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[Hardware] 100% Pass Quiz Perfect Salesforce - Salesforce-AI-Specialist Exam Questions

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【Hardware】 100% Pass Quiz Perfect Salesforce - Salesforce-AI-Specialist Exam Questions

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Salesforce Certified AI Specialist Exam Sample Questions (Q57-Q62):NEW QUESTION # 57
Which use case is best supported by Salesforce Einstein Copilot's capabilities?
  • A. Enable Salesforce admin users to create and train custom large language models (LLMs) using CRM data.
  • B. Enable data scientists to train predictive AI models with historical CRM data using built-in machine learning capabilities
  • C. Bring together a conversational interface for interacting with AI for all Salesforce users, such as developers and ecommerce retailers.
Answer: C
Explanation:
Salesforce Einstein Copilot is 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 an AI-powered assistant that 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 A is correct because Einstein Copilot brings a conversational interface that caters to a wide range of users.
Option B and Option C are more focused on developing and training AI models, which are not the primary functions of Einstein Copilot.
Reference:
Salesforce Einstein Copilot Overview: https://help.salesforce.com/s/ar ... opilot_overview.htm

NEW QUESTION # 58
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, Output Tokens
  • C. Temperature, Frequency Penalty, Presence Penalty
Answer: C
Explanation:
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.

NEW QUESTION # 59
Universal Containers (UC) wants to offer personalized service experiences and reduce agent handling time with Al-generated email responses, grounded in Knowledge base.
Which AI capability should UC use?
  • A. Einstein Service Replies for Email
  • B. Einstein Generative Service Replies for Email
  • C. Einstein Email Replies
Answer: A
Explanation:
ForUniversal Containers (UC)to offer personalized service experiences and reduce agent handling time using AI-generated responses grounded in theKnowledge base, the best solution isEinstein Service Replies for Email. This capability leverages AI to automatically generate responses to service-related emails based on historical data and theKnowledge base, ensuring accuracy and relevance while saving time for service agents.
* Einstein Email Replies(option A) is more suited for sales use cases.
* Einstein Generative Service Replies for Email(option C) could be a future offering, but as of now, Einstein Service Replies for Emailis the correct choice for grounded, knowledge-based responses.
References:
* Einstein Service Replies Overview:https://help.salesforce.com/s/articleView?id=sf.
einstein_service_replies.htm

NEW QUESTION # 60
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. The goal is to enhance the team's performance by identifying areas for improvement and competitive intelligence.
Which feature provides insights about competitor mentions and coaching opportunities?
  • A. Einstein Sales Insights
  • B. Call Summaries
  • C. Call Explorer
Answer: C
Explanation:
For analyzing voice and video call records to gain insights into competitor mentions, coaching opportunities, and other key information, Call Explorer is the most suitable feature. Call Explorer, a part of Einstein 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 mentions and moments for coaching. These insights are vital for improving sales performance by providing a clear understanding of the interactions during calls.
Call Summaries offer a quick overview of a call but do not delve deep into competitor mentions or coaching insights.
Einstein Sales Insights focuses more on pipeline and forecasting insights rather than call-based analysis.
Reference:
Salesforce Einstein Conversation Insights Documentation: https://help.salesforce.com/s/ar ... sation_insights.htm

NEW QUESTION # 61
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 will trigger a flow that utilizes a prompt template to generate the message.
  • C. Einstein Copilot analyzes the user's request and LLM technology is used to generate and display the appropriate response.
Answer: C
Explanation:
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.

NEW QUESTION # 62
......
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