Salesforce Certified AI Specialist Practice Test - Questions Answers, Page 8
List of questions
Question 71
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An AI Specialist needs to create a prompt template to fill a custom field named Latest Opportunities Summary on the Account object with information from the three most recently opened opportunities.
How should the AI Specialist gather the necessary data for the prompt template?
Explanation:
To gather the necessary data for populating the Latest Opportunities Summary custom field on the Account object with information from the three most recently opened opportunities, the AI Specialist should create a flow. A flow can be configured to query and retrieve the required opportunity records based on criteria such as their open date. Once the flow has gathered the necessary data, it can be used in a prompt template or other automation processes to populate the custom field on the Account record.
Option A is correct because creating a flow allows for dynamic data retrieval and control over the logic for selecting the most recent opportunities.
Option B and Option C do not provide sufficient control or data retrieval capabilities needed for this scenario.
Salesforce Flow Documentation: https://help.salesforce.com/s/articleView?id=sf.flow.htm
Question 72
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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?
Explanation:
To allow a data scientist to view and manage models in Einstein Studio and create prompt templates in Prompt Builder, the AI Specialist should assign the Data Cloud Admin and Prompt Template Manager permission sets.
Data Cloud Admin provides access to manage and oversee models within Einstein Studio.
Prompt Template Manager gives the user the ability to create and manage prompt templates within Prompt Builder.
Option A is correct because it assigns the necessary permissions for both managing models and creating prompt templates.
Option B and Option C are incorrect as they do not provide the correct combination of permissions for managing models and building prompts.
Salesforce Permissions Documentation: https://help.salesforce.com/s/articleView?id=sf.perm_sets_overview.htm
Question 73
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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?
Explanation:
To begin validating that the correct fields are being masked in Einstein Trust Layer, the AI Specialist should request the Einstein Generative AI Audit Data from the Security section of the Salesforce Setup menu. This audit data allows the AI Specialist to see how data is being processed, including which fields are being masked, providing transparency and validation that the configuration is working as expected.
Option B is correct because it allows for the retrieval of audit data that can be used to validate data masking.
Option A (Flow Debugger) and Option C (Einstein Feedback) do not relate to validating field masking in the context of the Einstein Trust Layer.
Salesforce Einstein Trust Layer Documentation: https://help.salesforce.com/s/articleView?id=sf.einstein_trust_layer_audit.htm
Question 74
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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?
Explanation:
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.
Salesforce Prompt Builder Overview: https://help.salesforce.com/s/articleView?id=sf.prompt_builder_overview.htm
Question 75
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What is the primary function of the planner service in the Einstein Copilot system?
Explanation:
The primary function of the planner service in the Einstein Copilot system is to identify copilot actions that should be taken in response to user utterances. This service is responsible for analyzing the conversation and determining the appropriate actions (such as querying records, generating a response, or taking another action) that the Einstein Copilot should perform based on user input.
Question 76
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Universal Containers (UC) wants to enable its sales team with automatic post-call visibility into mention of competitors, products, and other custom phrases.
Which feature should the AI Specialist set up to enable UC's sales team?
Explanation:
To enable Universal Containers' sales team with automatic post-call visibility into mentions of competitors, products, and custom phrases, the AI Specialist should set up Call Insights. Call Insights analyzes voice and video calls for key phrases, topics, and mentions, providing insights into critical aspects of the conversation. This feature automatically surfaces key details such as competitor mentions, product discussions, and custom phrases specified by the sales team.
Call Summaries provide a general overview of the call but do not specifically highlight keywords or topics.
Call Explorer is a tool for navigating through call data but does not focus on automatic insights.
For more information, refer to Salesforce's Call Insights documentation regarding the analysis of call content and extracting actionable information.
Question 77
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A sales rep at Universal Containers is extremely busy and sometimes will have very long sales calls on voice and video calls and might miss key details. They are just starting to adopt new generative AI
features.
Which Einstein Generative AI feature should an AI Specialist recommend to help the rep get the details they might have missed during a conversation?
Explanation:
For a sales rep who may miss key details during long sales calls, the AI Specialist should recommend the Call Summary feature. Call Summary uses Einstein Generative AI to automatically generate a concise summary of important points discussed during the call, helping the rep quickly review the key information they might have missed.
Call Explorer is designed for manually searching through call data but doesn't summarize.
Sales Summary is focused more on summarizing overall sales activity, not call-specific content.
For more details, refer to Salesforce's Call Summary documentation on how AI-generated summaries can improve sales rep productivity.
Question 78
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Universal Containers wants to allow its service agents to query the current fulfillment status of an order with natural language. There is an existing auto launched flow to query the information from Oracle ERP, which is the system of record for the order fulfillment process.
How should an AI Specialist apply the power of conversational AI to this use case?
Explanation:
To enable Universal Containers service agents to query the current fulfillment status of an order using natural language and leverage an existing auto-launched flow that queries Oracle ERP, the best solution is to create a custom copilot action that calls the flow. This action will allow Einstein Copilot to interact with the flow and retrieve the required order fulfillment information seamlessly. Custom copilot actions can be tailored to call various backend systems or flows in response to user requests.
Option B is correct because it enables integration between Einstein Copilot and the flow that connects to Oracle ERP.
Option A (Flex prompt template) is more suited for static responses and not for invoking flows.
Option C (Integration Flow Standard Action) is not directly related to creating a specific copilot action for this use case.
Salesforce Einstein Copilot Actions: https://help.salesforce.com/s/articleView?id=einstein_copilot_actions.htm
Question 79
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When a customer chat is initiated, which functionality in Salesforce provides generative AI replies or draft emails based on recommended Knowledge articles?
Explanation:
When a customer chat is initiated, Einstein Service Replies provides generative AI replies or draft emails based on recommended Knowledge articles. This feature uses the information from the Salesforce Knowledge base to generate responses that are relevant to the customer's query, improving the efficiency and accuracy of customer support interactions.
Option B is correct because Einstein Service Replies is responsible for generating AI-driven responses based on knowledge articles.
Option A (Einstein Reply Recommendations) is focused on recommending replies but does not generate them.
Option C (Einstein Grounding) refers to grounding responses in data but is not directly related to drafting replies.
Einstein Service Replies Overview: https://help.salesforce.com/s/articleView?id=sf.einstein_service_replies.htm
Question 80
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Universal Containers (UC) plans to send one of three different emails to its customers based on the customer's lifetime value score and their market segment.
Considering that UC are required to explain why an e-mail was selected, which AI model should UC use to achieve this?
Explanation:
Universal Containers should use a Predictive model to decide which of the three emails to send based on the customer's lifetime value score and market segment. Predictive models analyze data to forecast outcomes, and in this case, it would predict the most appropriate email to send based on customer attributes. Additionally, predictive models can provide explainability to show why a certain email was chosen, which is crucial for UC's requirement to explain the decision-making process.
Generative models are typically used for content creation, not decision-making, and thus wouldn't be suitable for this requirement.
Predictive models offer the ability to explain why a particular decision was made, which aligns with UC's needs.
Refer to Salesforce's Predictive AI model documentation for more insights on how predictive models are used for segmentation and decision making.
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