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First-rank Salesforce-AI-Specialist Exam Preparation: Salesforce Certified AI Specialist Exam boosts the Most Efficient Training Dumps - PassSureExam

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Salesforce Salesforce-AI-Specialist Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 3

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 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.

Salesforce Certified AI Specialist Exam Sample Questions (Q90-Q95):

NEW QUESTION # 90
An AI Specialist wants to ground a new prompt template with the User related list.
What should the AI Specialist consider?

A. The User related list is not supported in prompt templates.
B. The User related list needs to be included on the record page.
C. The User related list should have View All access.

Answer: A

Explanation:
An AI Specialist wants to ground a new prompt template with theUser related list. Grounding in prompt templates involves using data from related lists to provide context or additional information to the Large Language Model (LLM) when generating responses.
Key Consideration:
* Unsupported Related Lists in Prompt Templates:
* Limitation:TheUser related listisnot supportedin prompt templates for grounding purposes.
* Reason:Salesforce restricts certain objects and related lists from being used in prompt templates to maintain data security and integrity. The User object often contains sensitive information and is subject to strict access controls.
* Impact:Attempting to use the User related list in a prompt template will not work as expected because the system does not support it.
Why Options A and B are Incorrect:
* Option A (The User related list should have View All access):
* Incorrect:Even with View All access, the User related list is still not supported in prompt templates.
* Security Concerns:Granting View All access to the User object is a significant security risk and not a recommended practice.
* Option B (The User related list needs to be included on the record page):
* Incorrect:Including the User related list on the record page does not affect its availability in prompt templates.
* Irrelevance:The placement of the related list on the record page does not change the system's ability to access it in a prompt template.
References:
* Salesforce AI Specialist Documentation -Prompt Templates Limitations:
* Details the objects and related lists that are not supported in prompt templates.
* Salesforce Help -Data Access in Prompt Templates:
* Explains how data access and security considerations affect the use of objects in prompt templates.
* Salesforce Trailhead -Understanding Prompt Template Grounding:
* Provides insights into grounding prompt templates and the limitations involved.
Conclusion:
Since the User related list is not supported in prompt templates, the AI Specialist must consider alternative approaches. They might need to redesign the prompt template to use supported objects or related lists, or explore other methods to incorporate necessary user information while adhering to Salesforce's data access policies.

NEW QUESTION # 91
What is the role of the large language model (LLM) in executing an Einstein Copilot Action?

A. Find similar requests and provideactions that need to be executed
B. Determine a user's access and sort actions by priority to be executed
C. Identify the best matching actions and correct order of execution

Answer: C

Explanation:
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.
References:
Salesforce Einstein Documentation on Einstein Copilot Actions
Salesforce AI Documentation on Large Language Models

NEW QUESTION # 92
Universal Containers (UC) wants to improve the productivity of its sales team with generative AI technology.
However, UC is concerned that public AI virtual assistants lack adequate company data to general useful responses.
Which solution should UC consider?

A. Enable Agentforce and deploy to sales users.
B. fine-tune the Einstein AI model with CBM data.
C. Build Al model with Einstein discovery and deploy to sales users.

Answer: B

Explanation:
* Context of the Question: Universal Containers (UC) wants to harness generative AI to boost sales productivity. They are wary of public AI virtual assistants (like generic chatbots) that lack sufficient UC-specific data to generate useful business responses.
* Why Fine-Tune an Einstein AI Model with CRM Data?
* Company-Specific Relevance: By fine-tuning Einstein AI with UC's CRM data (accounts, opportunities, products, and historical interactions), the model learns the enterprise-specific context. This ensures that the generative outputs are accurate and tailored to UC's sales scenarios.
* Security and Compliance: Using Salesforce Einstein within the Salesforce ecosystem keeps data under UC's control, aligning with trust, security, and compliance requirements.
* Better Predictions: Einstein AI can produce more relevant insights (e.g., recommended next steps, content suggestions, or AI-generated email responses) when it has been trained on real, high-quality internal data.
* Why Not Build an AI Model with Einstein Discovery (Option B)?
* Einstein Discovery Use Case: Einstein Discovery is best suited for predictive and prescriptive analytics (e.g., analyzing large data sets for patterns, scoring leads, or predicting churn). While it provides advanced analytics, it is not primarily designed for generative text-based interactions for end-user consumption in a conversational format.
* Why Not Enable Agentforce (Option C)?
* Agentforce Overview: "Agentforce" (sometimes referencing a pilot or non-mainstream name) typically focuses on interactive help or workforce collaboration. It does not inherently solve the problem of large-scale generative AI using internal CRM data. Moreover, you still need a robust generative engine fine-tuned on company data.
* Outcome: Fine-tuning the Einstein AI model with UC's CRM data (Answer A) is the most direct, Salesforce-native solution to provide generative AI responses that are aligned with UC's context, driving productivity gains and ensuring data privacy.
Salesforce AI Specialist References & Documents
* Salesforce Official: Einstein GPT Overview
* Discusses how Einstein GPT can be fine-tuned with specific CRM data to deliver contextually relevant, generative AI responses.
* Salesforce Trailhead:Get Started with Salesforce Einstein
* Explains the fundamentals of AI within the Salesforce platform, including training and optimizing Einstein models.
* Salesforce Documentation: Einstein Discovery
* Details how Einstein Discovery is primarily used for advanced analytics and predictions, not direct generative text solutions.
* Salesforce AI Specialist Study Guide
* Provides the official outline of Einstein AI capabilities, referencing how to configure and fine- tune models for specialized enterprise use cases.

NEW QUESTION # 93
In addition to Recipient and Sender, which object should an AI Specialist utilize for inserting merge fields into a Sales email template prompt?

A. Recipient Account
B. Recipient Opportunities
C. User Organization

Answer: A

Explanation:
* Sales Email Template Use Case:When creating a Sales email template (especially for outreach or follow-up), you often need to reference relevant details about the Account linked to the recipient.
* Standard Merge Fields in Salesforce Email Templates:
* Recipient(Contact, Lead, or Person receiving the email)
* Sender(User sending the email)
* Recipient Account(the Account related to that Contact, providing company-level details and other relevant data)
* Why Recipient Account?
* For Sales communications, referencing theAccountdata (e.g., Account name, industry, or other custom fields) in an email is very common.
* This is especially important for B2B scenarios where the Contact is tied to an Account.
* "Recipient Opportunities" could be multiple, so it's less direct for standard email merges. The
"User Organization" is more generic internal information, not typically inserted for personalization to the recipient.
* References and Study Resources:
* Salesforce Help & Training#Email Templates: Merge Fields
* Salesforce Trailhead#"Create and Customize Email Templates in Sales Cloud"
* Salesforce AI Specialist Study Resources(covers recommended best practices for leveraging standard objects like Account in AI-powered or prompt-based communications)

NEW QUESTION # 94
An AI Specialist at Universal Containers is working on a prompt template to generate personalized emails for product demonstrationrequests from customers. It is important for the Al-generated email to adhere strictly to the guidelines, using only associated opportunityinformation, and to encourage the recipient to take the desired action.
How should the AI Specialist include these instructions on a new line in the prompt template?

A. Surround them with triple quotes (""").
B. Use curly brackets {} to encapsulate instructions.
C. Make sure merged fields are defined.

Answer: A

Explanation:
In Salesforce prompt templates, instructions that guide how the Large Language Model (LLM) should generate content (in this case, personalized emails) can be included by surrounding the instruction text with triple quotes ("""). This formatting ensures that the LLM adheres to the specific instructions while generating the email content.
The use oftriple quotesallows the AI to understand that the enclosed text is a directive for how to approach the task, such as limiting the content to associated opportunity information or encouraging a specific action from the recipient.
Refer toSalesforce Prompt Builder documentationfor detailed instructions on how to structure prompts for generative AI.

NEW QUESTION # 95
......

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