Quiz Salesforce-AI-Specialist - Salesforce Certified AI Specialist Exam Authoritative Testking Learning Materials

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Salesforce Salesforce-AI-Specialist Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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.
Topic 4	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 5	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.

Salesforce Certified AI Specialist Exam Sample Questions (Q32-Q37):

NEW QUESTION # 32
A data science team has trained an XGBoost classification model for product recommendations on Databricks. 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 Databricks, then configure the model using a Python SDK connector.
B. Create the serving endpoint in Einstein Studio, then configure the model using Model Builder.
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's Model 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 # 33
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.

Answer: C

Explanation:
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.

NEW QUESTION # 34
An AI Specialist needs to create a Sales Email with a custom prompt template. They need to ground on the following data.
Opportunity Products Events near the customer Tone and voice examples
How should the AI Specialist obtain related items?

A. Create a flex template that takes the records in question as inputs.
B. Utilize a standard email template and manually insert the required data fields.
C. Call prompt initiated flow to fetch and ground the required data.

Answer: C

Explanation:
To ground a sales email onOpportunity Products, Events near the customer, and Tone and voice examples, the AI Specialist should use aprompt-initiated flow. This flow can dynamically fetch the necessary data from related records in Salesforce and ground the generative AI output with contextually accurate information.
* Option B (flex template)does not provide the ability to fetch dynamic data from Salesforce records automatically.
* Option C (manual insertion)would not allow for the dynamic and automated grounding of data required for custom prompts.
Refer toSalesforce documentation on flowsand grounding for more details on integrating data into custom prompt templates.

NEW QUESTION # 35
A Salesforce Administrator wants to generate personalized, targeted emails that incorporate customer interaction data. The admin wants to leverage large language models (LLMs) to write the emails, and wants to reuse templates for different products and customers.
Which solution approach should the admin leverage?

A. Create a t field Generation prompt template type
B. Create a Sales Email prompt template type.
C. Use sales Email standard templates

Answer: B

Explanation:
To generate personalized emails using LLMs while reusing templates:
* Sales Email Prompt Template Type (Option C): Designed specifically for generating dynamic email content by combining LLMs with structured templates. It allows admins to define placeholders (e.g., customer name, product details) and reuse templates across scenarios.
* Option A: Standard email templates lack LLM integration and dynamic personalization.
* Option B: "t field Generation" is not a valid Salesforce prompt template type.
References:
* Salesforce Help: Sales Email Prompt Templates
* Describes using Sales Email prompt templates to "generate targeted emails using dynamic data and LLMs."

NEW QUESTION # 36
Universal Containers (UC) is implementing Einstein Generative AI to improve customer insights and interactions. UC needs audit and feedback data to be accessible for reporting purposes.
What is a consideration for this requirement?

A. Storing this data requires Salesforce big objects.
B. Storing this data requires Data Cloud to be provisioned.
C. Storing this data requires a custom object for data to be configured.

Answer: B

Explanation:
When implementingEinstein Generative AIfor improved customer insights and interactions, theData Cloud is a key consideration for storing and managing large-scale audit and feedback data. TheSalesforce Data Cloud(formerly known asCustomer 360 Audiences) is designed to handle and unify massive datasets from various sources, making it ideal for storing data required for AI-powered insights and reporting. By provisioningData Cloud, organizations likeUniversal Containers (UC)can gain real-time access to customer data, making it a central repository for unified reporting across various systems.
* Audit and feedback datagenerated by Einstein Generative AI needs to be stored in a scalable and accessible environment, and theData Cloudprovides this capability, ensuring that data can be easily accessed for reporting, analytics, and further model improvement.
* Custom objectsorSalesforce Big Objectsare not designed for the scale or the specific type of real- time, unified data processing required in such AI-driven interactions.Big Objectsare more suited for archival data, whereasData Cloudensures more robust processing, segmentation, and analysis capabilities.
References:
* Salesforce Data Cloud Documentation:https://www.salesforce.com/products/data-cloud/overview/
* Salesforce Einstein AI Overview:https://www.salesforce.com/products/einstein/overview/

NEW QUESTION # 37
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Quiz Salesforce-AI-Specialist - Salesforce Certified AI Specialist Exam Authoritative Testking Learning Materials

Practice Test Salesforce-AI-Specialist Pdf - Salesforce-AI-Specialist Free Sample

Salesforce Salesforce-AI-Specialist Exam Syllabus Topics:

Salesforce Certified AI Specialist Exam Sample Questions (Q32-Q37):

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