SALESFORCE-AI-SPECIALIST ACCURATE TEST & SALESFORCE-AI-SPECIALIST VCE TORRENT

Salesforce-AI-Specialist Accurate Test & Salesforce-AI-Specialist Vce Torrent

Salesforce-AI-Specialist Accurate Test & Salesforce-AI-Specialist Vce Torrent

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The Salesforce Salesforce-AI-Specialist certification exam is most useful for candidates who are from the working class, but students who are still in school can also use Salesforce Salesforce-AI-Specialist dumps in place of searching for other exam-related literature. In order to put it simply, we can state that the Salesforce Salesforce-AI-Specialist Practice Questions are the only thing that can save you from failing the challenging Salesforce-AI-Specialist certification exam.

Salesforce Salesforce-AI-Specialist Exam Syllabus Topics:

TopicDetails
Topic 1
  • Agentforce Tools: In this topic, AI specialists get knowledge using agents when it is appropriate. Moreover, the topic explains the working of agents and reasoning engine powers Agentforce. Lastly, the topic focuses on managing and monitoring agent adoption.
Topic 2
  • Prompt Builder: This section evaluates the expertise of AI specialists working with Salesforce's AI tools. It focuses on the Prompt Builder feature, requiring candidates to understand its usage based on business needs.
Topic 3
  • Einstein Trust Layer: This section evaluates the skills of Salesforce AI specialists responsible for implementing security protocols and safeguarding data privacy. It emphasizes the security, privacy, and foundational features of the Einstein Trust Layer.
Topic 4
  • Generative AI in CRM Applications: This part of the exam assesses AI specialists’ knowledge of generative AI within CRM systems. It covers the use of generative AI features in Einstein for Sales and Einstein for Service.
Topic 5
  • Model Builder: This portion of the exam focuses on Salesforce AI specialists' expertise in working with AI models within Salesforce environments. Candidates will need to demonstrate knowledge of when to use the Model Builder and how to configure standard, custom, or Bring Your Own Large Language Model (BYOLLM) generative models to meet business needs.

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Salesforce Certified AI Specialist Exam Sample Questions (Q26-Q31):

NEW QUESTION # 26
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?

  • A. Sales Summary
  • B. Call Explorer
  • C. Call Summary

Answer: C

Explanation:
For a sales rep who may miss key details during long sales calls, the AI Specialist should recommend theCall Summaryfeature.Call SummaryusesEinstein Generative AIto 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 Exploreris designed for manually searching through call data but doesn't summarize.
* Sales Summaryis focused more on summarizing overall sales activity, not call-specific content.
For more details, refer toSalesforce's Call Summary documentationon how AI-generated summaries can improve sales rep productivity.


NEW QUESTION # 27
Universal Containers plans to enhance the customer support team's productivity using AI.
Which specific use case necessitates the use of Prompt Builder?

  • A. Creating a draft of a support bulletin post for new product patches
  • B. Estimating support ticket volume based on historical data and seasonal trends
  • C. Creating an Al-generated customer support agent performance score

Answer: A

Explanation:
The use case that necessitates the use ofPrompt Builderiscreating a draft of a support bulletin postfor new product patches.Prompt Builderallows the AI Specialist to create and refine prompts that generate specific, relevant outputs, such as drafting support communication based on product information and patch details.
* Option B(agent performance score) would likely involve predictive modeling, not prompt generation.
* Option C(estimating support ticket volume) would require data analysis and predictive tools, not prompt building.
For more details, refer toSalesforce's Prompt Builder documentationfor generative AI content creation.


NEW QUESTION # 28
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 analyzes the user's request and LLM technology is used to generate and display the appropriate response.
  • B. Einstein Copilot will perform an HTTP callout to an LLM provider.
  • C. Einstein Copilot will trigger a flow that utilizes a prompt template to generate the message.

Answer: A

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 # 29
Universal Containers wants to make a sales proposal and directly use data from multiple unrelated objects (standard and custom) inaprompt template.
What should the AI Specialist recommend?

  • A. Create a prompt template-triggered flow to access the data from standard and custom objects.
  • B. Create a Flex template to add resources with standard and custom objects as inputs.
  • C. Create a prompt template passing in a special custom object that connects the records temporarily,

Answer: B

Explanation:
Universal Containers needs to generate a sales proposal using data from multiple unrelated standard and custom objects within a prompt template. The most effective way to achieve this is by using aFlex template.
Flex templates in Salesforce allow AI specialists to create prompt templates that can accept inputs from multiple sources, including various standard and custom objects. This flexibility enables the direct use of data from unrelated objects without the need to create intermediary custom objects or complex flows.
References:
* Salesforce AI Specialist Documentation - Flex Templates:Explains how Flex templates can be utilized to incorporate data from multiple sources, providing a flexible solution for complex data requirements in prompt templates.


NEW QUESTION # 30
A data science team has trained an XGBoost classification model for product recommendations onDatabricks.
The AI Specialist is tasked with bringing inferences for product recommendations from this model into Data Cloud as a stand-alone data model object (DMO).
How should the AI Specialist set this up?

  • A. Create the serving endpoint in Einstein Studio, then configure the model using Model Builder.
  • B. Create the serving endpoint in Databricks, then configure the model using a Python SDK connector.
  • C. Create the serving endpoint in Databricks, then configure the model using Model Builder.

Answer: C

Explanation:
To integrate inferences from an XGBoost model into Salesforce's Data Cloud as a stand-alone Data Model Object (DMO):
* Create the Serving Endpoint in Databricks:
* The serving endpoint is necessary to make the trained model available for real-time inference.
Databricks provides tools to host and expose the model via an endpoint.
* Configure the Model Using Model Builder:
* After creating the endpoint, the AI Specialist should configure it within Einstein Studio'sModel Builder, which integrates external endpoints with Salesforce Data Cloud for processing and storing inferences as DMOs.
* Option B: Serving endpoints are not created in Einstein Studio; they are set up in external platforms like Databricks before integration.
* Option C: A Python SDK connector is not used to bring model inferences into Salesforce Data Cloud; Model Builder is the correct tool.


NEW QUESTION # 31
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