SALESFORCE-AI-SPECIALIST NEW TEST MATERIALS - LATEST SALESFORCE-AI-SPECIALIST TEST PDF

Salesforce-AI-Specialist New Test Materials - Latest Salesforce-AI-Specialist Test Pdf

Salesforce-AI-Specialist New Test Materials - Latest Salesforce-AI-Specialist Test Pdf

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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
  • 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 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
  • 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.

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Salesforce Certified AI Specialist Exam Sample Questions (Q53-Q58):

NEW QUESTION # 53
Universal Containers (UC) is building a Flex prompt template. UC needs to use data returned by the flow in the prompt template.
Which flow element should UC use?

  • A. Add Prompt Instructions
  • B. Add Flow Instructions
  • C. Add Flex Instructions

Answer: B


NEW QUESTION # 54
Universal Containers (UC) is looking to improve its sales team's productivity by providing real-time insights and recommendations during customer interactions.
Why should UC consider using Agentforce Sales Agent?

  • A. To streamline the sales process and increase conversion rates
  • B. To automate the entire sales process for maximum efficiency
  • C. To track customer interactions for future analysis

Answer: A

Explanation:
Agentforce Sales Agent provides real-time insights and AI-powered recommendations, which are designed to streamline the sales processand help sales representatives focus on key tasks toincrease conversion rates. It offers features like lead scoring, opportunity prioritization, and proactive recommendations, ensuring that sales teams can interact with customers efficiently and close deals faster.
* Option A: While tracking customer interactions is beneficial, it is only part of the broader capabilities offered by Agentforce Sales Agent and is not the primary objective for improving real-time productivity.
* Option B: Agentforce Sales Agent does not automate the entire sales process but provides actionable recommendations to assist the sales team.
* Option C: This aligns with the tool's core purpose of enhancing productivity and driving sales success.


NEW QUESTION # 55
Universal Containers (UC) needs to save agents time with AI-generated case summaries. UC has implemented the Work Summary feature.
What does Einstein consider when generating a summary?

  • A. Generation is grounded with existing conversation context only.
  • B. Generation is grounded with conversation context and Knowledge articles.
  • C. Generation is grounded with conversation context, Knowledge articles, and cases.

Answer: C

Explanation:
When generating a Work Summary, Einstein leverages multiple sources of information to provide a comprehensive and accurate case summary for agents.
* Conversation Context:
* Einstein analyzes the details of the customer interaction, including chat or email threads, to extract relevant information for the summary.
* Knowledge Articles:
* It considers linked Knowledge Articles or articles referred to during the case resolution process, ensuring the summary incorporates accurate resolutions or additional resources provided to the customer.
* Cases:
* Einstein also examines historical cases and related case records to ground the summary in context from past resolutions or interactions.
* Option Ais correct as it includes all three: conversation context, Knowledge articles, and cases.
* Option Bis incorrect because it limits the grounding to conversation context only, excluding other critical elements.
* Option Cis incorrect because it omits case data, which Einstein considers for more accurate and contextually rich summaries.


NEW QUESTION # 56
An AI Specialist configured Data Masking within the Einstein Trust Layer.
How should the AI Specialist begin validating that the correct fields are being masked?

  • A. Use a Flow-based resource in Prompt Builder to debug the fields' merge values using Flow Debugger.
  • B. Request the Einstein Generative AI Audit Data from the Security section of the Setup menu.
  • C. Enable the collection and storage of Einstein Generative AI Audit Data on the Einstein Feedback setup page.

Answer: B

Explanation:
To begin validating that the correct fields are being masked in Einstein Trust Layer, the AI Specialist should request the Einstein Generative AI Audit Data from the Security section of the Salesforce Setup menu.
This audit data allows the AI Specialist to see how data is being processed, including which fields are being masked, providing transparency and validation that the configuration is working as expected.
* Option B is correct because it allows for the retrieval of audit data that can be used to validate data masking.
* Option A (Flow Debugger) and Option C (Einstein Feedback) do not relate to validating field masking in the context of the Einstein Trust Layer.
References:
* Salesforce Einstein Trust Layer Documentation: https://help.salesforce.com/s/articleView?id=sf.
einstein_trust_layer_audit.htm


NEW QUESTION # 57
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. fine-tune the Einstein AI model with CBM data.
  • B. Enable Agentforce and deploy to sales users.
  • C. Build Al model with Einstein discovery and deploy to sales users.

Answer: A

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 # 58
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