VALID SALESFORCE-AI-SPECIALIST EXAM CAMP PDF - SALESFORCE-AI-SPECIALIST EXAM CRAM QUESTIONS

Valid Salesforce-AI-Specialist Exam Camp Pdf - Salesforce-AI-Specialist Exam Cram Questions

Valid Salesforce-AI-Specialist Exam Camp Pdf - Salesforce-AI-Specialist Exam Cram Questions

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Salesforce Salesforce-AI-Specialist Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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 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
  • 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.

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Salesforce-AI-Specialist Exam Cram Questions | Technical Salesforce-AI-Specialist Training

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Salesforce Certified AI Specialist Exam Sample Questions (Q25-Q30):

NEW QUESTION # 25
How does the Einstein Trust Layer ensure that sensitive data is protected 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 response journey.
  • C. Masked data will be de-masked during request journey.

Answer: B

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 # 26
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 Sales Email prompt template type.
  • B. Use sales Email standard templates
  • C. Create a t field Generation prompt template type

Answer: A


NEW QUESTION # 27
Universal Containers' data science team is hosting a generative large language model (LLM) on Amazon Web Services (AWS).
What should the team use to access externally-hosted models in the Salesforce Platform?

  • A. Copilot Builder
  • B. App Builder
  • C. Model Builder

Answer: C

Explanation:
To accessexternally-hosted models, such as a large language model (LLM) hosted on AWS, theModel Builderin Salesforce is the appropriate tool.Model Builderallows teams to integrate and deploy external AI models into the Salesforce platform, making it possible to leverage models hosted outside of Salesforce infrastructure while still benefiting from the platform's native AI capabilities.
* Option B, App Builder, is primarily used to build and configure applications in Salesforce, not to integrate AI models.
* Option C, Copilot Builder, focuses on building assistant-like tools rather than integrating external AI models.
Model Builder enables seamless integration with external systems and models, allowing Salesforce users to use external LLMs for generating AI-driven insights and automation.
Salesforce AI Specialist References:For more details, check the Model Builder guide here:https://help.
salesforce.com/s/articleView?id=sf.model_builder_external_models.htm


NEW QUESTION # 28
Universal Containers (UC) recently rolled out Einstein Generative capabilities and has created a custom prompt to summarize case records. Users have reported that the case summaries generated are not returning the appropriate information.
What is a possible explanation for the poor prompt performance?

  • A. The Einstein Trust Layer is incorrectly configured.
  • B. The data being used for grounding Is incorrect or incomplete.
  • C. The prompt template version is incompatible with the chosen LLM.

Answer: B

Explanation:
Poor prompt performance when generating case summaries is often due to the data used forgroundingbeing incorrect or incomplete. Grounding involves feeding accurate, relevant data to the AI so it can generate appropriate outputs. If the data source is incomplete or contains errors, the generated summaries will reflect that by being inaccurate or insufficient.
* Option B(prompt template incompatibility with the LLM) is unlikely because such incompatibility usually results in more technical failures, not poor content quality.
* Option C(Einstein Trust Layer misconfiguration) is focused on data security and auditing, not the quality of prompt responses.
For more information, refer toSalesforce documentation on grounding AI modelsand data quality best practices.


NEW QUESTION # 29
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. Call Explorer
  • B. Sales Summary
  • 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 # 30
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