Salesforce-AI-Specialist by Salesforce Actual Free Exam Questions And Answers [UPDATED 2026]
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NEW QUESTION # 51
Universal Containers Is Interested In Improving the sales operation efficiency by analyzing their data using Al-powered predictions in Einstein Studio.
Which use case works for this scenario?
- A. Predict customer sentiment toward a promotion message.
- B. Predict most popular products from new product catalog.
- C. Predict customer lifetime value of an account.
Answer: C
Explanation:
For improvingsales operations efficiency,Einstein Studiois ideal for creating AI-powered models that can predict outcomes based on data. One of the most valuable use cases is predictingcustomer lifetime value, which helps sales teams focus on high-value accounts and make more informed decisions.Customer lifetime value (CLV)predictions can optimize strategies around customer retention, cross-selling, and long-term engagement.
* Option Bis the correct choice as predicting customer lifetime value is a well-established use case for AI in sales.
* Option A(customer sentiment) is typically handled through NLP models, whileOption C(product popularity) is more of a marketing analysis use case.
References:
* Salesforce Einstein Studio Use Case Overview:https://help.salesforce.com/s/articleView?id=sf.
einstein_studio_overview
NEW QUESTION # 52
Universal Containers (UC) wants to assess Salesforce's generative features but has concerns over its company data being exposed to third- party large language models (LLMs). Specifically, UC wants the followingcapabilities to be part of Einstein's generative AI service.
No data is used for LLM training or product improvements by third- party LLMs.
No data is retained outside of UC's Salesforce org.
The data sent cannot be accessed by the LLM provider.
Which property of the Einstein Trust Layer should the AI Specialist highlight to UC that addresses these requirements?
- A. Data Masking
- B. Zero-Data Retention Policy
- C. Prompt Defense
Answer: B
Explanation:
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
NEW QUESTION # 53
Which configuration must an AI Specialist complete for users to access generative Al-enabled fields in the Salesforce mobile app?
- A. Enable Mobile Prompt Responses.
- B. Enable Mobile Generative AI.
- C. Enable Dynamic Forms on Mobile.
Answer: C
Explanation:
* 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.
NEW QUESTION # 54
An Al Specialist is creating a custom action for Agentforce.
Which setting should the AI Specialist test and iterate on to ensure the action performs as expected?
- A. Action Input
- B. Action Instructions
- C. Action Name
Answer: B
Explanation:
To ensure a custom action in Agentforce performs as expected, the AI Specialist must focus on Action Instructions. Here's why:
* Action Instructions define the logic, parameters, and steps the AI should follow to execute the action.
They include:
* How input data is processed.
* API calls or Apex invocations.
* Conditional logic (e.g., decision trees).Testing and iterating on these instructions ensures alignment with the intended workflow. For example, incorrect API endpoint references or misconfigured parameters in the instructions will cause failures.
* Action Input (Option A) refers to the data provided to the action. While validating input formats is important, inputs are static once defined. The primary issue lies in whether the instructions correctly use the inputs.
* Action Name (Option B) is a descriptive label and does not affect functionality.
Salesforce Documentation Support:
* Salesforce Einstein Bots & Custom Actions Guide highlights that Action Instructions are where the
"core logic" resides, requiring rigorous testing (Source: Einstein Bots Developer Guide).
* Trailhead Module "Build Custom Actions for Einstein Bots" emphasizes refining instructions to handle edge cases and validate outputs (Source: Trailhead).
By iterating on Action Instructions, the AI Specialist ensures the action's logic, integrations, and error handling are robust.
NEW QUESTION # 55
An AI Specialist implements Einstein Sales Emails for a sales team. The team wants to send personalized follow-up emails to leads based on their interactions and data stored in Salesforce. The AI Specialist needs to configure the system to use the most accurate and up-to-date information for email generation.
Which grounding technique should the AI Specialist use?
- A. Ground with Record Merge Fields
- B. Ground with Apex Merge Fields
- C. Automatic grounding using Draft with Einstein feature
Answer: C
Explanation:
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
NEW QUESTION # 56
Universal Containers has a strict change management process that requires all possible configuration to be completed in a sandbox which will be deployed to production. The AI Specialist is tasked with setting up Work Summaries for Enhanced Messaging. Einstein Generative AI is already enabled in production, and the Einstein Work Summaries permission set is already available in production.
Which other configuration steps should the AI Specialist take in the sandbox that can be deployed to the production org?
- A. Create custom fields to store issue, Resolution, and Summary; create a Quick Action that updates these fields: and ado the Wrap up component to the Messaging session record page lavcut.
- B. From the Epstein setup menu, select Turn on Einstein: create custom fields to store Issue, Resolution, and Summary: create a Quick Action that updates these fields: and add the wrap up componert to the Messaging session record page layout.
- C. create custom fields to store Issue, Resolution, and Summary; create a Quick Action that updates these fields: add the Wrap Up component to the Messaging Session record paae layout: and create Permission Set Assignments for the intended Agents.
Answer: A
Explanation:
* 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.
NEW QUESTION # 57
Universal Containers is interested in using Call Explorer to quickly gain insights from meetings recorded by its sales team.
What should the AI Specialist be aware of before enabling this feature?
- A. Custom Call Explorer actions need to be built before it can be configured.
- B. Call Explorer operates independently of Salesforce Knowledge, requiring no prior setup.
- C. Call Explorer requires the Einstein Conversation Insights permission set to be enabled.
Answer: C
Explanation:
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.
NEW QUESTION # 58
Universal Containers (UC) plans to automatically populate the Description field on the Account object.
Which type of prompt template should UC use?
- A. Field Generation prompt template
- B. Sales Email prompt template
- C. Flex Prompt template
Answer: A
NEW QUESTION # 59
Universal Containers (UC) is discussing its AI strategy in an agile Scrum meeting.
Which business requirement would lead an AI Specialist to recommend connecting to an external foundational model via Einstein Studio (Model Builder)?
- A. UC wants to fine-tune model temperature.
- B. UC wants a model fine-tuned using company data.
- C. UC wants to change the frequency penalty of the model.
Answer: B
Explanation:
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.
NEW QUESTION # 60
Universal Containers (UC) has recently received an increased number of support cases. As a result, UC has hired more customer support reps and has started to assign some of the ongoing cases to newer reps.
Which generative AI solution should the new support reps use to understand the details of a case without reading through each case comment?
- A. Einstein Sales Summaries
- B. Einstein Copilot
- C. Einstein Work Summaries
Answer: C
Explanation:
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.
NEW QUESTION # 61
A data scientist needs to view and manage models in Einstein Studio. The data scientist also needs to create prompt templates in Prompt Builder.
Which permission sets should an AI Specialist assign to the data scientist?
- A. Data Cloud Admin and Prompt Template Manager
- B. Prompt Template Manager and Prompt Template User
- C. Prompt Template User and Data Cloud Admin
Answer: A
Explanation:
To allow a data scientist to view and manage models in Einstein Studio and create prompt templates in Prompt Builder, the AI Specialist should assign the Data Cloud Admin and Prompt Template Manager permission sets.
Data Cloud Admin provides access to manage and oversee models within Einstein Studio.
Prompt Template Manager gives the user the ability to create and manage prompt templates within Prompt Builder.
Option A is correct because it assigns the necessary permissions for both managing models and creating prompt templates.
Option B and Option C are incorrect as they do not provide the correct combination of permissions for managing models and building prompts.
Reference:
Salesforce Permissions Documentation: https://help.salesforce.com/s/articleView?id=sf.perm_sets_overview.htm
NEW QUESTION # 62
An AI Specialist configured Data Masking within the Einstein Trust Layer.
How should the AI Specialist begin validating that the correct fields are being masked?
- A. Use a Flow-based resource in Prompt Builder to debug the fields' merge values using Flow Debugger.
- B. Enable the collection and storage of Einstein Generative AI Audit Data on the Einstein Feedback setup page.
- C. Request the Einstein Generative AI Audit Data from the Security section of the Setup menu.
Answer: C
Explanation:
To begin validating that the correct fields are being masked in Einstein Trust Layer, the AI Specialist should request the Einstein Generative AI Audit Data from the Security section of the Salesforce Setup menu. This audit data allows the AI Specialist to see how data is being processed, including which fields are being masked, providing transparency and validation that the configuration is working as expected.
Option B is correct because it allows for the retrieval of audit data that can be used to validate data masking.
Option A (Flow Debugger) and Option C (Einstein Feedback) do not relate to validating field masking in the context of the Einstein Trust Layer.
Reference:
Salesforce Einstein Trust Layer Documentation: https://help.salesforce.com/s/articleView?id=sf.einstein_trust_layer_audit.htm
NEW QUESTION # 63
How does the Einstein Trust Layer ensure that sensitive data is protected while generating useful and meaningful responses?
- A. Masked data will be de-masked during response journey.
- B. Responses that do not meet the relevance threshold will be automatically rejected.
- C. Masked data will be de-masked during request journey.
Answer: A
Explanation:
The Einstein Trust Layer ensures that sensitive data is protected while generating useful and meaningful responses by masking sensitive data before it is sent to the Large Language Model (LLM) and then de-masking it during the response journey.
How It Works:
Data Masking in the Request Journey:
Sensitive Data Identification: Before sending the prompt to the LLM, the Einstein Trust Layer scans the input for sensitive data, such as personally identifiable information (PII), confidential business information, or any other data deemed sensitive.
Masking Sensitive Data: Identified sensitive data is replaced with placeholders or masks. This ensures that the LLM does not receive any raw sensitive information, thereby protecting it from potential exposure.
Processing by the LLM:
Masked Input: The LLM processes the masked prompt and generates a response based on the masked data.
No Exposure of Sensitive Data: Since the LLM never receives the actual sensitive data, there is no risk of it inadvertently including that data in its output.
De-masking in the Response Journey:
Re-insertion of Sensitive Data: After the LLM generates a response, the Einstein Trust Layer replaces the placeholders in the response with the original sensitive data.
Providing Meaningful Responses: This de-masking process ensures that the final response is both meaningful and complete, including the necessary sensitive information where appropriate.
Maintaining Data Security: At no point is the sensitive data exposed to the LLM or any unintended recipients, maintaining data security and compliance.
Why Option A is Correct:
De-masking During Response Journey: The de-masking process occurs after the LLM has generated its response, ensuring that sensitive data is only reintroduced into the output at the final stage, securely and appropriately.
Balancing Security and Utility: This approach allows the system to generate useful and meaningful responses that include necessary sensitive information without compromising data security.
Why Options B and C are Incorrect:
Option B (Masked data will be de-masked during request journey):
Incorrect Process: De-masking during the request journey would expose sensitive data before it reaches the LLM, defeating the purpose of masking and compromising data security.
Option C (Responses that do not meet the relevance threshold will be automatically rejected):
Irrelevant to Data Protection: While the Einstein Trust Layer does enforce relevance thresholds to filter out inappropriate or irrelevant responses, this mechanism does not directly relate to the protection of sensitive data. It addresses response quality rather than data security.
Reference:
Salesforce AI Specialist Documentation - Einstein Trust Layer Overview:
Explains how the Trust Layer masks sensitive data in prompts and re-inserts it after LLM processing to protect data privacy.
Salesforce Help - Data Masking and De-masking Process:
Details the masking of sensitive data before sending to the LLM and the de-masking process during the response journey.
Salesforce AI Specialist Exam Guide - Security and Compliance in AI:
Outlines the importance of data protection mechanisms like the Einstein Trust Layer in AI implementations.
Conclusion:
The Einstein Trust Layer ensures sensitive data is protected by masking it before sending any prompts to the LLM and then de-masking it during the response journey. This process allows Salesforce to generate useful and meaningful responses that include necessary sensitive information without exposing that data during the AI processing, thereby maintaining data security and compliance.
NEW QUESTION # 64
Universal Containers (UC) wants to enable its sales team to use Al to suggest recommended products from its catalog.
Which type of prompt template should UC use?
- A. Record summary prompt template
- B. Flex prompt template
- C. Email generation prompt template
Answer: B
Explanation:
Universal Containers (UC) wants to enable its sales team to leverage AI to recommend products from its catalog. The best option for this use case is a Flex prompt template.
A Flex prompt template is designed to provide flexible, customizable AI-driven recommendations or responses based on specific data points, such as product information, customer needs, or sales history. This template type allows the AI to consider various inputs and parameters, making it ideal for generating product recommendations dynamically.
In contrast:
* A Record summary prompt template (Option A) is used to summarize data related to a specific record, such as generating a quick summary of a sales opportunity or account, but not for recommending products.
* An Email generation prompt template (Option B) is tailored for crafting email content and is not suitable for suggesting products based on a catalog.
Given the need for dynamic recommendations that pull from a product catalog and potentially other sales data, the Flex prompt template is the correct approach.
Salesforce References:
* Salesforce Prompt Templates Overview: https://help.salesforce.com/s/articleView?
id=000391407&type=1
* Flex Prompt Template Usage: https://developer.salesforce.com/docs/atlas.en-us.salesforce_ai.meta
/salesforce_ai/prompt_flex_template
NEW QUESTION # 65
Universal Containers has an active standard email prompt template that does not fully deliver on the business requirements.
Which steps should an AI Specialist take to use the content of the standard prompt email template in question andcustomize it to fully meet thebusiness requirements?
- A. Save as New Version and edit as needed.
- B. Clone the existing template and modify as needed.
- C. Save as New Template and edit as needed.
Answer: B
Explanation:
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.
NEW QUESTION # 66
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