FREE PDF 2025 SALESFORCE AGENTFORCE-SPECIALIST: SALESFORCE CERTIFIED AGENTFORCE SPECIALIST–PROFESSIONAL DUMPS GUIDE

Free PDF 2025 Salesforce Agentforce-Specialist: Salesforce Certified Agentforce Specialist–Professional Dumps Guide

Free PDF 2025 Salesforce Agentforce-Specialist: Salesforce Certified Agentforce Specialist–Professional Dumps Guide

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Salesforce Agentforce-Specialist Exam Syllabus Topics:

TopicDetails
Topic 1
  • Agentforce and Sales Cloud: This section assesses the skills of AI Developers and covers identifying the correct generative AI features in Agentforce for Sales Cloud scenarios. It also includes determining when to use Agentforce Sales Agents, such as Sales Development Representatives (SDRs) and Sales Coaches.
Topic 2
  • Agentforce Concepts: This section assesses the skills of AI Engineers and covers how Agentforce works, including its reasoning engine, standard and custom topics, agent actions, and user security management. It also includes testing and deploying agents from sandbox to production environments.
Topic 3
  • Agentforce and Service Cloud: This section measures the skills of AI Engineers and focuses on building agents that answer questions based on Knowledge articles and connecting them to digital channels. It also covers identifying the correct generative AI features in Agentforce for Service Cloud scenarios.
Topic 4
  • Prompt Engineering: This section measures the skills of AI Developers and focuses on prompt engineering techniques. It covers identifying when to use Prompt Builder, managing prompt templates, selecting appropriate grounding techniques, and explaining the process for creating and executing prompt templates.
Topic 5
  • Agentforce and Data Cloud: This section measures the skills of AI Developers and addresses how Agentforce integrates with Data Cloud to improve response accuracy and personalize answers. It involves grounding with retrievers in Data Cloud to enhance agent performance.

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Salesforce Certified Agentforce Specialist Sample Questions (Q159-Q164):

NEW QUESTION # 159
An Agentforce is tasked with analyzing Agent interactions looking into user inputs, requests, and queries to identify patterns and trends.
What functionality allows the AX Specialist to achieve this?

  • A. Agent Event Logs dashboard
  • B. User Utterances dashboard
  • C. AI Audit & Feedback Data dashboard

Answer: B

Explanation:
The User Utterances dashboard (Option A) is the correct functionality for analyzing user inputs, requests, and queries to identify patterns and trends. This dashboard aggregates and categorizes the natural language inputs (utterances) from users, enabling theAgentforce Specialistto:
* Identify Common Queries: Surface frequently asked questions or recurring issues.
* Detect Intent Patterns: Understand how users phrase requests, which helps refine intent detection models.
* Improve Bot Training: Highlight gaps in training data or misclassified utterances that require adjustment.
Why Other Options Are Incorrect:
* B. Agent Event Logs dashboard: Focuses on agent activity (e.g., response times, resolved cases) rather than user input analysis.
* C. AI Audit & Feedback Data dashboard: Tracks AI model performance, audit trails, and user feedback scores but does not directly analyze raw user utterances or queries.
References:
* Salesforce EinsteinAgentforce SpecialistCertification Guide: Emphasizes the User Utterances dashboard as the primary tool for analyzing user inputs to improve conversational AI.
* Trailhead Module: "Einstein Bots Basics" highlights using the dashboard to refine bot training based on user interaction data.
* Salesforce Help Documentation: Describes the User Utterances dashboard as critical for identifying trends in customer interactions.


NEW QUESTION # 160
A data scientist needs to view and manage models in Einstein Studio, and also needs to create prompt templates in Prompt Builder. Which permission sets should an Agentforce Specialist assign to the data scientist?

  • A. Data Cloud Admin and Prompt Template Manager
  • B. Prompt Template Manager and Prompt Template User
  • C. Prompt Template User and Data Cloud Admin

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation:The data scientist requires permissions for Einstein Studio (model management) and Prompt Builder (template creation). Note: "Einstein Studio" may be a misnomer for Data Cloud's model management or a related tool, but we'll interpret based on context. Let's evaluate.
* Option A: Prompt Template Manager and Prompt Template UserThere's no distinct "Prompt Template Manager" or "Prompt Template User" permission set in Salesforce-Prompt Builder access is typically via "Einstein Generative AI User" or similar. This option lacks coverage for Einstein Studio
/Data Cloud, making it incorrect.
* Option B: Data Cloud Admin and Prompt Template ManagerThe "Data Cloud Admin" permission set grants access to manage models in Data Cloud (assumed as Einstein Studio's context), including viewing and editing AI models. "Prompt Template Manager" isn't a real set, but Prompt Builder creation is covered by "Einstein Generative AI Admin" or similar admin-level access (assumed intent).
This combination approximates the needs, making it the closest correct answer despite naming ambiguity.
* Option C: Prompt Template User and Data Cloud Admin"Prompt Template User" isn't a standard set, and user-level access (e.g., Einstein Generative AI User) typically allows execution, not creation.
The data scientist needs to create templates, so this lacks sufficient Prompt Builder rights, making it incorrect.
Why Option B is Correct (with Caveat):"Data Cloud Admin" covers model management in Data Cloud (likely intended as Einstein Studio), and "Prompt Template Manager" is interpreted as admin-level Prompt Builder access (e.g., Einstein Generative AI Admin). Despite naming inconsistencies, this fits the requirements per Salesforce permissions structure.
References:
* Salesforce Data Cloud Documentation: Permissions- Details Data Cloud Admin for models.
* Trailhead: Set Up Einstein Generative AI- Covers Prompt Builder admin access.
* Salesforce Help: Agentforce Permission Sets- Aligns with admin-level needs.


NEW QUESTION # 161
What is true of Agentforce Testing Center?

  • A. Running tests does not consume Einstein Requests.
  • B. Agentforce Testing Center can only be used in a production environment.
  • C. Running tests risks modifying CRM data in a production environment.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation:The Agentforce Testing Center is a tool in Agentforce Studio for validating agent performance. Let's evaluate the statements.
* Option A: Running tests risks modifying CRM data in a production environment.Agentforce Testing Center runs synthetic interactions in a controlled environment (e.g., sandbox or isolated test space) and doesn't modify live CRM data. It's designed for safe pre-deployment testing, making this incorrect.
* Option B: Running tests does not consume Einstein Requests.Einstein Requests are part of the usage quota for Einstein Generative AI features (e.g., prompt executions in production). Testing Center uses synthetic data to simulate interactions without invoking live AI calls that count against this quota.
Salesforce documentation confirms tests don't consume requests, making this the correct answer.
* Option C: Agentforce Testing Center can only be used in a production environment.Testing Center is available in both sandbox and production orgs, but it's primarily used pre-deployment (e.g., in sandboxes) to validate agents safely. This restriction is false, making it incorrect.
Why Option B is Correct:Not consuming Einstein Requests is a key feature of Testing Center, allowing extensive testingwithout impacting quotas, as per Salesforce documentation.
References:
* Salesforce Agentforce Documentation: Testing Center > Overview- Confirms no request consumption.
* Trailhead: Test Your Agentforce Agents- Notes quota-free testing.
* Salesforce Help: Agentforce Testing- Details safe, isolated testing.


NEW QUESTION # 162
Universal Containers wants to reduce overall customer support handling time by minimizing the time spent typing routine answers for common questions in-chat, and reducing the post-chat analysis by suggesting values for case fields. Which combination of Agentforce for Service features enables this effort?

  • A. Einstein Service Replies and Work Summaries
  • B. Einstein Reply Recommendations and Case Summaries
  • C. Einstein Reply Recommendations and Case Classification

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation:Universal Containers (UC) aims to streamline customer support by addressing two goals: reducing in-chat typing time for routine answers and minimizing post-chat analysis by auto-suggesting case field values. In Salesforce Agentforce for Service,Einstein Reply RecommendationsandCase Classification(Option A) are the ideal combination to achieve this.
* Einstein Reply Recommendations: This feature uses AI to suggest pre-formulated responses based on chat context, historical data, and Knowledge articles. By providing agents with ready-to-use replies for common questions, it significantly reduces the time spent typing routine answers, directly addressing UC's first goal.
* Case Classification: This capability leverages AI to analyze case details (e.g., chat transcripts) and suggest values for case fields (e.g., Subject, Priority, Resolution) during or after the interaction. By automating field population, it reduces post-chat analysis time, fulfilling UC's second goal.
* Option B: While "Einstein Reply Recommendations" is correct for the first part, "Case Summaries" generates a summary of the case rather than suggesting specific field values. Summaries are useful for documentation but don't directly reduce post-chat field entry time.
* Option C: "Einstein Service Replies" is not a distinct, documented feature in Agentforce (possibly a distractor for Reply Recommendations), and "Work Summaries" applies more to summarizing work orders or broader tasks, not case field suggestions in a chat context.
* Option A: This combination precisely targets both in-chat efficiency (Reply Recommendations) and post-chat automation (Case Classification).
Thus, Option A is the correct answer for UC's needs.
References:
* Salesforce Agentforce Documentation: "Einstein Reply Recommendations" (Salesforce Help:
https://help.salesforce.com/s/articleView?id=sf.einstein_reply_recommendations.htm&type=5)
* Salesforce Agentforce Documentation: "Case Classification" (Salesforce Help:https://help.salesforce.
com/s/articleView?id=sf.case_classification.htm&type=5)
* Trailhead: "Agentforce for Service" (https://trailhead.salesforce.com/content/learn/modules/agentforce- for-service)


NEW QUESTION # 163
A Salesforce Administrator is exploring the capabilities of Agent to enhance user interaction within their organization. They are particularly interested in how Agent processes user requests and the mechanism it employs to deliver responses. The administrator is evaluating whether Agent directly interfaces with a large language model (LLM) to fetch and display responses to user inquiries, facilitating a broad range of requests from users.
How does Agent handle user requests In Salesforce?

  • A. Agent analyzes the user's request and LLM technology is used to generate and display the appropriate response.
  • B. Agent will trigger a flow that utilizes a prompt template to generate the message.
  • C. Agent will perform an HTTP callout to an LLM provider.

Answer: A

Explanation:
Agent is designed to enhance user interaction within Salesforce by leveraging Large Language Models (LLMs) to process and respond to user inquiries. When a user submits a request, Agent analyzes the input using natural language processing techniques. It then utilizes LLM technology to generate an appropriate and contextually relevant response, which is displayed directly to the user within the Salesforce interface.
Option C accurately describes this process. Agent does not necessarily trigger a flow (Option A) or perform an HTTP callout to an LLM provider (Option B) for each user request. Instead, it integrates LLM capabilities to provide immediate and intelligent responses, facilitating a broad range of user requests.
References:
* Salesforce Agentforce Specialist Documentation - Agent Overview: Details how Agent employs LLMs to interpret user inputs and generate responses within the Salesforce ecosystem.
* Salesforce Help - How Agent Works: Explains the underlying mechanisms of how Agent processes user requests using AI technologies.


NEW QUESTION # 164
......

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