AGENTFORCE-SPECIALIST TRAINING ONLINE & AGENTFORCE-SPECIALIST RELIABLE BRAINDUMPS EBOOK

Agentforce-Specialist Training Online & Agentforce-Specialist Reliable Braindumps Ebook

Agentforce-Specialist Training Online & Agentforce-Specialist Reliable Braindumps Ebook

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Tags: Agentforce-Specialist Training Online, Agentforce-Specialist Reliable Braindumps Ebook, Cost Effective Agentforce-Specialist Dumps, Valid Exam Agentforce-Specialist Preparation, Agentforce-Specialist Real Dumps

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Salesforce Certified Agentforce Specialist Sample Questions (Q44-Q49):

NEW QUESTION # 44
An Al Specialist is tasked with configuring a generative model to create personalized sales emails using customer data stored in Salesforce. The AI Specialist has already fine-tuned a large language model (LLM) on the OpenAI platform. Security and data privacy are critical concerns for the client.
How should the Agentforce Specialist integrate the custom LLM into Salesforce?

  • A. Enable model endpoint on OpenAl and make callouts to the model to generate emails.
  • B. Add the fine-tuned LLM in Einstein Studio Model Builder.
  • C. Create an application of the custom LLM and embed it in Sales Cloud via iFrame.

Answer: B

Explanation:
Since security and data privacy are critical, the best option for the Agentforce Specialist is to integrate the fine- tuned LLM (Large Language Model) into Salesforce by adding it to Einstein Studio Model Builder.
Einstein Studio allows organizations to bring their own AI models (BYOM), ensuring the model is securely managed within Salesforce's environment, adhering to data privacy standards.
* Option A (embedding via iFrame) is less secure and doesn't integrate deeply with Salesforce's data and security models.
* Option C (making callouts to OpenAI) raises concerns about data privacy, as sensitive Salesforce data would be sent to an external system.
Einstein Studio provides the most secure and seamless way to integrate custom AI models while maintaining control over data privacy and compliance. More details can be found in Salesforce's Einstein Studio documentation on integrating external models.


NEW QUESTION # 45
Universal Containers (UC) has a mature Salesforce org with a lot of data in cases and Knowledge articles. UC is concerned that there are many legacy fields, with data that might not be applicable for Einstein AI to draft accurate email responses.
Which solution should UC use to ensure Einstein AI can draft responses from a defined data source?

  • A. Service AI Grounding
  • B. Service Replies
  • C. Work Summaries

Answer: A

Explanation:
Service AI Groundingis the solution thatUniversal Containersshould use to ensureEinstein AIdrafts responses based on a well-defined data source. Service AI Grounding allows the AI model to be anchored in specific, relevant data sources, ensuring that any AI-generated responses (e.g., email replies) are accurate, relevant, and drawn from up-to-date information, such asKnowledge articlesorcases.
Given that UC has legacy fields and outdated data, Service AI Grounding ensures that only the valid and applicable data is used by Einstein AI to craft responses. This helps improve the relevance of responses and avoids inaccuracies caused by outdated or irrelevant fields.
Work SummariesandService Repliesare useful features but do not address the need for grounding AI outputs in specific, current data sources likeService AI Groundingdoes.
For more details, you can refer to Salesforce'sService AI Grounding documentationfor managing AI- generated content based on accurate data sources.


NEW QUESTION # 46
Universal Containers recently launched a pilot program to integrate conversational AI into its CRM business operations with Agentforce Agents. How should the Agentforce Specialist monitor Agents' usability and the assignment of actions?

  • A. Run Agent Analytics.
  • B. Query the Agent log data using the Metadata API.
  • C. Run a report on the Platform Debug Logs.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation:Monitoring the usability and action assignments of Agentforce Agents requires insights into how agents perform, how users interact with them, and how actions are executed within conversations. Salesforce providesAgent Analytics(Option C) as a built-in capability specifically designed for this purpose. Agent Analytics offers dashboards and reports that track metrics such as agent response times, user satisfaction, action invocation frequency, and success rates. This tool allows the Agentforce Specialist to assess usability (e.g., are agents meeting user needs?) and monitor action assignments (e.g., which actions are triggered and how often), providing actionable data to optimize the pilot program.
* Option A: Platform Debug Logs are low-level logs for troubleshooting Apex, Flows, or system processes. They don't provide high-level insights into agent usability or action assignments, making this unsuitable.
* Option B: The Metadata API is used for retrieving or deploying metadata (e.g., object definitions), not runtime log data about agent performance. While Agent log data might exist, querying it via Metadata API is not a standard or documented approach for this use case.
* Option C: Agent Analytics is the dedicated solution, offering a user-friendly way to monitor conversational AI performance without requiring custom development.
Option C is the correct choice for effectively monitoring Agentforce Agents in a pilot program.
References:
* Salesforce Agentforce Documentation: "Agent Analytics Overview" (Salesforce Help:https://help.
salesforce.com/s/articleView?id=sf.agentforce_analytics.htm&type=5)
* Trailhead: "Agentforce for Admins" (https://trailhead.salesforce.com/content/learn/modules/agentforce- for-admins)


NEW QUESTION # 47
How does Secure Data Retrieval ensure that only authorized users can access necessary Salesforce data for dynamic grounding?

  • A. Retrieves Salesforce data based on the user's permissions executing the prompt.
  • B. Retrieves Salesforce data based on the 'Run As" users permissions.
  • C. Retrieves Salesforces data based on the Prompt template's object permissions.

Answer: A

Explanation:
Secure Data Retrieval enforces Salesforce's security model by dynamically grounding data access in the permissions of the user executing the prompt. This ensures compliance with CRUD (Create, Read, Update, Delete) and FLS (Field-Level Security) settings, preventing unauthorized access to sensitive data. For example, if a user lacks access to a specific object or field, the AI model cannot retrieve it for dynamic grounding.
* "Run As" user permissions (A) would bypass user-specific security, posing a compliance risk.
* Prompt template permissions (C) are not a Salesforce security mechanism; access is always tied to the user's profile and sharing settings.


NEW QUESTION # 48
Universal Containers (UC) is implementing generative AI and wants to leverage a prompt template to provide responses to customers that gives personalized product recommendations to website visitors based on their browsing history.
Which initial step should UC take to ensure the chatbot can deliver accurate recommendations'

  • A. Collect and analyze browsing data.
  • B. Design universal product recommendations.
  • C. Write a response scrip for the chatbot.

Answer: A

Explanation:
To enable personalized product recommendations using generative AI, the foundational step for Universal Containers (UC) is collecting and analyzing browsing data (Option C). Personalized recommendations depend on understanding user behavior, which requires structured data about their browsing history. Without this data, the AI model lacks the context needed to generate relevant suggestions.
* Data Collection: UC must first aggregate browsing data (e.g., pages visited, products viewed, session duration) to build a dataset that reflects user preferences.
* Data Analysis: Analyzing this data identifies patterns (e.g., frequently viewed categories) that inform how prompts should be structured to retrieve relevant recommendations.
* Grounding in Data: Salesforce's Prompt Templates rely on grounding data to generate accurate outputs. Without analyzing browsing data, the prompt template cannot reference meaningful insights for personalization.
Options A and D are incorrect because:
* Universal recommendations (A) ignore personalization, which is the core requirement.
* Writing a response script (D) addresses chatbot interaction design, not the accuracy of recommendations.
References:
* SalesforceAgentforce SpecialistCertification Guide: Highlights the importance of grounding prompts in relevant data sources to ensure accuracy.
* Trailhead Module: "Einstein for Developers" emphasizes data preparation as a prerequisite for effective AI-driven personalization.
* Salesforce Help Documentation: Recommends analyzing user behavior data to tailor generative AI outputs in commerce use cases.


NEW QUESTION # 49
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