exemptions are typically managed via permissions (e.g., field-level security), not directly within Einstein Trust Layer’s Data Masking settings.

B. Encryption keys for masking: Encryption is separate from masking. Masking involves obfuscation (e.g., replacing "john@example.com" with "@"), not encryption, which uses keys to secure data.

References:

Einstein Trust Layer Documentation: States that Data Masking allows admins to "define which fields should be masked to protect sensitive data."

Trailhead Module: "Einstein Trust Layer Basics" explains configuring privacy entities for masking.

Salesforce Help Article: "Secure AI with Einstein Trust Layer" details masking configurations for privacy compliance.

An AI Specialist wants to include data from the response of an external service invocation (REST API callout) into a prompt template. The goal is to incorporate dynamic data retrieved from an external API into the AI-generated content.

Solution:

Use External Service Record Merge Fields

External Service Integration:

Definition:External Services in Salesforce allow the integration of external REST APIs into Salesforce without custom code.

Registration:The external service must be registered in Salesforce, defining the API's schema and methods.

External Service Record Merge Fields:

Purpose:Enables the inclusion of data from external service responses directly into prompt templates using merge fields.

Functionality:

Dynamic Data Inclusion:Allows prompt templates to access and use data returned from REST API callouts.

Merge Fields Syntax:Use merge fields in the prompt template to reference specific data points from the API response.

Implementation Steps:

Register the External Service:

UseExternal Servicesto register the REST API in Salesforce.

Define the API's schema, including methods and data structures.

Create a Named Credential:

Configure authentication and endpoint details for the external API.

Use External Service in Flow:

Build aFlowthat invokes the external service and captures the response.

Ensure the flow outputs the necessary data for use in the prompt template.

Configure the Prompt Template:

UseExternal Service Record merge fieldsin the prompt template to reference data from the flow's output.

Syntax Example: {{flowOutputVariable.fieldName}}

Why Other Options are Less Suitable:

Option A (Convert the JSON to an XML merge field):

Irrelevance:Converting JSON to XML merge fields is unnecessary and complicates the process.

Unsupported Method:Salesforce prompt templates do not support direct inclusion of XML merge fields from JSON conversion.

Option C (Use “Add Prompt Instructions” flow element):

Purpose of Add Prompt Instructions:

Allows adding instructions to the prompt within a flow but does not facilitate including external data.

Limitation:Does not directly help in incorporating external service responses into the prompt template.

References:

Salesforce AI Specialist Documentation -Integrating External Services with Prompt Templates:

Explains how to use External Services and merge fields in prompt templates.

Salesforce Help -Using Merge Fields with External Data:

Provides guidance on referencing external data in templates using merge fields.

Salesforce Trailhead -External Services and Flow:

Offers a practical understanding of integrating external APIs using External Services and Flow.

Conclusion:

By using External Service Record merge fields, the AI Specialist can effectively include data from external REST API responses into prompt templates, ensuring that the AI-generated content is enriched with up-to-date and relevant external data.

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.

Sales Email Template Use Case:When creating a Sales email template (especially for outreach or follow-up), you often need to reference relevant details about the Account linked to the recipient.

Standard Merge Fields in Salesforce Email Templates:

Recipient(Contact, Lead, or Person receiving the email)

Sender(User sending the email)

Recipient Account(the Account related to that Contact, providing company-level details and other relevant data)

Why Recipient Account?

For Sales communications, referencing theAccountdata (e.g., Account name, industry, or other custom fields) in an email is very common.

This is especially important for B2B scenarios where the Contact is tied to an Account.

“Recipient Opportunities” could be multiple, so it’s less direct for standard email merges. The “User Organization” is more generic internal information, not typically inserted for personalization to the recipient.

References and Study Resources:

Salesforce Help & Training→Email Templates: Merge Fields

Salesforce Trailhead→“Create and Customize Email Templates in Sales Cloud”

Salesforce AI Specialist Study Resources(covers recommended best practices for leveraging standard objects like Account in AI-powered or prompt-based communications)

To monitorEinstein Copilot'susability and the assignment of actions, the AI Specialist should runEinstein Copilot Analytics. This feature provides insights into how often Copilot is used, the types of actions it is handling, and overall user engagement with the system. It’s the most effective way to track Copilot's performance and usage patterns.

Platform Debug Logsare not relevant for tracking user behavior or the assignment of Copilot actions.

Querying the Copilot log data via the Metadata APIwould not provide the necessary insights in a structured manner.

For more details, refer toSalesforce's Copilot Analytics documentationfor tracking AI-driven interactions.

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

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

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.

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.

The sales team at a hotel resort wants to generate a guest summary about guests' interests and provide recommendations based on their activity preferences captured in each guest profile. They require the summary to be availableonly on the contact record page.

Solution:

UsePrompt Builderto create a prompt template that generates the desired summary and displays it on the contact record page.

Prompt Builder:

Purpose:Allows the creation of custom prompt templates that leverage AI to generate content based on Salesforce data.

Functionality:

Field Generation Templates:Can be used to populate fields on records with AI-generated summaries.

Customization:Enables the AI Specialist to design prompts that utilize data from the guest profiles to produce personalized summaries and recommendations.

Relevance to the Use Case:

The sales team wants the summary to be available on the contact record page, which aligns with the capabilities of Prompt Builder to generate and display content on specific record pages.

Implementation Steps:

Create a Field Generation Prompt Template:

Use Prompt Builder to create a new prompt template of typeField Generation.

Design the prompt to instruct the AI to generate a summary based on the guest's interests and activity preferences.

Include Relevant Data:

Use merge fields to include data from the guest profile in the prompt.

Ensure that the prompt accesses the necessary fields to generate accurate recommendations.

Configure the Contact Page Layout:

Add the field that will display the AI-generated summary to the contact record page layout.

Ensure that the field is only visible where appropriate, adhering to the requirement of availability only on the contact record page.

Why Not Einstein Copilot or Model Builder:

Option A (Einstein Copilot):

Purpose:Einstein Copilot is a conversational AI assistant designed to interact with users through natural language.

Mismatch with Requirements:

The team wants a static summary displayed on the contact record page, not an interactive conversational experience.

Option C (Model Builder):

Purpose:Model Builder is used to create custom AI models for predictions and classifications.

Inapplicability:

Building a custom model is unnecessary for generating text summaries based on existing data.

Model Builder does not directly provide functionality to generate and display summaries on record pages.

References:

Salesforce AI Specialist Documentation -Prompt Builder Overview:

Provides an introduction to Prompt Builder and its capabilities.

Salesforce Help -Creating Field Generation Prompt Templates:

Guides on creating prompt templates that generate content for fields on records.

Salesforce Trailhead -Customize AI Content with Prompt Builder:

Offers hands-on experience in building and customizing prompt templates.

Conclusion:

By utilizing Prompt Builder, the sales team can create a customized prompt template that generates personalized guest summaries and recommendations based on activity preferences. This solution meets the requirement of displaying the summary only on the contact record page, enhancing the team's ability to engage with guests effectively.

Agentforce’s LLM dynamically interprets natural language requests (e.g., "Show me open opportunities"), generates an execution plan using the planner service, and retrieves data via actions (e.g., querying Salesforce records). This contrasts with static rules (B) or rigid dialog patterns (C), which lack contextual adaptability. Salesforce documentation highlights the planner’s role in converting intents into actionable steps while adhering to security and business logic.

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 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.

ForUniversal Containers (UC)to offer personalized service experiences and reduce agent handling time using AI-generated responses grounded in theKnowledge base, the best solution isEinstein Service Replies for Email. This capability leverages AI to automatically generate responses to service-related emails based on historical data and theKnowledge base, ensuring accuracy and relevance while saving time for service agents.

Einstein Email Replies(option A) is more suited for sales use cases.

Einstein Generative Service Replies for Email(option C) could be a future offering, but as of now,Einstein Service Replies for Emailis the correct choice for grounded, knowledge-based responses.

References:

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

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.

Einstein Trust Layer’s Toxicity Scoring categorizes content on a scale of 0 to 1, where 1 indicates the highest level of toxicity (e.g., harmful, biased, or inappropriate language). This scoring helps organizations filter unsafe AI-generated content. A score of 1 triggers mitigation actions, such as blocking the response or alerting administrators.

A score of 0 would indicate no toxicity (B is incorrect).

The scoring system does not use "least toxic" as a category (A is misleading).

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

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

WhenUniversal Containers' AI data masking rulesdo not meet organizational privacy and security standards, the AI Specialist should configure thedata maskingrules within theEinstein Trust Layer. TheEinstein Trust Layerprovides a secure and compliant environment where sensitive data can be masked or anonymized to adhere to privacy policies and regulations.

Option A, enabling data masking for sandbox refreshes, is related to sandbox environments, which are separate from how AI interacts with production data.

Option C, adding masking rules in the LLM setup, is not appropriate because data masking is managed through theEinstein Trust Layer, not the LLM configuration.

The Einstein Trust Layer allows for more granular control over what data is exposed to the AI model and ensures compliance with privacy regulations.

Salesforce AI Specialist References:For more information, refer to:https://help.salesforce.com/s/articleView?id=sf.einstein_trust_layer_data_masking.htm

Before enabling Call Explorer, the Salesforce AI Specialist must ensure that the Einstein Conversation Insights permission set is assigned to users (Option C). Call Explorer is a feature within Einstein Conversation Insights (ECI) that analyzes meeting recordings to surface trends, keywords, and actionable insights.

Key Considerations:

Permission Set Requirement:

Users (including admins) need the Einstein Conversation Insights permission set to access and use Call Explorer. Without this, the feature remains inaccessible.

The permission set grants access to ECI tools, including call transcription, analysis, and dashboard visibility.

Why Other Options Are Incorrect:

A. Independence from Salesforce Knowledge: While Call Explorer does not rely on Salesforce Knowledge, this is irrelevant to the setup prerequisite. The critical dependency is the permission set, not Knowledge configuration.

B. Custom Actions: Call Explorer does not require custom actions to be built before configuration. It is a pre-built analytics tool that works once permissions and data sources (e.g., call recordings) are configured.

References:

Salesforce Einstein Conversation Insights Guide: Explicitly states that the Einstein Conversation Insights permission set is required to access Call Explorer.

Trailhead Module: "Einstein Conversation Insights Basics" outlines permission prerequisites for enabling call analytics.

Salesforce Help Documentation: Confirms that Call Explorer functionality is governed by ECI permissions.

In this scenario,Universal Containerswants to bring data fromunified Data Cloud objectsinto prompt templates, and the best way to do that is through aData Cloud-triggered flow. This type of flow is specifically designed to trigger actions based on data changes within Salesforce Data Cloud objects.

Data Cloud-triggered flows can listen for changes in the unified data model and automatically bring relevant data into the system, making it available for prompt templates. This ensures that the data is both real-time and up-to-date when used in generative AI contexts.

For more detailed guidance, refer to Salesforce documentation onData Cloud-triggered flowsandData Cloud integrationswith generative AI solutions.

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

To effectively manage and use prompt templates, two distinct permission sets are required:

Prompt Template Manager: This permission set allows users to create prompt templates. It provides the necessary access to define templates, which can be shared and utilized across the organization.

Prompt Template User: This permission set is designed for users who need to execute the templates. It provides the ability to interact with pre-designed prompts and generate outcomes based on these templates.

TheData Cloud Adminpermission set is not directly relevant to creating or executing prompt templates but is more focused on managing the Data Cloud.

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 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.

The support team should useEinstein Reply Recommendationsto provide quick, relevant responses to customer inquiries that are grounded in the organization’s knowledge base. This feature leverages AI to recommend accurate and consistent replies based on historical interactions and the knowledge stored in the system, ensuring that responses are aligned with organizational standards.

Einstein Service Replies(Option A) is focused on generating replies but doesn't have the same emphasis on grounding responses in the knowledge base.

Einstein Knowledge Recommendations(Option C) suggests knowledge articles to agents, which is more about assisting the agent in finding relevant articles than providing automated or AI-generated responses to customers.

Salesforce AI Specialist References:For more information on Einstein Reply Recommendations:https://help.salesforce.com/s/articleView?id=sf.einstein_reply_recommendations_overview.htm