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質問 # 42
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 Replies, Service AI Grounding, and Grounding with Knowledge.
- B. Turn on Service AI Grounding and Grounding with Knowledge.
- C. Turn on Service AI Grounding, Grounding with Case, and Service Replies.
正解：A
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 # 43
An administrator is responsible for ensuring the security and reliability of Universal Containers' (UC) CRM dat a. UC needs enhanced data protection and up-to-date AI capabilities. UC also needs to include relevant information from a Salesforce record to be merged with the prompt.
Which feature in the Einstein Trust Layer best supports UC's need?
- A. Zero-data retention policy
- B. Dynamic grounding with secure data retrieval
- C. Data masking
正解：B
解説：
Dynamic grounding with secure data retrieval is a key feature in Salesforce's Einstein Trust Layer, which provides enhanced data protection and ensures that AI-generated outputs are both accurate and securely sourced. This feature allows relevant Salesforce data to 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 masking refers to obscuring sensitive data for privacy purposes and is not related to merging Salesforce records into prompts.
Zero-data retention policy ensures 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.
Reference:
Salesforce Developer Documentation on Einstein Trust Layer
Salesforce Security Documentation for AI and Data Privacy
質問 # 44
What is the main purpose of Prompt Builder?
- A. A tool that enables companies to create reusable prompts for large language models (LLMs), bringing generative AI responses to their flow of work
- B. A tool within Salesforce offering real-time Al-powered suggestions and guidance to users, Improving productivity and decision-making.
- C. A tool for developers to use in Visual Studio Code that creates prompts for Apex programming, assisting developers in writing code more efficiently.
正解：A
解説：
Prompt Builderis designed to help organizations create and configure reusable prompts for large language models (LLMs). By integratinggenerative AIresponses into workflows,Prompt Builderenables customization of AI prompts that interact with Salesforce data and automate complex processes. This tool is especially useful for creating tailored and consistent AI-generated content in various business contexts, including customer service and sales.
* It is not a tool forApex programming(as in option A).
* It is also not limited to real-time suggestions as mentioned in option C. Instead, it provides a flexible way for companies to manage and customize how AI-driven responses are generated and used in their workflows.
References:
* Salesforce Prompt Builder Overview:https://help.salesforce.com/s/articleView?id=sf.prompt_builder.
htm
質問 # 45
Universal Containers wants to utilize Einstein for Sales to help sales reps reach their sales quotas by providing Al-generated plans containing guidance and steps for closing deals.
Which feature should the AI Specialist recommend to the sales team?
- A. Find Similar Deals
- B. Create Close Plan
- C. Create Account Plan
正解：B
解説：
The "Create Close Plan" feature is designed to help sales reps by providing AI-generated strategies and steps specifically 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 Reference:
For more information on using AI for sales, visit: https://help.salesforce.com/s/articleView?id=sf.einstein_for_sales_overview.htm
質問 # 46
Universal Containers implements Custom Copilot Actions to enhance its customer service operations. The development team needs to understand 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. Output Types
- B. Instructions
- C. Action Triggers
正解：B
解説：
Instructions: This is a core component of Custom Copilot Actions. Instructions tell the AI model what the action should do and how it should be executed. Clear and concise instructions are crucial for the action to function correctly and provide the expected outcome.
Let's look at why the other options are not the primary core component:
Output Types: While important for defining the kind of data the action produces, it's not the core defining element of the action itself.
Action Triggers: These determine when the action is initiated, but they don't define the core functionality of the action.
質問 # 47
In Model Playground, which hyperparameters of an existing
Salesforce-enabled foundational model can an AI Specialist change?
- A. Temperature, Top-k sampling, Presence Penalty
- B. Temperature, Frequency Penalty, Presence Penalty
- C. Temperature, Frequency Penalty, Output Tokens
正解：B
解説：
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.
質問 # 48
What is the main purpose of Prompt Builder?
- A. A tool that enables companies to create reusable prompts for large language models (LLMs), bringing generative AI responses to their flow of work
- B. A tool within Salesforce offering real-time Al-powered suggestions and guidance to users, Improving productivity and decision-making.
- C. A tool for developers to use in Visual Studio Code that creates prompts for Apex programming, assisting developers in writing code more efficiently.
正解：A
解説：
Prompt Builder is designed to help organizations create and configure reusable prompts for large language models (LLMs). By integrating generative AI responses into workflows, Prompt Builder enables customization of AI prompts that interact with Salesforce data and automate complex processes. This tool is especially useful for creating tailored and consistent AI-generated content in various business contexts, including customer service and sales.
It is not a tool for Apex programming (as in option A).
It is also not limited to real-time suggestions as mentioned in option C. Instead, it provides a flexible way for companies to manage and customize how AI-driven responses are generated and used in their workflows.
Reference:
Salesforce Prompt Builder Overview: https://help.salesforce.com/s/articleView?id=sf.prompt_builder.htm
質問 # 49
Universal Containers' service team wants to customize the standard case summary response from Einstein Copilot.
What should the AI Specialist do to achieve this?
- A. Customize the standard Record Summary template for the Case object,
- B. Summarize the Case with a standard copilot action.
- C. Create a custom Record Summary prompt template for the Case object.
正解：C
解説：
To customize thecase summary responsefromEinstein Copilot, the AI Specialist should create acustom Record Summary prompt templatefor theCase object. This allows Universal Containers to tailor the way case data is summarized, ensuring the output aligns with specific business requirements or user preferences.
* Option A(customizing the standard Record Summary template) does not provide the flexibility required for deep customization.
* Option B(standard Copilot action) won't allow customization; it will only use default settings.
Refer toSalesforce Prompt Builder documentationfor guidance on creating custom templates for record summaries.
質問 # 50
What is an AI Specialist able to do when the "Enrich event logs with conversation data" setting in Einstein Copilot is enabled?
- A. Generate details reports on all Copilot conversations over any time period.
- B. View the user click path that led to each copilot action.
- C. View session data including user Input and copilot responses for sessions over the past 7 days.
正解：C
解説：
When the "Enrich event logs with conversation data" setting is enabled in Einstein Copilot, it allows an AI Specialist or admin to view session data, including both the user input and copilot responses from 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 Reference:
You can refer to this documentation for further insights: https://help.salesforce.com/s/articleView?id=sf.einstein_copilot_event_logging.htm
質問 # 51
A Salesforce Administrator is exploring the capabilities of Einstein Copilot to enhance user interaction within their organization. They are particularly interested in how Einstein Copilot processes user requests and the mechanism it employs to deliver responses. The administrator is evaluating whether Einstein Copilot directly interfaces with a large language model (LLM) to fetch and display responses to user inquiries, facilitating a broad range of requests from users.
How does Einstein Copilot handle user requests In Salesforce?
- A. Einstein Copilot will trigger a flow that utilizes a prompt template to generate the message.
- B. Einstein Copilot will perform an HTTP callout to an LLM provider.
- C. Einstein Copilot analyzes the user's request and LLM technology is used to generate and display the appropriate response.
正解：C
解説：
Einstein Copilot is designed to enhance user interaction within Salesforce by leveraging Large Language Models (LLMs) to process and respond to user inquiries. When a user submits a request, Einstein Copilot analyzes the input using natural language processing techniques. It then utilizes LLM technology to generate an appropriate and contextually relevant response, which is displayed directly to the user within the Salesforce interface.
Option C accurately describes this process. Einstein Copilot does not necessarily trigger a flow (Option A) or perform an HTTP callout to an LLM provider (Option B) for each user request. Instead, it integrates LLM capabilities to provide immediate and intelligent responses, facilitating a broad range of user requests.
Reference:
Salesforce AI Specialist Documentation - Einstein Copilot Overview: Details how Einstein Copilot employs LLMs to interpret user inputs and generate responses within the Salesforce ecosystem.
Salesforce Help - How Einstein Copilot Works: Explains the underlying mechanisms of how Einstein Copilot processes user requests using AI technologies.
質問 # 52
Anadministrator is responsible for ensuring the security and reliability of Universal Containers' (UC) CRM data. UC needs enhanced data protection and up-to-date AI capabilities. UC also needs to include relevant information from a Salesforce record to be merged with the prompt.
Which feature in the Einstein Trust Layer best supports UC's need?
- A. Zero-data retention policy
- B. Dynamic grounding with secure data retrieval
- C. Data masking
正解：B
解説：
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
質問 # 53
An Al Specialist is tasked with configuring a generative model to create personalized sales emails using customer data stored in Salesforce. The AI Specialist has already fine-tuned a large language model (LLM) on the OpenAI platform. Security and data privacy are critical concerns for the client.
How should the AI Specialist integrate the custom LLM into Salesforce?
- A. Create an application of the custom LLM and embed it in Sales Cloud via iFrame.
- B. Enable model endpoint on OpenAl and make callouts to the model to generate emails.
- C. Add the fine-tuned LLM in Einstein Studio Model Builder.
正解：C
解説：
Since security and data privacy are critical, the best option for the AI Specialist is to integrate the fine-tuned LLM (Large Language Model)into Salesforce by adding it toEinstein Studio Model Builder.Einstein Studioallows organizations to bring their own AI models (BYOM), ensuring the model is securely managed within Salesforce's environment, adhering to data privacy standards.
* Option A(embedding via iFrame) is less secure and doesn't integrate deeply with Salesforce's data and security models.
* Option C(making callouts to OpenAI) raises concerns about data privacy, as sensitive Salesforce data would be sent to an external system.
Einstein Studioprovides the most secure and seamless way to integrate custom AI models while maintaining control over data privacy and compliance. More details can be found inSalesforce's Einstein Studio documentationon integrating external models.
質問 # 54
Where should the AI Specialist go to add/update actions assigned to a copilot?
- A. Copilot Detail page, Global Actions, or the record page for the copilot action
- B. Copilot Actions page or Global Actions
- 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 # 55
A service agent is looking at a custom object that stores travel information. They recently received a weather alert and now need to cancel flights for the customers that are related with this itinerary. The service agent needs to review the Knowledge articles about canceling and rebooking the customer flights.
Which Einstein Copilot capability helps the agent accomplish this?
- A. Execute tasks based on available actions, answering questions using information from accessible Knowledge articles.
- B. Invoke a flow which makes a call to external data to create a Knowledge article.
- C. Generate a Knowledge article based off the prompts that the agent enters to create steps to cancel flights.
正解：A
解説：
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
質問 # 56
Universal Containers is using Einstein Copilot for Sales to find similar opportunities to help close deals faster.
The team wants to understand the criteria used by the copilot to match opportunities.
What is one criteria that Einstein Copilot for Sales uses to match similar opportunities?
- A. Matched opportunities have a status of Closed Won from last 12 months.
- B. Matched opportunities are limited to the same account.
- C. Matched opportunities were created in the last 12 months.
正解：A
解説：
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 matching and sales success patterns.
質問 # 57
Universal Containers wants touse an external large languagemodel (LLM) in Prompt Builder.
What should an AI Specialist recommend?
- A. Use Flow and External Services to bring data from an external LLM.
- B. Use Apex to connect to an external LLM and ground the prompt.
- C. Use BYO-LLM functionality in Einstein Studio,
正解：C
解説：
Bring Your Own Large Language Model (BYO-LLM)functionality inEinstein Studioallows organizations to integrate and use external large language models (LLMs) within the Salesforce ecosystem.Universal Containerscan leverage this feature to connect and ground prompts with external LLMs, allowing for custom AI model use cases and seamless integration with Salesforce data.
* Option Bis the correct choice asEinstein Studioprovides a built-in feature to work with external models.
* Option Asuggests using Apex, butBYO-LLMfunctionality offers a more streamlined solution.
* Option Cfocuses onFlow and External Services, which is more about data integration and isn't ideal for working with LLMs.
References:
* Salesforce Einstein Studio BYO-LLM Documentation:https://help.salesforce.com/s/articleView?id=sf.
einstein_studio_llm.htm
質問 # 58
An AI Specialist built a Field Generation prompt template that worked for many records, but users are reporting random failures with token limit errors.
What is the cause of the random nature of this error?
- A. The number of tokens that can be processed by the LLM varies with total user demand.
- B. The number of tokens generated by the dynamic nature of the prompt template will vary by record.
- C. The template type needs to be switched to Flex to accommodate the variable amount of tokens generated by the prompt grounding.
正解：B
解説：
The reason behind the token limit errors lies in the dynamic nature of the prompt template used in Field Generation. In Salesforce's AI generative models, each prompt and its corresponding output are subject to a token limit, which encompasses both the input and output of the large language model (LLM). Since the prompt template dynamically adjusts based on the specific data of each record, the number of tokens varies per record. Some records may generate longer outputs based on their data attributes, pushing the token count beyond the allowable limit for the LLM, resulting in token limit errors.
This behavior explains why users experience random failures-it is dependent on the specific data used in each case. For certain records, the combined input and output may fall within the token limit, while for others, it may exceed it. This variation is intrinsic to how dynamic templates interact with large language models.
Salesforce provides guidance in their documentation, stating that prompt template design should take into account token limits and suggests testing with varied records to avoid such random errors. It does not mention switching to Flex template type as a solution, nor does it suggest that token limits fluctuate with user demand.
Token limits are a constant defined by the model itself, independent of external user load.
References:
* Salesforce Developer Documentation onToken Limits for Generative AI Models
* Salesforce AI Best Practices on Prompt Design (Trailhead or Salesforce blog resources)
質問 # 59
Universal Containers wants to be able to detect with a high level confidence if content generated by a large language model (LLM) contains toxic language.
Which action should an Al Specialist take in the Trust Layer to confirm toxicity is being appropriately managed?
- A. Create a flow that sends an email to a specified address each time the toxicity score from the response exceeds a predefined threshold.
- B. Access the Toxicity Detection log in Setup and export all entries where isToxicityDetected is true.
- C. Create a Trust Layer audit report within Data Cloud that uses a toxicity detector type filter to display toxic responses and their respective scores.
正解：C
解説：
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, allowing Universal 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
質問 # 60
Leadership needs to populate a dynamic form field with a summary or description created by a large language model (LLM) to facilitate more productive conversations with customers. Leadership also wants to keep a human in the loop to be considered in their AI strategy.
Which prompt template type should the AI Specialist recommend?
- A. Sales Email
- B. Field Generation
- C. Record Summary
正解：B
解説：
The correct answer is Field Generation because 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 the Field Generation prompt 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
質問 # 61
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. Email generation prompt template
- B. Flex prompt template
- C. Record summary prompt template
正解：B
解説：
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
質問 # 62
What is best practice when refining Einstein Copilot custom action instructions?
- A. Specify the persona who will request the action.
- B. Use consistent introductory phrases and verbs across multiple action instructions.
- C. Provide examples of user messages that are expected to trigger the action.
正解：C
解説：
When refiningEinstein Copilot custom action instructions, it is considered best practice toprovide examples of user messagesthat are expected to trigger the action. This helps ensure that the custom action understands a variety of user inputs and can effectively respond to the intent behind the messages.
* Option B(consistent phrases) can improve clarity but does not directly refine the triggering logic.
* Option C(specifying a persona) is not as crucial as giving examples that illustrate how users will interact with the custom action.
For more details, refer toSalesforce's Einstein Copilot documentationon building and refining custom actions.
質問 # 63
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. Enable the collection and storage of Einstein Generative AI Audit Data on the Einstein Feedback setup page.
- B. Request the Einstein Generative AI Audit Data from the Security section of the Setup menu.
- C. Use a Flow-based resource in Prompt Builder to debug the fields' merge values using Flow Debugger.
正解：B
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 # 64
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. Unified-object linking flow
- B. Data Cloud-triggered flow
- C. Template-triggered prompt flow
正解：B
解説：
In this scenario,Universal Containerswants to bring data fromunified Data Cloud objectsinto prompt templates, and the best way to do that is through aData Cloud-triggered flow. This type of flow is specifically designed to trigger actions based on data changes within Salesforce Data Cloud objects.
Data Cloud-triggered flows can listen for changes in the unified data model and automatically bring relevant data into the system, making it available for prompt templates. This ensures that the data is both real-time and up-to-date when used in generative AI contexts.
For more detailed guidance, refer to Salesforce documentation onData Cloud-triggered flowsandData Cloud integrationswith generative AI solutions.
質問 # 65
Universal Containers (UC) has a legacy system that needs to integrate with Salesforce. UC wishes to create a digest of account action plans using the generative API feature.
Which API service should UC use to meet this requirement?
- A. Metadata API
- B. SOAP API
- C. REST API
正解：C
解説：
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.
質問 # 66
Universal Containers' current AI data masking rules do not align with organizational privacy and security policies and requirements.
What should an AI Specialist recommend to resolve the issue?
- A. Configure data masking in the Einstein Trust Layer setup.
- B. Add new data masking rules in LLM setup.
- C. Enable data masking for sandbox refreshes.
正解：A
解説：
When Universal Containers' AI data masking rules do not meet organizational privacy and security standards, the AI Specialist should configure the data masking rules within the Einstein Trust Layer. The Einstein Trust Layer provides a secure and compliant environment where sensitive data can be masked or anonymized to adhere to privacy policies and regulations.
Option A, enabling data masking for sandbox refreshes, is related to sandbox environments, which are separate from how AI interacts with production data.
Option C, adding masking rules in the LLM setup, is not appropriate because data masking is managed through the Einstein Trust Layer, not the LLM configuration.
The Einstein Trust Layer allows for more granular control over what data is exposed to the AI model and ensures compliance with privacy regulations.
Salesforce AI Specialist Reference:
For more information, refer to: https://help.salesforce.com/s/articleView?id=sf.einstein_trust_layer_data_masking.htm
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