最適な練習法にはSalesforce Salesforce-AI-Specialist問題集で素晴らしいSalesforce-AI-Specialist試験問題PDF
更新された検証済みの合格させるSalesforce-AI-Specialist試験リアル問題と解答
Salesforce Salesforce-AI-Specialist 認定試験の出題範囲：
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質問 # 15
Universal Containers (UC) wants to create a new Sales Email prompt template in Prompt Builder using the "Save As" function. However, UC notices that the new template produces different results compared to the standard Sales Email prompt due to missing hyperparameters.
What should UC do to ensure the new prompt template produces results comparable to the standard Sales Email prompts?
- A. Revert to using the standard template without modifications.
- B. Use Model Playground to create a model configuration with the specified parameters.
- C. Manually add the hyperparameters to the new template.
正解：C
解説：
When Universal Containers creates a new Sales Email prompt template using the "Save As" function, missing hyperparameters can result in different outputs. To ensure the new prompt produces comparable results to the standard Sales Email prompt, the AI Specialist should manually add the necessary hyperparameters to the new template.
Hyperparameters like Temperature, Frequency Penalty, and Presence Penalty directly affect how the AI generates responses. Ensuring that these are consistent with the standard template will result in similar outputs.
Option A (Model Playground) is not necessary here, as it focuses on fine-tuning models, not adjusting templates directly.
Option C (Reverting to the standard template) does not solve the issue of customizing the prompt template.
For more information, refer to Prompt Builder documentation on configuring hyperparameters in custom templates.
質問 # 16
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. Template-triggered prompt flow
- C. Data Cloud-triggered flow
正解：C
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 # 17
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. Use a Flow-based resource in Prompt Builder to debug the fields' merge values using Flow Debugger.
- C. Request the Einstein Generative AI Audit Data from the Security section of the Setup menu.
正解：C
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.
Reference:
Salesforce Einstein Trust Layer Documentation: https://help.salesforce.com/s/articleView?id=sf.einstein_trust_layer_audit.htm
質問 # 18
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. Enable model endpoint on OpenAl and make callouts to the model to generate emails.
- B. Create an application of the custom LLM and embed it in Sales Cloud via iFrame.
- 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 # 19
Universal Containers needs a tool that can analyze voice and video call records to provide insights on competitor mentions, coaching opportunities, and other key information. Thegoal is to enhance the team'sperformance by identifying areas for improvement and competitive intelligence.
Which feature provides insights about competitor mentions and coaching opportunities?
- A. Call Explorer
- B. Einstein Sales Insights
- C. Call Summaries
正解：A
解説：
For analyzing voice and video call records to gain insights into competitor mentions, coaching opportunities, and other key information,Call Exploreris the most suitable feature.Call Explorer, a part ofEinstein Conversation Insights, enables sales teams to analyze calls, detect patterns, and identify areas where improvements can be made. It uses natural language processing (NLP) to extract insights, including competitor mentionsand moments for coaching. These insights are vital for improving sales performance by providing a clear understanding of the interactions during calls.
* Call Summariesoffer a quick overview of a call but do not delve deep into competitor mentions or coaching insights.
* Einstein Sales Insightsfocuses more on pipeline and forecasting insights rather than call-based analysis.
References:
* Salesforce Einstein Conversation Insights Documentation:https://help.salesforce.com/s/articleView?
id=einstein_conversation_insights.htm
質問 # 20
Universal Containers (UC) wants to use the Draft with Einstein feature in Sales Cloud to create a personalized introduction email.
After creating a proposed draft email, which predefined adjustment should UC choose to revise the draft with a more casual tone?
- A. Enhance Friendliness
- B. Optimize for Clarity
- C. Make Less Formal
正解：C
解説：
WhenUniversal Containersuses theDraft with Einsteinfeature inSales Cloudto create a personalized email, the predefined adjustment toMake Less Formalis the correct option to revise the draft with a more casual tone. This option adjusts the wording of the draft to sound less formal, making the communication more approachable while still maintaining professionalism.
* Enhance Friendlinesswould make the tone more positive, but not necessarily more casual.
* Optimize for Clarityfocuses on making the draft clearer but doesn't adjust the tone.
For more details, seeSalesforce documentation on Einstein-generated email draftsand tone adjustments.
質問 # 21
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. Record Summary
- C. Field Generation
正解：C
解説：
The correct answer isField Generationbecause this template type is designed to dynamically populate form fields with content generated by a large language model (LLM). In this scenario, leadership wants a dynamic form field that contains a summary or description generated by AI to aid customer interactions. Additionally, they want to keep a human in the loop, meaning the generated content will likely be reviewed or edited by a person before it's finalized, which aligns with theField Generationprompt template.
* Field Generation: This prompt type allows you to generate content for specific fields in Salesforce, leveraging large language models to create dynamic and contextual information. It ensures that AI content is available within the record where needed, but it allows human oversight or review, supporting the "human-in-the-loop" strategy.
* Sales Email: This prompt type is mainly used for generating email content for outreach or responses, which doesn't align directly with populating fields in a form.
* Record Summary: While this option might seem close, it is typically used to summarize entire records for high-level insights rather than filling specific fields with dynamic content based on AI generation.
Salesforce AI Specialist References:
* You can explore more about these prompt templates and AI capabilities through Salesforce documentation and official resources on Prompt Builder:https://help.salesforce.com/s/articleView?id=sf.
prompt_builder_templates_overview.htm
質問 # 22
Before activating a custom copilot action, an AI Specialist would like is to understand multiple real-world user utterances to ensure the action being selected appropriately.
Which tool should the AI Specialist recommend?
- A. Copilot Builder
- B. Model Playground
- C. Einstein Copilot
正解：A
解説：
To understand multiple real-world user utterances and ensure the correct action is selected before activating a custom copilot action, the recommended tool is Copilot Builder. This tool allows AI Specialists to design and test conversational actions in response to user inputs, helping ensure the copilot can accurately handle different user queries and phrases. Copilot Builder provides the ability to test, refine, and improve actions based on real-world utterances.
Option C is correct as Copilot Builder is designed for configuring and testing conversational actions.
Option A (Model Playground) is used for testing models, not user utterances.
Option B (Einstein Copilot) refers to the conversational interface but isn't the right tool for designing and testing actions.
Reference:
Salesforce Copilot Builder Overview: https://help.salesforce.com/s/articleView?id=sf.einstein_copilot_builder.htm
質問 # 23
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. Automatic grounding using Draft with Einstein feature
- C. Ground with Apex Merge Fields
正解：B
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 # 24
A support team handles a high volume of chat interactions and needs a solution to provide quick, relevant responses to customer inquiries.
Responses must be grounded in the organization's knowledge base to maintain consistency and accuracy.
Which feature in Einstein for Service should the support team use?
- A. Einstein Service Replies
- B. Einstein Knowledge Recommendations
- C. Einstein Reply Recommendations
正解：C
解説：
The support team should use Einstein Reply Recommendations to provide quick, relevant responses to customer inquiries that are grounded in the organization's knowledge base. This feature leverages AI to recommend accurate and consistent replies based on historical interactions and the knowledge stored in the system, ensuring that responses are aligned with organizational standards.
Einstein Service Replies (Option A) is focused on generating replies but doesn't have the same emphasis on grounding responses in the knowledge base.
Einstein Knowledge Recommendations (Option C) suggests knowledge articles to agents, which is more about assisting the agent in finding relevant articles than providing automated or AI-generated responses to customers.
Salesforce AI Specialist Reference:
For more information on Einstein Reply Recommendations: https://help.salesforce.com/s/articleView?id=sf.einstein_reply_recommendations_overview.htm
質問 # 25
An AI Specialist has created a copilot custom action using flow as the reference action type. However, it is not delivering the expected results to the conversation preview, and therefore needs troubleshooting.
What should the AI Specialist do to identify the root cause of the problem?
- A. In Copilot Builder within the Dynamic Panel, turn on dynamic debugging to show the inputs and outputs.
- B. In Copilot Builder, verify the utterance entered by the user and review session event logs for debug information.
- C. Copilot Builder within the Dynamic Panel, confirm selected action and observe the values in Input and Output sections.
正解：A
解説：
When troubleshooting acopilot custom actionusing flow as the reference action type, enablingdynamic debuggingwithinCopilot Builder's Dynamic Panelis the most effective way to identify the root cause. By turning on dynamic debugging, the AI Specialist can see detailed logs showing both theinputs and outputsof the flow, which helps identify where the action might be failing or not delivering the expected results.
* Option B, confirming selected actions and observing the Input and Output sections, is useful for monitoring flow configuration but does not provide the deep diagnostic details available with dynamic debugging.
* Option C, verifying the user utterance and reviewing session event logs, could provide helpful context, but dynamic debugging is the primary tool for identifying issues with inputs and outputs in real time.
Salesforce AI Specialist References:To explore more about dynamic debugging in Copilot Builder, see:
https://help.salesforce.com/s/articleView?id=sf.copilot_custom_action_debugging.htm
質問 # 26
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 References:You can refer to this documentation for further insights: https://help.
salesforce.com/s/articleView?id=sf.einstein_copilot_event_logging.htm
質問 # 27
An AI Specialist wants to include data from the response of external service invocation (REST API callout) into the prompt template.
How should the AI Specialist meet this requirement?
- A. Convert the JSON to an XML merge field.
- B. Use "Add Prompt Instructions" flow element.
- C. Use External Service Record merge fields.
正解：C
解説：
An AI Specialist wants to include data from the response of an external service invocation (REST API callout) into a prompt template. The goal is to incorporate dynamic data retrieved from an external API into the AI-generated content.
Solution:
Use External Service Record Merge Fields
External Service Integration:
Definition: External Services in Salesforce allow the integration of external REST APIs into Salesforce without custom code.
Registration: The external service must be registered in Salesforce, defining the API's schema and methods.
External Service Record Merge Fields:
Purpose: Enables the inclusion of data from external service responses directly into prompt templates using merge fields.
Functionality:
Dynamic Data Inclusion: Allows prompt templates to access and use data returned from REST API callouts.
Merge Fields Syntax: Use merge fields in the prompt template to reference specific data points from the API response.
Implementation Steps:
Register the External Service:
Use External Services to register the REST API in Salesforce.
Define the API's schema, including methods and data structures.
Create a Named Credential:
Configure authentication and endpoint details for the external API.
Use External Service in Flow:
Build a Flow that invokes the external service and captures the response.
Ensure the flow outputs the necessary data for use in the prompt template.
Configure the Prompt Template:
Use External Service Record merge fields in the prompt template to reference data from the flow's output.
Syntax Example: {{flowOutputVariable.fieldName}}
Why Other Options are Less Suitable:
Option A (Convert the JSON to an XML merge field):
Irrelevance: Converting JSON to XML merge fields is unnecessary and complicates the process.
Unsupported Method: Salesforce prompt templates do not support direct inclusion of XML merge fields from JSON conversion.
Option C (Use "Add Prompt Instructions" flow element):
Purpose of Add Prompt Instructions:
Allows adding instructions to the prompt within a flow but does not facilitate including external data.
Limitation: Does not directly help in incorporating external service responses into the prompt template.
Reference:
Salesforce AI Specialist Documentation - Integrating External Services with Prompt Templates:
Explains how to use External Services and merge fields in prompt templates.
Salesforce Help - Using Merge Fields with External Data:
Provides guidance on referencing external data in templates using merge fields.
Salesforce Trailhead - External Services and Flow:
Offers a practical understanding of integrating external APIs using External Services and Flow.
Conclusion:
By using External Service Record merge fields, the AI Specialist can effectively include data from external REST API responses into prompt templates, ensuring that the AI-generated content is enriched with up-to-date and relevant external data.
質問 # 28
Universal Containers (UC) uses Salesforce Service Cloud to support its customers and agents handling cases.
UC is considering implementing Einstein Copilot and extending Service Cloud to mobile users.
When would Einstein Copilot implementation be most advantageous?
- A. When the focus is on optimizing marketing campaigns and strategies
- B. When the goal is to streamline customer support processes and improve response times
- C. When the main objective is to enhance data security and compliance measures
正解：B
解説：
Einstein Copilotimplementation would be most advantageous inSalesforce Service Cloudwhen the goal is to streamline customer support processes and improve response times. Einstein Copilot can assist agents by providing real-time suggestions, automating repetitive tasks, and generating contextual responses, thus enhancing service efficiency.
* Option B (data security)is not the primary focus of Einstein Copilot, which is more about improving operational efficiency.
* Option C (marketing campaigns)falls outside the scope of Service Cloud and Einstein Copilot's primary benefits, which are aimed at improving customer service and case management.
For further reading, refer toSalesforce documentation on Einstein Copilot for Service Cloudand how it improves support processes.
質問 # 29
What is the role of the large language model (LLM) in executing an Einstein Copilot Action?
- A. Determine a user's access and sort actions by priority to be executed
- B. Identify the best matching actions and correct order of execution
- C. Find similar requests and provide actions that need to be executed
正解：B
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.
Reference:
Salesforce Einstein Documentation on Einstein Copilot Actions
Salesforce AI Documentation on Large Language Models
質問 # 30
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. Generate a Knowledge article based off the prompts that the agent enters to create steps to cancel flights.
- C. Invoke a flow which makes a call to external data to create a Knowledge article.
正解：B
解説：
In this scenario, the Einstein Copilot capability that best helps the agent is its ability to execute tasks based on available actions and answer questions using 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:
B refers to invoking a flow to create a Knowledge article, which is unrelated to the task of retrieving existing Knowledge articles.
C focuses on generating Knowledge articles, which is not the immediate need for this situation where the agent requires guidance on existing procedures.
Reference:
Salesforce Documentation on Einstein Copilot
Trailhead Module on Einstein for Service
質問 # 31
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 User and Data Cloud Admin
- C. Prompt Template Manager and Prompt Template User
正解：A
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 # 32
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 Account Plan
- C. Create Close Plan
正解：C
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 # 33
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 were created in the last 12 months.
- B. Matched opportunities have a status of Closed Won from last 12 months.
- C. Matched opportunities are limited to the same account.
正解：B
解説：
When Einstein Copilot for Sales matches similar opportunities, one of the primary criteria used is whether the opportunities have a status of Closed Won within the last 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, review Salesforce Einstein Copilot documentation related to opportunity matching and sales success patterns.
質問 # 34
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 most popular products from new product catalog.
- B. Predict customer lifetime value of an account.
- C. Predict customer sentiment toward a promotion message.
正解：B
解説：
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
質問 # 35
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 # 36
Universal Containers (UC) has implemented Generative AI within Salesforce to enable summarization of a custom object called Guest. Users have reported mismatches in the generated information.
In refining its prompt design strategy, which key practices should UC prioritize?
- A. Enable prompt test mode, allocate different prompt variations to a subset of users for evaluation, and standardize the most effective model based on performance feedback.
- B. Create concise, clear, and consistent prompt templates with effective grounding, contextual role- playing, clear instructions, and iterative feedback.
- C. Submit a prompt review case to Salesforce and conduct thorough testing In the playground to refine outputs until they meet user expectations.
正解：B
解説：
ForUniversal Containers (UC)to refine itsGenerative AIprompt design strategy and improve the accuracy of the generated summaries for the custom objectGuest, the best practice is to focus on craftingconcise, clear, and consistent prompt templates. This includes:
* Effective grounding: Ensuring the prompt pulls data from the correct sources.
* Contextual role-playing: Providing the AI with a clear understanding of its role in generating the summary.
* Clear instructions: Giving unambiguous directions on what to include in the response.
* Iterative feedback: Regularly testing and adjusting prompts based on user feedback.
* Option Bis correct because it follows industry best practices for refining prompt design.
* Option A(prompt test mode) is useful but less relevant for refining prompt design itself.
* Option C(prompt review case with Salesforce) would be more appropriate for technical issues or complex prompt errors, not general design refinement.
References:
* Salesforce Prompt Design Best Practices:https://help.salesforce.com/s/articleView?id=sf.
prompt_design_best_practices.htm
質問 # 37
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 perform an HTTP callout to an LLM provider.
- B. Einstein Copilot analyzes the user's request and LLM technology is used to generate and display the appropriate response.
- C. Einstein Copilot will trigger a flow that utilizes a prompt template to generate the message.
正解：B
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 # 38
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