SALESFORCE-AI-SPECIALIST QUESTION EXPLANATIONS, SALESFORCE-AI-SPECIALIST PASS RATE

Salesforce-AI-Specialist Question Explanations, Salesforce-AI-Specialist Pass Rate

Salesforce-AI-Specialist Question Explanations, Salesforce-AI-Specialist Pass Rate

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Tags: Salesforce-AI-Specialist Question Explanations, Salesforce-AI-Specialist Pass Rate, Exam Salesforce-AI-Specialist Lab Questions, Salesforce-AI-Specialist New Exam Braindumps, Salesforce-AI-Specialist Test Dumps.zip

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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
  • 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 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
  • 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
  • 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.

>> Salesforce-AI-Specialist Question Explanations <<

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Salesforce Certified AI Specialist Exam Sample Questions (Q144-Q149):

NEW QUESTION # 144
What is the main purpose of Prompt Builder?

  • A. A tool within Salesforce offering real-time Al-powered suggestions and guidance to users, Improving productivity and decision-making.
  • B. A tool for developers to use in Visual Studio Code that creates prompts for Apex programming, assisting developers in writing code more efficiently.
  • C. A tool that enables companies to create reusable prompts for large language models (LLMs), bringing generative AI responses to their flow of work

Answer: C

Explanation:
Prompt Builder is designed to help organizations create and configure reusable prompts for large language models (LLMs). By integrating generative AI responses into workflows, Prompt Builder enables customization of AI prompts that interact with Salesforce data and automate complex processes. This tool is especially useful for creating tailored and consistent AI-generated content in various business contexts, including customer service and sales.
* It is not a tool for Apex programming (as in option A).
* It is also not limited to real-time suggestions as mentioned in option C. Instead, it provides a flexible way for companies to manage and customize how AI-driven responses are generated and used in their workflows.
References:
* Salesforce Prompt Builder Overview: https://help.salesforce.com/s/articleView?id=sf.prompt_builder.htm


NEW QUESTION # 145
Universal Containers is very concerned about security compliance and wants to understand:
Which prompt text is sent to the large language model (LLM)
* How it is masked
* The masked response
What should the AI Specialist recommend?

  • A. Ingest the Einstein Shield Event logs into CRM Analytics.
  • B. Review the debug logs of the running user.
  • C. Enable audit trail in the Einstein Trust Layer.

Answer: C

Explanation:
To addresssecurity complianceconcerns and provide visibility into theprompt text sent to the LLM, how it ismasked, and themasked response, the AI Specialist should recommend enabling theaudit trail in the Einstein Trust Layer. This feature captures and logs the prompts sent to the large language model (LLM) along with the masking of sensitive information and the AI's response. This audit trail ensures full transparency and compliance with security requirements.
* Option A (Einstein Shield Event logs)is focused on system events rather than specific AI prompt data.
* Option B (debug logs)would not provide the necessary insight into AI prompt masking or responses.
For further details, refer toSalesforce's Einstein Trust Layer documentationabout auditing and security measures.


NEW QUESTION # 146
Universal Container (UC) has effectively utilized prompt templates to update summary fields on Lightning record pages. An admin now wishes to incorporate similar functionality into UC's automation process using Flow.
How can the admin get a response from this prompt template from within a flow to use as part of UC's automation?

  • A. Invocable Apex
  • B. Einstein for Flow
  • C. Flow Action

Answer: C

Explanation:
* Context of the Question
* Universal Container (UC) has used prompt templates to update summary fields on record pages.
* Now, the admin wants to incorporate similar generative AI functionality within a Flow for automation purposes.
* copyright a Prompt Template Within a Flow
* Flow Action: Salesforce provides a standard way to invoke generative AI templates or prompts within a Flow step. From the Flow Builder, you can add an "Action" that references the prompt template you created in Prompt Builder.
* Other Options:
* Invocable Apex: Possible fallback if there's no out-of-the-box Flow Action available.
However, Salesforce is releasing native Flow integration for AI prompts, making custom Apex less necessary.
* Einstein for Flow: A broad label for Salesforce's generative AI features within Flow.
Under the hood, you typically use a "Flow Action" that points to your prompt.
* Conclusion
* The easiest out-of-the-box solution is to use aFlow Actionreferencing the prompt template.
Hence,Option Bis correct.
Salesforce AI Specialist References & Documents
* Salesforce Trailhead:Use Prompt Templates in FlowDemonstrates how to add an Action in Flow that calls a prompt template.
* Salesforce Documentation:Einstein GPT for FlowExplains standard flow actions to invoke and handle generative AI responses.


NEW QUESTION # 147
How does the Einstein Trust Layer ensure that sensitive data is protected while generating useful and meaningful responses?

  • A. Masked data will be de-masked during request journey.
  • B. Responses that do not meet the relevance threshold will be automatically rejected.
  • 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.
Reference:
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 # 148
Universal Containers wants to use an external large language model (LLM) in Prompt Builder.
What should an AI Specialist recommend?

  • A. Use Apex to connect to an external LLM and ground the prompt.
  • B. Use BYO-LLM functionality in Einstein Studio,
  • C. Use Flow and External Services to bring data from an external LLM.

Answer: B

Explanation:
Bring Your Own Large Language Model (BYO-LLM) functionality in Einstein Studio allows organizations to integrate and use external large language models (LLMs) within the Salesforce ecosystem.
Universal Containers can leverage this feature to connect and ground prompts with external LLMs, allowing for custom AI model use cases and seamless integration with Salesforce data.
* Option B is the correct choice as Einstein Studio provides a built-in feature to work with external models.
* Option A suggests using Apex, but BYO-LLM functionality offers a more streamlined solution.
* Option C focuses on Flow and External Services, which is more about data integration and isn't ideal for working with LLMs.
References:
* Salesforce Einstein Studio BYO-LLM Documentation: https://help.salesforce.com/s/articleView?id=sf.
einstein_studio_llm.htm


NEW QUESTION # 149
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