Salesforce-AI-Specialist試験問題集でPDF問題とテストエンジン
Salesforce-AI-Specialist問題集で必ず試験合格させる
Salesforce Salesforce-AI-Specialist 認定試験の出題範囲：
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質問 # 34
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.
正解：B
解説：
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.
質問 # 35
When configuring a prompt template, an AI Specialist previews the results of the prompt template they've written. They see two distinct text outputs: Resolution and Response.
Which information does the Resolution text provide?
- A. It shows the response from the LLM based on the sample record.
- B. It shows the full text that is sent to the Trust Layer.
- C. It shows which sensitive data is masked before it is sent to the LLM.
正解：A
解説：
When previewing a prompt template in Salesforce, the Resolution text provides the response from the LLM (Large Language Model) based on the data from a sample record. This output shows what the AI model generated in response to the prompt, giving the AI Specialist a chance to review and adjust the response before finalizing the template.
Option B is correct because Resolution displays the actual response generated by the LLM.
Option A refers to sending the text to the Trust Layer, but that's not what Resolution represents.
Option C relates to data masking, which is shown elsewhere, not under Resolution.
Reference:
Salesforce Prompt Builder Overview: https://help.salesforce.com/s/articleView?id=sf.prompt_builder_overview.htm
質問 # 36
An administrator wants to check the response of the Flex prompt
template they've built, but the preview button is greyed out.
What is the reason for this?
- A. The prompt has not been saved and activated,
- B. A merge field has not been inserted in the prompt.
- C. The records related to the prompt have not been selected.
正解：C
解説：
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
質問 # 37
Where should the AI Specialist go to add/update actions assigned to a copilot?
- A. Copilot Actions page or Global Actions
- B. Copilot Detail page, Global Actions, or the record page for the copilot action
- C. Copilot Actions page, the record page for the copilot action, or the Copilot Action Library tab
正解：C
解説：
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
質問 # 38
An AI Specialist is considering using a Field Generation prompt template type.
What should the AI Specialist check before creating the Field Generation prompt to ensure it is possible for the field to be enabled for generative AI?
- A. That the Lightning page layout where the field will reside has been upgraded to Dynamic Forms
- B. That the field chosen must be a rich text field with 255 characters or more.
- C. That the org is set to API version 59 or higher
正解：C
解説：
Before creating aField Generation prompt template, the AI Specialist must ensure that the Salesforceorg is set to API version 59 or higher. This version of the API introduces support for advanced generative AI features, such as enabling fields for generative AI outputs. This is a critical technical requirement for the Field Generation prompt template to function correctly.
* Option A(rich text field requirement) is not necessary for generative AI functionality.
* Option C(Dynamic Forms) does not impact the ability of a field to be generative AI-enabled, although it might enhance the user interface.
For more information, refer toSalesforce documentation on API versioningandField Generation templates.
質問 # 39
Universal Containers (UC) wants to use Flow to bring data from unified Data Cloud objects to prompt templates.
Which type of flow should UC use?
- A. Template-triggered prompt flow
- B. Unified-object linking flow
- C. Data Cloud-triggered flow
正解：A
解説：
In this scenario, Universal Containers wants to bring data from unified Data Cloud objects into prompt templates, and the best way to do that is through a Data 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 on Data Cloud-triggered flows and Data Cloud integrations with generative AI solutions.
質問 # 40
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. Request the Einstein Generative AI Audit Data from the Security section of the Setup menu.
- B. Enable the collection and storage of Einstein Generative AI Audit Data on the Einstein Feedback setup page.
- C. Use a Flow-based resource in Prompt Builder to debug the fields' merge values using Flow Debugger.
正解：A
解説：
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.
References:
* Salesforce Einstein Trust Layer Documentation: https://help.salesforce.com/s/articleView?id=sf.
einstein_trust_layer_audit.htm
質問 # 41
How should an organization use theEinstein Trust layer to audit,track, and view masked data?
- A. Access the audit trail in Setup and export all user-generated prompts.
- B. Utilize the audit trail that captures and stores all LLM submitted prompts in Data Cloud.
- C. In Setup, use Prompt Builder to send a prompt to the LLM requesting for the masked data.
正解：B
解説：
TheEinstein Trust Layeris designed to ensure transparency, compliance, and security for organizations leveraging Salesforce's AI and generative AI capabilities. Specifically, for auditing, tracking, and viewing masked data, organizations can utilize:
* Audit Trail in Data Cloud: Theaudit trailcaptures and stores all prompts submitted to large language models (LLMs), ensuring that sensitive or masked data interactions are logged. This allows organizations to monitor and audit all AI-generated outputs, ensuring that data handling complies with internal and regulatory guidelines. TheData Cloudprovides the infrastructure for managing and accessing this audit data.
* Why not B?UsingPrompt Builderin Setup to send prompts to the LLM is for creating and managing prompts, not for auditing or tracking data. It does not interact directly with the audit trail functionality.
* Why not C?Although the audit trail can be accessed in Setup, the user-generated prompts are primarily tracked in the Data Cloud for broader control, auditing, and analysis. Setup is not the primary tool for exporting or managing these audit logs.
More information on auditing AI interactions can be found in theSalesforce AI Trust Layerdocumentation, which outlines how organizations can manage and track generative AI interactions securely.
質問 # 42
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. Prompt Template User and Data Cloud Admin
- B. Data Cloud Admin and Prompt Template Manager
- C. Prompt Template Manager and Prompt Template User
正解：B
解説：
To allow a data scientist to view and manage models inEinstein Studioand create prompt templates in Prompt Builder, the AI Specialist should assign theData Cloud AdminandPrompt Template Manager permission 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 within Prompt 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
質問 # 43
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 following capabilities 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. Zero-Data Retention Policy
- B. Prompt Defense
- C. Data Masking
正解：A
解説：
Universal Containers (UC) has concerns about data privacy when using Salesforce's generative AI features, particularly around preventing third-party LLMs from accessing or retaining their data. The Zero-Data Retention Policy in the Einstein Trust Layer is designed to address these concerns by ensuring that:
No data is used for training or product improvements by third-party LLMs.
No data is retained outside 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 Defense and Data Masking are also security features, but they do not directly address the concerns related to third-party data access and retention.
Reference:
Salesforce Einstein Trust Layer Documentation: https://help.salesforce.com/s/articleView?id=sf.einstein_trust_layer.htm
質問 # 44
Universal Containers (UC) is implementing Einstein Generative AI to improve customer insights and interactions. UC needs audit and feedback data to be accessible for reporting purposes.
What is a consideration for this requirement?
- A. Storing this data requires a custom object for data to be configured.
- B. Storing this data requires Salesforce big objects.
- C. Storing this data requires Data Cloud to be provisioned.
正解：C
解説：
When implementingEinstein Generative AIfor improved customer insights and interactions, theData Cloud is 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/
質問 # 45
Universal Containers is planning a marketing email about products that most closely match a customer's expressed interests.
What should an AI Specialist recommend to generate this email?
- A. Standard email marketing template using Apex or flows for matching interest in products
- B. Custom sales email template which is grounded with interest and product information
- C. Standard email draft with Einstein and choose standard email template
正解：B
解説：
To generate an email about products that closely match a customer's expressed interests, an AI Specialist should recommend using acustom sales email templatethat isgrounded with interest and product information. This ensures that the email content is personalized based on the customer's preferences, increasing the relevance of the marketing message.
Using grounding ensures that the generative AI pulls the correct data related to customer interests and product matches, making the email more effective.
For more information, refer toSalesforce documentationon grounding AI-generated content and email personalization strategies.
質問 # 46
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. Request the Einstein Generative AI Audit Data from the Security section of the Setup menu.
- B. Enable the collection and storage of Einstein Generative AI Audit Data on the Einstein Feedback setup page.
- C. Use a Flow-based resource in Prompt Builder to debug the fields' merge values using Flow Debugger.
正解：A
解説：
To begin validating that the correct fields are being masked inEinstein Trust Layer, the AI Specialist should request theEinstein Generative AI Audit Datafrom theSecurity sectionof 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 Bis correct because it allows for the retrieval of audit data that can be used to validate data masking.
* Option A(Flow Debugger) andOption C(Einstein Feedback) do not relate to validating field masking in the context of theEinstein Trust Layer.
References:
* Salesforce Einstein Trust Layer Documentation:https://help.salesforce.com/s/articleView?id=sf.
einstein_trust_layer_audit.htm
質問 # 47
Which use case is best supported by Salesforce Einstein Copilot's capabilities?
- A. Enable data scientists to train predictiveAI models with historical CRM data using built-in machine learning capabilities
- B. Bring together a conversational interface for interacting with AI for all Salesforce users, such as developers and ecommerce retailers.
- C. Enable Salesforce admin users to create and train custom large language models (LLMs) using CRM data.
正解：B
解説：
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
質問 # 48
Universal Containers (UC) recently rolled out Einstein Generative capabilities and has created a custom prompt to summarize case records. Users have reported that the case summaries generated are not returning the appropriate information.
What is a possible explanation for the poor prompt performance?
- A. The Einstein Trust Layer is incorrectly configured.
- B. The prompt template version is incompatible with the chosen LLM.
- C. The data being used for grounding Is incorrect or incomplete.
正解：C
解説：
Poor prompt performance when generating case summaries is often due to the data used forgroundingbeing incorrect or incomplete. Grounding involves feeding accurate, relevant data to the AI so it can generate appropriate outputs. If the data source is incomplete or contains errors, the generated summaries will reflect that by being inaccurate or insufficient.
* Option B(prompt template incompatibility with the LLM) is unlikely because such incompatibility usually results in more technical failures, not poor content quality.
* Option C(Einstein Trust Layer misconfiguration) is focused on data security and auditing, not the quality of prompt responses.
For more information, refer toSalesforce documentation on grounding AI modelsand data quality best practices.
質問 # 49
What is the correct process to leverage Prompt Builder in a Salesforce org?
- A. Select the appropriate prompt template type to use, select one of Salesforce's standard prompts, determine the object to associate the prompt, select a record to validate against, and associate the prompt to an action.
- B. Enable the target object for generative prompting, develop the prompt within the prompt workspace, select records to fine-tune and ground the response, enable the Trust Layer, and associate the prompt to an action.
- C. Select the appropriate prompt template type to use, develop the prompt within the prompt workspace, select resources to dynamically insert CRM-derived grounding data, pick the model to use, and test and validate the generated responses.
正解：C
解説：
When using Prompt Builder in a Salesforce org, the correct process involves several important steps:
Select the appropriate prompt template type based on the use case.
Develop the prompt within the prompt workspace, where the template is created and customized.
Select CRM-derived grounding data to be dynamically inserted into the prompt, ensuring that the AI-generated responses are based on accurate and relevant data.
Pick the model to use for generating responses, either using Salesforce's built-in models or custom ones.
Test and validate the generated responses to ensure accuracy and effectiveness.
Option B is correct as it follows the proper steps for using Prompt Builder.
Option A and Option C do not capture the full process correctly.
Reference:
Salesforce Prompt Builder Documentation: https://help.salesforce.com/s/articleView?id=sf.prompt_builder_overview.htm
質問 # 50
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. Automatic grounding using Draft with Einstein feature
- B. Ground with Record Merge Fields
- C. Ground with Apex Merge Fields
正解：A
解説：
For Einstein Sales Emails to 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 is Ground 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 Fields ensure the generated emails are highly personalized using data like lead name, company, or other Salesforce fields directly from the records.
Apex Merge Fields are typically more suited for advanced, custom logic-driven scenarios but are not the most straightforward for this use case.
Automatic grounding using Draft with Einstein is a different feature where Einstein automatically drafts the email, but it does not specifically ground the content with record-specific data like Record Merge Fields.
Reference:
Salesforce Einstein Sales Emails Documentation: https://help.salesforce.com/s/articleView?id=release-notes.rn_einstein_sales_emails.htm
質問 # 51
What is the role of the large language model (LLM) in executing an Einstein Copilot Action?
- A. Identify the best matching actions and correct order of execution
- B. Determine a user's access and sort actions by priority to be executed
- C. Find similar requests and provideactions that need to be executed
正解：A
解説：
In Einstein Copilot, the role of the Large Language Model (LLM) is to analyze user inputs and identify the best matching actions that need to be executed. It uses natural language understanding to break down the user' s request and determine the correct sequence of actions that should be performed.
By doing so, the LLM ensures that the tasks and actions executed are contextually relevant and are performed in the proper order. This process provides a seamless, AI-enhanced experience for users by matching their requests to predefined Salesforce actions or flows.
The other options are incorrect because:
A mentions finding similar requests, which is not the primary role of the LLM in this context.
C focuses on access and sorting by priority, which is handled more by security models and governance than by the LLM.
References:
Salesforce Einstein Documentation on Einstein Copilot Actions
Salesforce AI Documentation on Large Language Models
質問 # 52
Universal Containers (UC) wants to improve the efficiency of addressing customer questions and reduce agent handling time with AI- generated responses. The agents should be able to leverage their existing knowledge base and identify whether the responses are coming from the large language model (LLM) or from Salesforce Knowledge.
Which step should UC take to meet this requirement?
- A. Turn on Service AI Grounding and Grounding with Knowledge.
- B. Turn on Service AI Grounding, Grounding with Case, and Service Replies.
- C. Turn on Service Replies, Service AI Grounding, and Grounding with Knowledge.
正解：C
解説：
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.
質問 # 53
Universal Containers implements Custom Copilot Actions to enhance its customer service operations.The development team needs tounderstand the core components of a Custom Copilot Action to ensure proper configuration and functionality.
What should the development team review in the Custom Copilot Action configuration to identify one of the core components of a Custom Copilot Action?
- A. Instructions
- B. Action Triggers
- C. Output Types
正解：C
解説：
Universal Containers is enhancing its customer service operations with Custom Copilot Actions. The development team needs to understand thecore componentsof a Custom Copilot Action to ensure proper configuration and functionality. One of these core components is theOutput Types.
* Core Components of a Custom Copilot Action:
* Instructions:
* Defines what the action should accomplish.
* Provides guidance to the AI assistant on how to execute the action.
* Input Parameters:
* The data inputs required for the action to perform its task.
* Specifies the parameters that users or systems need to provide.
* Output Types:
* Definition:Specifies the type of data the action will output after execution.
* Importance:Ensures that the Copilot and other components understand the format and structure of the action's results.
* Examples:Text, JSON, records, or other data structures.
* Action Reference:
* Points to the underlying implementation, such as an Apex class or Flow.
* Action Triggers:
* Conditions or events that initiate the action.
* Focus on Output Types:
* Relevance in Configuration:
* The development team must define the Output Types correctly to ensure that the action's results are properly processed and displayed by Copilot.
* Incorrect or undefined Output Types can lead to misinterpretation of data and failures in functionality.
Why Output Types are a Core Component:
* Integration with Copilot:
* Copilot relies on the Output Types to understand how to handle the data returned by the action.
* Data Consistency:
* Defines the structure and format of the output, ensuring consistent communication between the action and Copilot.
* User Experience:
* Proper Output Types ensure that users receive the expected results in an understandable format.
Why Other Options are Less Suitable:
* Option A (Instructions):
* While Instructions are a core component, the question asks for what should be reviewed in the configuration to identifyone of the core components.
* In this context, reviewingOutput Typesis more critical to ensuring proper configuration and functionality.
* Option C (Action Triggers):
* Action Triggers are important but are not always considered a core component within the configuration of a Custom Copilot Action.
* Triggers often relate to when an action is initiated rather than the configuration of the action itself.
References:
* Salesforce AI Specialist Documentation -Custom Copilot Actions:
* Details the components and configuration of Custom Copilot Actions.
* Salesforce Help -Defining Output Types in Custom Actions:
* Explains the importance of Output Types and how to configure them.
* Salesforce Trailhead -Building Custom Copilot Actions:
* Provides a hands-on approach to creating and configuring Custom Copilot Actions, highlighting key components.
質問 # 54
An AI Specialist needs to create a Sales Email with a custom prompt template. They need to ground on the following data.
Opportunity Products Events near the customer Tone and voice examples
How should the AI Specialist obtain related items?
- A. Utilize a standard email template and manually insert the required data fields.
- B. Create a flex template that takes the records in question as inputs.
- C. Call prompt initiated flow to fetch and ground the required data.
正解：C
解説：
To ground a sales email onOpportunity Products, Events near the customer, and Tone and voice examples, the AI Specialist should use aprompt-initiated flow. This flow can dynamically fetch the necessary data from related records in Salesforce and ground the generative AI output with contextually accurate information.
* Option B (flex template)does not provide the ability to fetch dynamic data from Salesforce records automatically.
* Option C (manual insertion)would not allow for the dynamic and automated grounding of data required for custom prompts.
Refer toSalesforce documentation on flowsand grounding for more details on integrating data into custom prompt templates.
質問 # 55
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