Salesforce Salesforce-AI-Specialist Latest Test Cost | Training Salesforce-AI-Specialist Kit

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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 administrators responsible for implementing security protocols and safeguarding data privacy. It emphasizes the security, privacy, and foundational features of the Einstein Trust Layer.
Topic 2	Einstein Copilot: This section assesses Salesforce consultants' skills in using Einstein Copilot in CRM workflows. It includes identifying when Einstein Copilot is appropriate based on business requirements, understanding how the large language model (LLM) performs copilot actions, and creating both standard and custom copilot actions.
Topic 3	Generative AI in CRM Applications: This part of the exam assesses Salesforce professionals’ knowledge of generative AI within CRM systems. It covers the use of generative AI features in Einstein for Sales and Einstein for Service.
Topic 4	Model Builder: This portion of the exam focuses on Salesforce developers' 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 5	Prompt Builder: This section evaluates the expertise of Salesforce consultants and administrators working with Salesforce's AI tools. It focuses on the Prompt Builder feature, requiring candidates to understand its usage based on business needs.

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Salesforce Certified AI Specialist Exam Sample Questions (Q61-Q66):

NEW QUESTION # 61
Universal Containers wants to utilize Einstein for Sales to help sales reps reach their sales quotas by providing Al-generated plans containing guidance and steps for closing deals.
Which feature should the AI Specialist recommend to the sales team?

A. Find Similar Deals
B. Create Close Plan
C. Create Account Plan

Answer: B

Explanation:
The"Create Close Plan"feature is designed to help sales reps by providingAI-generated strategies and stepsspecifically focused on closing deals. This feature leverages AI to analyze the current state of opportunities and generate a plan that outlines the actions, timelines, and key steps required to movedeals toward closure. It aligns directly with the sales team's need to meet quotas by offering actionable insights and structured plans.
* Find Similar Deals(Option A) helps sales reps discover opportunities similar to their current deals but doesn't offer a plan for closing.
* Create Account Plan(Option B) focuses on long-term strategies for managing accounts, which might include customer engagement and retention, but doesn't focus on deal closure.
Salesforce AI Specialist References:For more information on using AI for sales, visit:https://help.salesforce.
com/s/articleView?id=sf.einstein_for_sales_overview.htm

NEW QUESTION # 62
Universal Containers (UC) is using Einstein Generative AI to generate an account summary. UC aims to ensure the content is safe and inclusive, utilizing the Einstein Trust Layer's toxicity scoring to assess the content's safety level.
What does a safety category score of 1 indicate in the Einstein Generative Toxicity Score?

A. Not safe
B. Safe
C. Moderately safe

Answer: B

Explanation:
In theEinstein Trust Layer, thetoxicity scoringsystem is used to evaluate the safety level of content generated by AI, particularly to ensure that it is non-toxic, inclusive, and appropriate for business contexts. A toxicity score of 1indicates that the content is deemedsafe.
The scoring system ranges from 0 (unsafe) to 1 (safe), with intermediate values indicating varying degrees of safety. In this case, a score of 1 means that the generated content is fully safe and meets the trust and compliance guidelines set by theEinstein Trust Layer.
For further reference, check Salesforce's officialEinstein Trust Layer documentationregardingtoxicity scoringfor AI-generated content.

NEW QUESTION # 63
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. Call prompt initiated flow to fetch and ground the required data.
B. Utilize a standard email template and manually insert the required data fields.
C. Create a flex template that takes the records in question as inputs.

Answer: A

NEW QUESTION # 64
Universal Containers Is Interested In Improving the sales operation efficiency by analyzing their data using Al-powered predictions in Einstein Studio.
Which use case works for this scenario?

A. Predict customer lifetime value of an account.
B. Predict most popular products from new product catalog.
C. Predict customer sentiment toward a promotion message.

Answer: A

Explanation:
For improvingsales operations efficiency,Einstein Studiois ideal for creating AI-powered models that can predict outcomes based on data. One of the most valuable use cases is predictingcustomer lifetime value, which helps sales teams focus on high-value accounts and make more informed decisions.Customer lifetime value (CLV)predictions can optimize strategies around customer retention, cross-selling, and long-term engagement.
* Option Bis the correct choice as predicting customer lifetime value is a well-established use case for AI in sales.
* Option A(customer sentiment) is typically handled through NLP models, whileOption C(product popularity) is more of a marketing analysis use case.
References:
* Salesforce Einstein Studio Use Case Overview:https://help.salesforce.com/s/articleView?id=sf.
einstein_studio_overview

NEW QUESTION # 65
How does the Einstein Trust Layer ensure that sensitive data isprotected while generating useful and meaningful responses?

A. Responses that do not meet the relevance threshold will be automatically rejected.
B. Masked data will be de-masked during request journey.
C. Masked data will be de-masked during response journey.

Answer: C

Explanation:
The Einstein Trust Layer ensures that sensitive data is protected while generating useful and meaningful responses by masking sensitive data before it is sent to the Large Language Model (LLM) and then de- masking it during the response journey.
How It Works:
* Data Masking in the Request Journey:
* Sensitive Data Identification:Before sending the prompt to the LLM, the Einstein Trust Layer scans the input for sensitive data, such as personally identifiable information (PII), confidential business information, or any other data deemed sensitive.
* Masking Sensitive Data:Identified sensitive data is replaced with placeholders or masks. This ensures that the LLM does not receive any raw sensitive information, thereby protecting it from potential exposure.
* Processing by the LLM:
* Masked Input:The LLM processes the masked prompt and generates a response based on the masked data.
* No Exposure of Sensitive Data:Since the LLM never receives the actual sensitive data, there is no risk of it inadvertently including that data in its output.
* De-masking in the Response Journey:
* Re-insertion of Sensitive Data:After the LLM generates a response, the Einstein Trust Layer replaces the placeholders in the response with the original sensitive data.
* Providing Meaningful Responses:This de-masking process ensures that the final response is both meaningful and complete, including the necessary sensitive information where appropriate.
* Maintaining Data Security:At no point is the sensitive data exposed to the LLM or any unintended recipients, maintaining data security and compliance.
Why Option A is Correct:
* De-masking During Response Journey:The de-masking process occurs after the LLM has generated its response, ensuring that sensitive data is only reintroduced into the output at the final stage, securely and appropriately.
* Balancing Security and Utility:This approach allows the system to generate useful and meaningful responses that include necessary sensitive information without compromising data security.
Why Options B and C are Incorrect:
* Option B (Masked data will be de-masked during request journey):
* Incorrect Process:De-masking during the request journey would expose sensitive data before it reaches the LLM, defeating the purpose of masking and compromising data security.
* Option C (Responses that do not meet the relevance threshold will be automatically rejected):
* Irrelevant to Data Protection:While the Einstein Trust Layer does enforce relevance thresholds to filter out inappropriate or irrelevant responses, this mechanism does not directly relate to the protection of sensitive data. It addresses response quality rather than data security.
References:
* Salesforce AI Specialist Documentation -Einstein Trust Layer Overview:
* Explains how the Trust Layer masks sensitive data in prompts and re-inserts it after LLM processing to protect data privacy.
* Salesforce Help -Data Masking and De-masking Process:
* Details the masking of sensitive data before sending to the LLM and the de-masking process during the response journey.
* Salesforce AI Specialist Exam Guide -Security and Compliance in AI:
* Outlines the importance of data protection mechanisms like the Einstein Trust Layer in AI implementations.
Conclusion:
The Einstein Trust Layer ensures sensitive data is protected by masking it before sending any prompts to the LLM and then de-masking it during the response journey. This process allows Salesforce to generate useful and meaningful responses that include necessary sensitive information without exposing that data during the AI processing, thereby maintaining data security and compliance.

NEW QUESTION # 66
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