Free Agentforce-Specialist Exam Dumps

Universal Containers (UC) uses Salesforce Service Cloud to support its customers and agents handling cases. UC is considering implementing Agent and extending Service Cloud to mobile users.
When would Agent implementation be most advantageous?

Correct Answer:A
Agent implementation would be most advantageous in Salesforce Service Cloud when the goal is to streamline customer support processes and improve response times. Agent can assist agents by providing real-time suggestions, automating repetitive tasks, and generating contextual responses, thus enhancing service efficiency.
✑ Option B (data security) is not the primary focus of Agent, which is more about
improving operational efficiency.
✑ Option C (marketing campaigns) falls outside the scope of Service Cloud and Agent??s primary benefits, which are aimed at improving customer service and case
management.
For further reading, refer to Salesforce documentation on Agent for Service Cloud and how it improves support processes.

Universal Containers has a strict change management process that requires all possible configuration to be completed in a sandbox which will be deployed to production. The Agentforce Specialist is tasked with setting up Work Summaries for Enhanced Messaging. Einstein Generative AI is already enabled in production, and the Einstein Work Summaries permission set is already available in production.
Which other configuration steps should the Agentforce Specialist take in the sandbox that can be deployed to the production org?

Correct Answer:C
✑ Context of the Question
✑ What Can Actually Be Deployed from Sandbox to Production?
✑ Why Option C is Correct
✑ Why Not Option A or B?
✑ ConclusionThe main deployable items you can reliably create and test in a sandbox, and then migrate to Production, are:
Therefore, Option C is correct and focuses on actions that are truly deployable as metadata from a sandbox to Production.
Salesforce Agentforce Specialist References & Documents
✑ Salesforce Trailhead: Work Summaries with Einstein GPTProvides an overview of how to configure Work Summaries, including the need for custom fields, quick actions, and UI components.
✑ Salesforce Documentation: Deploying Metadata Between OrgsExplains what can and cannot be deployed via change sets (e.g., custom fields, page layouts, quick actions vs. user permission set assignments).
✑ Salesforce Agentforce Specialist Study GuideOutlines which Einstein Generative AI and Work Summaries configurations are deployable as metadata.

Universal Containers (UC) wants to create a new Sales Email prompt template in Prompt Builder using the "Save As" function. However, UC notices that the new template produces different results compared to the standard Sales Email prompt due to missing hyperparameters.
What should UC do to ensure the new prompt template produces results comparable to the standard Sales Email prompts?

Correct Answer:B
When Universal Containers creates a new Sales Email prompt template using the "Save As" function, missing hyperparameters can result in different outputs. To ensure the new prompt produces comparable results to the standard Sales Email prompt, the Agentforce Specialist should manually add the necessary hyperparameters to the new template.
✑ Hyperparameters like Temperature, Frequency Penalty, and Presence Penalty
directly affect how the AI generates responses. Ensuring that these are consistent with the standard template will result in similar outputs.
✑ Option A (Model Playground) is not necessary here, as it focuses on fine-tuning
models, not adjusting templates directly.
✑ Option C (Reverting to the standard template) does not solve the issue of customizing the prompt template.
For more information, refer to Prompt Builder documentation on configuring hyperparameters in custom templates.

Universal Containers (UC) wants to use Generative AI Salesforce functionality to reduce Service Agent handling time by providing recommended replies based on the existing Knowledge articles. On which AI capability should UC train the service agents?

Correct Answer:C
Comprehensive and Detailed In-Depth Explanation:Salesforce Agentforce leverages generative AI to enhance service agent efficiency, particularly through capabilities that generate recommended replies. In this scenario, Universal Containers
aims to reduce handling time by providing replies based on existing Knowledge articles, which are a core component of Salesforce Knowledge. The Knowledge Replies capability is specifically designed for this purpose—it uses generative AI to analyze Knowledge articles, match them to the context of a customer inquiry (e.g., a case or chat), and suggest relevant, pre-formulated responses for service agents to use or adapt. This aligns directly with UC??s goal of leveraging existing content to streamline agent workflows.
✑ Option A (Service Replies): While "Service Replies" might sound plausible, it is not a specific, documented capability in Agentforce. It appears to be a generic distractor and does not tie directly to Knowledge articles.
✑ Option B (Case Replies): "Case Replies" is not a recognized AI capability in Agentforce either. While replies can be generated for cases, the focus here is on Knowledge article integration, which points to Knowledge Replies.
✑ Option C (Knowledge Replies): This is the correct capability, as it explicitly connects generative AI with Knowledge articles to produce recommended replies, reducing agent effort and handling time.
Training service agents on Knowledge Replies ensures they can effectively use AI- suggested responses, review them for accuracy, and integrate them into their workflows, fulfilling UC??s objective.
References:
✑ Salesforce Agentforce Documentation: "Knowledge Replies for Service Agents" (Salesforce Help: https://help.salesforce.com/s/articleView?id=sf.agentforce_knowledge_replies.htm
&type=5)
✑ Trailhead: "Agentforce for Service" module (https://trailhead.salesforce.com/content/learn/modules/agentforce-for-service)

Universal Containers (UC) is discussing its AI strategy in an agile Scrum meeting.
Which business requirement would lead An Agentforce to recommend connecting to an external foundational model via Einstein Studio (Model Builder)?

Correct Answer:B
Einstein Studio (Model Builder) allows organizations to connect and utilize external foundational models while fine-tuning them with company-specific data. This capability is particularly suited to businesses like Universal Containers (UC) that require customization of foundational models to better align with their unique data and use cases.
✑ Option A: Adjusting model temperature is a parameter-level setting for controlling
randomness in AI-generated responses but does not necessitate connecting to an external foundational model.
✑ Option B: This is the correct answer because Einstein Studio supports fine-tuning
external models with proprietary company data, enabling a tailored and more accurate AI solution for UC.
✑ Option C: Changing frequency penalties is another parameter-level adjustment
and does not require external foundational models or Einstein Studio.
Reference:
"Using Einstein Studio to Connect Foundational Models | Salesforce Trailhead" .