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

The primary function of theplanner servicein theEinstein Copilotsystem is toidentify copilot actionsthat should be taken in response to user utterances. This service is responsible for analyzing the conversation and determining the appropriate actions (such as querying records, generating a response, or taking another action) that theEinstein Copilotshould perform based on user input.

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.

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

Action Instructions are critical for defining how a custom Agent action should be executed, ensuring alignment with the intended user experience. They provide step-by-step guidance to the bot or LLM on logic, data handling, and integration workflows, directly impacting how users interact with the action. For example, clear instructions prevent errors in API calls or data processing, ensuring seamless interactions.

Salesforce documentation states that poorly defined instructions lead to mismatched expectations, while well-structured instructions ensure the action behaves predictably. This aligns with delivering a consistent user experience.

A refers to user invocation, which is handled by dialogue flows, not instructions.

B is incorrect because the LLM selects actions based on context/intent, not instructions.

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.

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)."

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 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).

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

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

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.

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.

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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To integrate inferences from an XGBoost model into Salesforce's Data Cloud as a stand-alone Data Model Object (DMO):

Create the Serving Endpoint in Databricks:

The serving endpoint is necessary to make the trained model available for real-time inference. Databricks provides tools to host and expose the model via an endpoint.

Configure the Model Using Model Builder:

After creating the endpoint, the AI Specialist should configure it within Einstein Studio'sModel Builder, which integrates external endpoints with Salesforce Data Cloud for processing and storing inferences as DMOs.

Option B: Serving endpoints are not created in Einstein Studio; they are set up in external platforms like Databricks before integration.

Option C: A Python SDK connector is not used to bring model inferences into Salesforce Data Cloud; Model Builder is the correct tool.

In theEinstein Trust Layer, thetoxicity scoringsystem is used to evaluate the safety level of content generated by AI, particularly to ensure that it is non-toxic, inclusive, and appropriate for business contexts. Atoxicity score of 1indicates that the content is deemedsafe.

The scoring system ranges from 0 (unsafe) to 1 (safe), with intermediate values indicating varying degrees of safety. In this case, a score of 1 means that the generated content is fully safe and meets the trust and compliance guidelines set by theEinstein Trust Layer.

For further reference, check Salesforce’s officialEinstein Trust Layer documentationregardingtoxicity scoringfor AI-generated content.

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

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

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."

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.

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 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.

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.

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."

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.

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

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

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

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

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.

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

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

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.

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.

Sales Insight Summary aggregates key data points from multiple Salesforce objects (accounts, contacts, leads, opportunities) into a consolidated view, enabling account managers to quickly access relevant information for customer calls.

Option A (Sales Summaries): Typically refers to Einstein-generated summaries of specific interactions (e.g., emails, calls), not multi-object snapshots.

Option C (Work Summaries): Focuses on summarizing customer service interactions (e.g., chat transcripts), not sales data.

Option B (Sales Insight Summary): Directly provides a holistic snapshot of sales-related objects, aligning with the scenario.

References:

Salesforce Help: Sales Insight Overview

Describes Sales Insight Summary as "a unified view of account, contact, and opportunity data for sales readiness."

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.

When configuring acustom prompt templateto use related lists, the AI Specialist must be aware of themaximum number of related list merge fieldsthat can be included. Salesforce enforces limits to ensure prompt templates perform efficiently and do not overload the system with too much data. As a best practice, it's important to monitor and optimize the number of merge fields used.

Option Bis correct because there is a limit on how many related list merge fields can be included in a prompt template.

Option A(text encoding) andOption C(XML/JSON rendering) are not key considerations in this context.

References:

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

Einstein Copilotimplementation would be most advantageous inSalesforce Service Cloudwhen the goal is tostreamline customer support processes and improve response times. Einstein Copilot can assist agents by providing real-time suggestions, automating repetitive tasks, and generating contextual responses, thus enhancing service efficiency.

Option B (data security)is not the primary focus of Einstein Copilot, which is more about improving operational efficiency.

Option C (marketing campaigns)falls outside the scope of Service Cloud and Einstein Copilot’s primary benefits, which are aimed at improving customer service and case management.

For further reading, refer toSalesforce documentation on Einstein Copilot for Service Cloudand how it improves support processes.

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

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.

When creating a custom action for Einstein Bots in Salesforce (including Agentforce), Action Instructions are critical for defining how the bot processes and executes the action. These instructions guide the bot on the logic to follow, such as API calls, data transformations, or conditional steps. Testing and iterating on the instructions ensures the bot understands how to handle dynamic inputs, external integrations, and decision-making.

Salesforce documentation emphasizes that Action Instructions directly impact the bot’s ability to execute workflows accurately. For example, poorly defined instructions may lead to incorrect API payloads or failure to parse responses. The Einstein Bot Developer Guide highlights that refining instructions is essential for aligning the bot’s behavior with business requirements.

In contrast:

Action Name (A) is a static identifier and does not affect functionality.

Action Input (B) defines parameters passed to the action but does not dictate execution logic.

Thus, iterating on Action Instructions (C) ensures the action performs as expected.

The"Create Close Plan"feature is designed to help sales reps by providingAI-generated strategies and stepsspecifically focused on closing deals. This feature leverages AI to analyze the current state of opportunities and generate a plan that outlines the actions, timelines, and key steps required to move deals toward closure. It aligns directly with the sales team’s need to meet quotas by offering actionable insights and structured plans.

Find Similar Deals(Option A) helps sales reps discover opportunities similar to their current deals but doesn’t offer a plan for closing.

Create Account Plan(Option B) focuses on long-term strategies for managing accounts, which might include customer engagement and retention, but doesn’t focus on deal closure.

Salesforce AI Specialist References:For more information on using AI for sales, visit:https://help.salesforce.com/s/articleView?id=sf.einstein_for_sales_overview.htm

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.

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

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

No data is retainedoutside of the customer's Salesforce organization.

The LLM provider cannot access any customer data.

This policy aligns perfectly with UC’s requirements for keeping their data safe while leveraging generative AI capabilities.

Prompt DefenseandData Maskingare also security features, but they do not directly address the concerns related to third-party data access and retention.

References:

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

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 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.