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

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."

To create a digest of account action plans using the generative API feature,Universal Containersshould use theREST API. TheREST APIis ideal for integrating Salesforce with external systems and enabling interaction with Salesforce data, including generative capabilities like creating summaries or digests. It supports modern web standards and is suitable for flexible, lightweight interactions between Salesforce and legacy systems.

Metadata APIis used for retrieving and deploying metadata, not for data operations like generating summaries.

SOAP APIis an older API used for integration but is less flexible compared to REST for this specific use case.

For more details, refer toSalesforce REST API documentationregarding using REST for data integration and generating content.

When the"Enrich event logs with conversation data"setting is enabled inEinstein Copilot, it allows an AI Specialist or admin to view session data, including both theuser inputandcopilot responsesfrom interactions over the past 7 days. This data is crucial for monitoring how the copilot is being used, analyzing its performance, and improving future interactions based on past inputs.

This setting enriches the event logs with detailed conversational data for better insights into the interaction history, helping AI specialists track AI behavior and user engagement.

Option A, viewing the user click path, focuses on navigation but is not part of the conversation data enrichment functionality.

Option C, generating detailed reports over any time period, is incorrect because this specific feature is limited to data for the past 7 days.

Salesforce AI Specialist References:You can refer to this documentation for further insights:https://help.salesforce.com/s/articleView?id=sf.einstein_copilot_event_logging.htm

When implementingEinstein Generative AIfor improved customer insights and interactions, theData Cloudis a key consideration for storing and managing large-scale audit and feedback data. TheSalesforce Data Cloud(formerly known asCustomer 360 Audiences) is designed to handle and unify massive datasets from various sources, making it ideal for storing data required for AI-powered insights and reporting. By provisioningData Cloud, organizations likeUniversal Containers (UC)can gain real-time access to customer data, making it a central repository for unified reporting across various systems.

Audit and feedback datagenerated by Einstein Generative AI needs to be stored in a scalable and accessible environment, and theData Cloudprovides this capability, ensuring that data can be easily accessed for reporting, analytics, and further model improvement.

Custom objectsorSalesforce Big Objectsare not designed for the scale or the specific type of real-time, unified data processing required in such AI-driven interactions.Big Objectsare more suited for archival data, whereasData Cloudensures more robust processing, segmentation, and analysis capabilities.

References:

Salesforce Data Cloud Documentation:https://www.salesforce.com/products/data-cloud/overview/

Salesforce Einstein AI Overview:https://www.salesforce.com/products/einstein/overview/

Context of the Question

Universal Containers (UC) wants to build a Flex prompt template that uses data returned by a Flow.

“Flex Prompt Templates” allow admins and AI Specialists to incorporate external or dynamic data into generative AI prompts.

Why “Add Flow Instructions” Is Needed

Passing Flow Data into Prompt Templates: When configuring the prompt, you must specify how data from the running Flow is passed into the Flex template. The designated element for that is typically “Flow Instructions,” which map the Flow outputs to the prompt.

Other Options:

Add Flex Instructions: Typically controls how the AI responds or structures the output, not how to bring Flow data into the template.

Add Prompt Instructions: Usually for static or manual instructions that shape the AI’s response, rather than referencing dynamic data from the Flow.

Outcome

“Add Flow Instructions” ensures the prompt can dynamically use the data that the Flow returns—makingOption Ccorrect.

Salesforce AI Specialist References & Documents

Salesforce Help & Training:Using Prompt Templates with FlowExplains how to pass Flow variables into a prompt template via a specialized step (e.g., “Flow Instructions”).

Salesforce AI Specialist Study GuideOutlines how to configure generative AI prompts that reference real-time Flow data.

When thepreview button is greyed outin a Flex prompt template, it is often because the records related to the prompt have not been selected. Flex prompt templates pull data dynamically from Salesforce records, and if there are no records specified for the prompt, it can't be previewed since there is no content to generate based on the template.

Option B, not saving or activating the prompt, would not necessarily cause the preview button to be greyed out, but it could prevent proper functionality.

Option C, missing a merge field, would cause issues with the output but would not directly grey out the preview button.

Ensuring that the related records are correctly linked is crucial for testing and previewing how the prompt will function in real use cases.

Salesforce AI Specialist References:Refer to the documentation on troubleshooting Flex templates here:https://help.salesforce.com/s/articleView?id=sf.flex_prompt_builder_troubleshoot.htm

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

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

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

The LLM provider cannot access any customer data.

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

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

References:

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

Einstein Work Summaries automatically generate concise summaries of customer interactions (e.g., chat transcripts). Beyond the "summary" field, it extracts and populates Issue (key problem discussed) and Resolution (action taken to resolve the issue). These fields help agents and supervisors quickly grasp the conversation's context without reviewing the full transcript.

Sentiment Analysis and Emotion Detection (Option A): While Einstein Conversation Insights provides sentiment scores and emotion detection, these are separate from Work Summaries. Work Summaries focus on factual summaries, not sentiment.

Draft Survey Request Email (Option B): Not part of Work Summaries. This would require automation tools like Flow or Email Studio.

Issue and Resolution (Option C): Directly referenced in Salesforce documentation as fields populated by Einstein Work Summaries.

References:

Salesforce Help Article: Einstein Work Summaries

Einstein Work Summaries focus on "key details like Issue and Resolution" alongside summaries.

Contrast with Einstein Conversation Insights for sentiment/emotion analysis.

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

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

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

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.

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.

Dynamic grounding with secure data retrieval is a key feature in Salesforce'sEinstein Trust Layer, which provides enhanced data protection and ensures that AI-generated outputs are both accurate and securely sourced. This feature allowsrelevant Salesforce datato be merged into the AI-generated responses, ensuring that the AI outputs are contextually aware and aligned with real-time CRM data.

Dynamic grounding means that AI models are dynamically retrieving relevant information from Salesforce records (such as customer records, case data, or custom object data) in a secure manner. This ensures that any sensitive data is protected during AI processing and that the AI model’s outputs are trustworthy and reliable for business use.

The other options are less aligned with the requirement:

Data maskingrefers to obscuring sensitive data for privacy purposes and is not related to merging Salesforce records into prompts.

Zero-data retention policyensures that AI processes do not store any user data after processing, but this does not address the need to merge Salesforce record information into a prompt.

References:

Salesforce Developer Documentation onEinstein Trust Layer

Salesforce Security Documentation for AI andData Privacy

Event Logs in Salesforce capture detailed interaction data, including agent errors, triggered actions, and incomplete plans. These logs provide visibility into user-Agent interactions for troubleshooting performance issues. The Einstein Bot Analytics documentation highlights Event Logs as the primary source for auditing bot behavior and diagnosing issues like misconfigured actions or plan execution failures.

Plan Canvas (B) is for designing workflows, not auditing.

Agent Settings (C) control configuration but do not store interaction history.

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

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 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.

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.

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.

To address the ineffectiveness of a prompt template reported by a customer, the Salesforce AI Specialist should use the Prompt Builder Scorecard (Option B). This tool is explicitly designed to evaluate and monitor prompt templates against key criteria such as relevance, accuracy, safety, and grounding. By leveraging the scorecard, the specialist can systematically identify weaknesses in the template and make data-driven refinements. While monitoring and refining based on user feedback (Option A) is a general best practice, the Prompt Builder Scorecard is Salesforce’s recommended tool for structured evaluation, aligning with documented processes for maintaining prompt effectiveness. Changing the grounding object (Option C) without proper evaluation is reactive and does not address the root cause.

References:

Salesforce Einstein AI Specialist Certification Guide: Emphasizes using the Prompt Builder Scorecard to evaluate prompts and iterate based on results.

Trailhead Module: "Einstein for Developers" highlights the scorecard as a critical tool for assessing prompt performance.

Salesforce Help Documentation: Details the Scorecard’s role in evaluating prompts against predefined criteria.

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

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

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

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.

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.

WhenEinstein Copilot for Salesmatches similar opportunities, one of the primary criteria used is whether the opportunities have astatus of Closed Wonwithin thelast 12 months. This is a key factor in identifying successful patterns that could help close current deals. By focusing on opportunities that have been recently successful, Einstein Copilot can provide relevant insights and suggestions to sales reps to help them close similar deals faster.

For more information, reviewSalesforce Einstein Copilot documentationrelated toopportunity matchingand sales success patterns.

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

Service AI Groundingis the solution thatUniversal Containersshould use to ensureEinstein AIdrafts responses based on a well-defined data source. Service AI Grounding allows the AI model to be anchored in specific, relevant data sources, ensuring that any AI-generated responses (e.g., email replies) are accurate, relevant, and drawn from up-to-date information, such asKnowledge articlesorcases.

Given that UC has legacy fields and outdated data, Service AI Grounding ensures that only the valid and applicable data is used by Einstein AI to craft responses. This helps improve the relevance of responses and avoids inaccuracies caused by outdated or irrelevant fields.

Work SummariesandService Repliesare useful features but do not address the need for grounding AI outputs in specific, current data sources likeService AI Groundingdoes.

For more details, you can refer to Salesforce’sService AI Grounding documentationfor managing AI-generated content based on accurate data sources.

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)."

When buildingprompt templates in Prompt Builder, it is essential to consider how the Large Language Model (LLM) processes and generates outputs. Training the LLM with variouswriting styles, such as differentword choices, intensifiers, emojis, and punctuation, helps the model better understand diverse writing patterns and produce more contextually appropriate responses.

This approach enhances the flexibility and accuracy of the LLM when generating outputs for different use cases, as it is trained to recognize various writing conventions and styles. The prompt template should focus on providing rich context, and this stylistic variety helps improve the model’s adaptability.

Options A and B are less relevant because adding multiple-choice questions or role-playing scenarios doesn’t contribute significantly to improving the AI’s output generation quality within standard business contexts.

For more details, refer to Salesforce’sPrompt Builder documentationand LLM tuning strategies.

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

The User Utterances dashboard (Option A) is the correct functionality for analyzing user inputs, requests, and queries to identify patterns and trends. This dashboard aggregates and categorizes the natural language inputs (utterances) from users, enabling the AI Specialist to:

Identify Common Queries: Surface frequently asked questions or recurring issues.

Detect Intent Patterns: Understand how users phrase requests, which helps refine intent detection models.

Improve Bot Training: Highlight gaps in training data or misclassified utterances that require adjustment.

Why Other Options Are Incorrect:

B. Agent Event Logs dashboard: Focuses on agent activity (e.g., response times, resolved cases) rather than user input analysis.

C. AI Audit & Feedback Data dashboard: Tracks AI model performance, audit trails, and user feedback scores but does not directly analyze raw user utterances or queries.

References:

Salesforce Einstein AI Specialist Certification Guide: Emphasizes the User Utterances dashboard as the primary tool for analyzing user inputs to improve conversational AI.

Trailhead Module: "Einstein Bots Basics" highlights using the dashboard to refine bot training based on user interaction data.

Salesforce Help Documentation: Describes the User Utterances dashboard as critical for identifying trends in customer interactions.

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

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

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

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

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

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.

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

To implement a solution where users enter a sales order number and the system generates a summary, the AI Specialist should create ascreen flowto collect the sales order number and invoke the prompt template. The standard"Prompt Template" flow actioncan then be used to trigger the custom prompt, providing a summary of the sales order header and details.

Option B, creating a template-triggered prompt flow, is not necessary for this scenario because the requirement is to directly collect input through a screen flow.

Option C, using an autolaunched flow, would be inappropriate here because the solution requires user interaction (entering a sales order number), which is best suited to a screen flow.

Salesforce AI Specialist References:For further guidance on creating prompt templates with flows:https://help.salesforce.com/s/articleView?id=sf.prompt_template_flow_integration.htm

When a flow is invoked via a custom Agent action, its data access depends on the flow’s runtime configuration, not system mode by default. Salesforce flows can be configured to respect the running user’s permissions and sharing settings:

If the flow is set to "Run as the User Who Launched the Flow" (enabled in Flow Settings), it adheres to the user’s permissions, field-level security (FLS), and sharing rules.

Option C is incorrect because flows do not always run in system mode unless explicitly configured to do so.

Option A is misleading because "with sharing" is an Apex concept, not a flow setting. Flows use runtime settings like FLS and sharing enforcement.

References:

Salesforce Help: Flow Runtime and Security Context

Flow Settings: "Run with User Permission and Field-Level Security" ensures data access aligns with the user’s permissions.

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.

Salesforce has restrictions on which objects and related lists can be used for grounding prompt templates. This is likely due to security and privacy concerns related to user data.

While it might seem intuitive to use the User related list to provide context to the LLM, Salesforce prevents this to ensure that sensitive user information is not inadvertently exposed or misused.

Therefore, the AI Specialist needs to explore alternative ways to incorporate the necessary user information into the prompt template, perhaps by using other related objects or fields that are supported.

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).

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

Context of the Question

When an Agent (e.g., Agentforce or a similar generative AI assistant in Salesforce) cannot understand a user’s request or fails to locate the requested information, it should provide a coherent fallback.

Fallback / Error Handling in Agentforce

Preconfigured or “Fallback” Message: Typically, within the setup of any AI assistant, admins define a fallback or “failure” response. This message can be tailored to each action type or scenario (e.g., “No data found,” “Sorry, I didn’t get that—please try again,” etc.).

Why Not an Automatically Generated Error Message?(Option A) AI assistants rarely show raw system error messages; they generally display friendly, standardized messages.

Why Not a Purely Generic “Rephrase” Message?(Option B) Although an agent might prompt the user to rephrase, Salesforce best practices are to configure a fallback response or fallback action that aligns with the brand and the context. This is typically a “preconfigured message based on the action type.”

ConclusionBecause the assistant uses apreconfigured fallbackthat is set up in the environment (for example, in the agent’s or domain’s settings), the correct choice isOption C.

Salesforce AI Specialist References & Documents

Salesforce Pilot / Agentforce Setup DocumentationExplains how to configure a fallback or default message when the AI cannot fulfill a user’s request.

Salesforce AI Specialist Study GuideDetails best practices for AI-driven assistants and how fallback scenarios are handled with preconfigured messages.

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.