2025 Salesforce Pass-Sure Agentforce-Specialist: Reliable Salesforce Certified Agentforce Specialist Exam Tutorial
2025 Salesforce Pass-Sure Agentforce-Specialist: Reliable Salesforce Certified Agentforce Specialist Exam Tutorial
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Salesforce Certified Agentforce Specialist Sample Questions (Q145-Q150):
NEW QUESTION # 145
How does an Agent respond when it can't understand the request or find any requested information?
- A. With a general message asking the user to rephrase the request.
- B. With a generated error message.
- C. With a preconfigured message, based on the action type.
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation:Agentforce Agents are designed to handle situations where they cannot interpret a request or retrieve requested data gracefully. Let's assess the options based on Agentforce behavior.
* Option A: With a preconfigured message, based on the action type.While Agentforce allows customization of responses, there's no specific mechanism tying preconfigured messages to action types for unhandled requests. Fallback responses are more general, not action-specific, making this incorrect.
* Option B: With a general message asking the user to rephrase the request.When an Agentforce Agent fails to understand a request or find information, it defaults to a general fallback response, typically asking the user to rephrase or clarify their input (e.g., "I didn't quite get that-could you try asking again?"). This is configurable in Agent Builder but defaults to a user-friendly prompt to encourage retry, aligning with Salesforce's focus on conversational UX. This is the correct answer per documentation.
* Option C: With a generated error message.Agentforce Agents prioritize user experience over technical error messages. While errors might log internally (e.g., in Event Logs), the user-facing response avoids jargon and focuses on retry prompts, making this incorrect.
Why Option B is Correct:The default behavior of asking users to rephrase aligns with Agentforce's conversational design principles, ensuring a helpful response when comprehension fails, as noted in official resources.
References:
* Salesforce Agentforce Documentation: Agent Builder > Fallback Responses- Describes general retry messages.
* Trailhead: Build Agents with Agentforce- Covers handling ununderstood requests.
* Salesforce Help: Agentforce Interaction Design- Confirms user-friendly fallback behavior.
NEW QUESTION # 146
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. Add the fine-tuned LLM in Einstein Studio Model Builder.
- B. Create an application of the custom LLM and embed it in Sales Cloud via iFrame.
- C. Enable model endpoint on OpenAl and make callouts to the model to generate emails.
Answer: A
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 # 147
Universal Containers wants to utilize Agentforce for Sales to help sales reps reach their sales quotas by providing AI-generated plans containing guidance and steps for closing deals. Which feature meets this requirement?
- A. Create Account Plan
- B. Create Close Plan
- C. Find Similar Deals
Answer: B
Explanation:
Comprehensive and Detailed In-Depth Explanation:Universal Containers (UC) aims to leverage Agentforce for Sales to assist sales reps with AI-generated plans that provide guidance and steps for closing deals. Let's evaluate the options based on Agentforce for Sales features.
* Option A: Create Account PlanWhile account planning is valuable for long-term strategy, Agentforce for Sales does not have a specific "Create Account Plan" feature focused on closing individual deals.
Account plans typically involve broader account-level insights, not deal-specific closure steps, making this incorrect for UC's requirement.
* Option B: Find Similar Deals"Find Similar Deals" is not a documented feature in Agentforce for Sales. It might imply identifying past deals for reference, but it doesn't involve generating plans with guidance and steps for closing current deals. This option is incorrect and not aligned with UC's goal.
* Option C: Create Close PlanThe "Create Close Plan" feature in Agentforce for Sales uses AI to generate a detailed plan with actionable steps and guidance tailored to closing a specific deal. Powered by the Atlas Reasoning Engine, it analyzes deal data (e.g., Opportunity records) and provides reps with a roadmap to meet quotas. This directly meets UC's requirement for AI-generated plans focused on deal closure, making it the correct answer.
Why Option C is Correct:"Create Close Plan" is a specific Agentforce for Sales capability designed to help reps close deals with AI-driven plans, aligning perfectly with UC's needs as per Salesforce documentation.
References:
* Salesforce Agentforce Documentation: Agentforce for Sales > Create Close Plan- Details AI-generated close plans.
* Trailhead: Explore Agentforce Sales Agents- Highlights close plan generation for sales reps.
* Salesforce Help: Sales Features in Agentforce- Confirms focus on deal closure.
NEW QUESTION # 148
Universal Containers (UC) needs to improve the agent productivity in replying to customer chats.
Which generative AI feature should help UC address this issue?
- A. Service Replies
- B. Case Escalation
- C. Case Summaries
Answer: A
Explanation:
* Service Replies: This generative AI feature automates and assists in generating accurate, contextual, and efficient replies for customer service agents. It uses past interactions, case data, and the context of the conversation to provide draft responses, thereby enhancing productivity and reducing response times.
* Case Summaries: Summarizes case information but does not assist directly in replying to customer chats.
* Case Escalation: Refers to moving cases to higher-level support teams but does not address the need to improve chat response productivity.
Thus,Service Repliesis the best feature for this requirement as it directly aligns with improving agent efficiency in replying to chats.
NEW QUESTION # 149
An Agentforce configured Data Masking within the Einstein Trust Layer.
How should theAgentforce Specialistbegin 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. Enable the collection and storage of Einstein Generative AI Audit Data on the Einstein Feedback setup page.
- C. Request the Einstein Generative AI Audit Data from the Security section of the Setup menu.
Answer: B
Explanation:
To begin validating that the correct fields are being masked inEinstein Trust Layer, theAgentforce Specialistshould request theEinstein Generative AI Audit Datafrom theSecurity sectionof the Salesforce Setup menu. This audit data allows theAgentforce Specialistto see how data is being processed, including which fields are being masked, providing transparency and validation that the configuration is working as expected.
* Option Bis correct because it allows for the retrieval of audit data that can be used to validate data masking.
* Option A(Flow Debugger) andOption C(Einstein Feedback) do not relate to validating field masking in the context of theEinstein Trust Layer.
References:
* Salesforce Einstein Trust Layer Documentation:https://help.salesforce.com/s/articleView?id=sf.
einstein_trust_layer_audit.htm
NEW QUESTION # 150
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