ACTUAL SALESFORCE AGENTFORCE-SPECIALIST EXAM DUMPS - PASS EXAM WITH GOOD SCORES

Actual Salesforce Agentforce-Specialist Exam Dumps - Pass Exam With Good Scores

Actual Salesforce Agentforce-Specialist Exam Dumps - Pass Exam With Good Scores

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Tags: Sample Agentforce-Specialist Test Online, Agentforce-Specialist Test Result, Latest Agentforce-Specialist Exam Topics, Test Agentforce-Specialist Cram, Agentforce-Specialist Valid Exam Online

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Sample Agentforce-Specialist Test Online & Leading Offer in Qualification Exams & Salesforce Salesforce Certified Agentforce Specialist

The Salesforce Certified Agentforce Specialist (Agentforce-Specialist) certification test is an important part of career growth, and passing it may lead to more employment opportunities. However, preparing for the Salesforce Certified Agentforce Specialist (Agentforce-Specialist) test may be tough, and many busy applicants have difficulty cracking it. This is where Pass4guide Salesforce Certified Agentforce Specialist real exam questions come to help you clear the test in a short time.

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
  • 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 3
  • 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.
Topic 4
  • 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 5
  • 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.

Salesforce Certified Agentforce Specialist Sample Questions (Q79-Q84):

NEW QUESTION # 79
The marketing team at Universal Containers is looking for a way personalize emails based on customer behavior, preferences, and purchase history.
Why should the team use Einstein Copilot as the solution?

  • A. To analyze past campaign performance
  • B. To send automated emails to all customers
  • C. To generate relevant content when engaging with each customer

Answer: C

Explanation:
Einstein Copilotis designed to assist in generating personalized, AI-driven content based on customer data such as behavior, preferences, and purchase history. For the marketing team atUniversal Containers, this is the perfect solution to create dynamic and relevant email content. By leveragingEinstein Copilot, they can ensure that each customer receives tailored communications, improving engagement and conversion rates.
* Option Ais correct asEinstein Copilothelps generate real-time, personalized content based on comprehensive data about the customer.
* Option Brefers more to Einstein Analytics or
* Marketing Cloud Intelligence, andOption Cdeals with automation, which isn't the primary focus of Einstein Copilot.
References:
* Salesforce Einstein Copilot Overview:https://help.salesforce.com/s/articleView?
id=einstein_copilot_overview.htm


NEW QUESTION # 80
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. Work Summaries
  • C. Service Replies

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 # 81
Universal Containers deploys a new Agentforce Service Agent into the company's website but is getting feedback that the Agentforce Service Agent is not providing answers to customer questions that are found in the company's Salesforce Knowledge articles. What is the likely issue?

  • A. The Agentforce Service Agent user was not given the Allow View Knowledge permission set.
  • B. The Agentforce Service Agent user is not assigned the correct Agent Type License.
  • C. The Agentforce Service Agent user needs to be created under the standard Agent Knowledge profile.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation:Universal Containers (UC) has deployed an Agentforce Service Agent on its website, but it's failing to provide answers from Salesforce Knowledge articles. Let's troubleshoot the issue.
* Option A: The Agentforce Service Agent user is not assigned the correct Agent Type License.
There's no "Agent Type License" in Salesforce-agent functionality is tied to Agentforce licenses (e.g., Service Agent license) and permissions. Licensing affects feature access broadly, but the specific issue of not retrieving Knowledge suggests a permission problem, not a license type, making this incorrect.
* Option B: The Agentforce Service Agent user needs to be created under the standard Agent Knowledge profile.No "standard Agent Knowledge profile" exists. The Agentforce Service Agent runs under a system user (e.g., "Agentforce Agent User") with a custom profile or permission sets. Profile creation isn't the issue-access permissions are, making this incorrect.
* Option C: The Agentforce Service Agent user was not given the Allow View Knowledge permission set.The Agentforce Service Agent user requires read access to Knowledge articles to ground responses. The "Allow View Knowledge" permission (typically via the "Salesforce Knowledge User" license or a permission set like "Agentforce Service Permissions") enables this. If missing, the agent can't access Knowledge, even if articles are indexed, causing the reported failure. This is a common setup oversight and the likely issue, making it the correct answer.
Why Option C is Correct:Lack of Knowledge access permissions for the Agentforce Service Agent user directly prevents retrieval of article content, aligning with the symptoms and Salesforce security requirements.
References:
* Salesforce Agentforce Documentation: Service Agent Setup > Permissions- Requires Knowledge access.
* Trailhead: Set Up Agentforce Service Agents- Lists "Allow View Knowledge" need.
* Salesforce Help: Knowledge in Agentforce- Confirms permission necessity.


NEW QUESTION # 82
Universal Containers (UC) has configured an Agentforce Data Library using Knowledge articles. When testing in Agent Builder and the Experience Cloud site, the agent is not responding with grounded Knowledge article information. However, when tested in Prompt Builder, the response returns correctly. What should UC do to troubleshoot the issue?

  • A. Ensure the Data Cloud User permission set has been assigned to the Agentforce Service Agent User.
  • B. Create a new permission set that assigns "Manage Knowledge" and assign it to the Agentforce Service Agent User.
  • C. Ensure the assigned User permission set includes access to the prompt template used to access the Knowledge articles.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation:UC has set up an Agentforce Data Library with Knowledge articles, and while Prompt Builder retrieves the data correctly, the agent fails to do so in Agent Builder and Experience Cloud. Let's troubleshoot the issue.
* Option A: Create a new permission set that assigns "Manage Knowledge" and assign it to the Agentforce Service Agent User.The "Manage Knowledge" permission is for authoring and managing Knowledge articles, not for reading or retrieving them in an agent context. The Agentforce Service Agent User (a system user) needs read access to Knowledge, not management rights. This option is excessive and irrelevant to the grounding issue, making it incorrect.
* Option B: Ensure the assigned User permission set includes access to the prompt template used to access the Knowledge articles.Prompt templates in Prompt Builder don't require specific permissions beyond general Einstein Generative AI access. Since the Prompt Builder test works, the template and its grounding are accessible to the testing user. The issue lies with the agent's runtime access,not the template itself, making this incorrect.
* Option C: Ensure the Data Cloud User permission set has been assigned to the Agentforce Service Agent User.When Knowledge articles are grounded via an Agentforce Data Library, they are often ingested into Data Cloud for indexing and retrieval. The Agentforce Service Agent User, which runs the agent, needs the "Data Cloud User" permission set (or equivalent) to access Data Cloud resources, including the Data Library. If this permission is missing, the agent cannot retrieve Knowledge article data during runtime (e.g., in Agent Builder or Experience Cloud), even though Prompt Builder (running under a different user context) succeeds. This is a common setup oversight and aligns with the symptoms, making it the correct answer.
Why Option C is Correct:The Agentforce Service Agent User's lack of Data Cloud access explains the failure in agent-driven contexts while Prompt Builder (likely run by an admin with broader permissions) succeeds. Assigning the "Data Cloud User" permission set resolves this, per Salesforce documentation.
References:
* Salesforce Agentforce Documentation: Data Library Setup > Permissions- Requires Data Cloud access for agents.
* Trailhead: Ground Your Agentforce Prompts- Notes Data Cloud User permission for Knowledge grounding.
* Salesforce Help: Agentforce Security > Agent User Setup- Lists required permission sets.


NEW QUESTION # 83
What is the role of the large language model (LLM) in executing an Einstein Copilot Action?

  • A. Determine a user's access and sort actions by priority to be executed
  • B. Identify the best matching actions and correct order of execution
  • C. Find similar requests and provide actions that need to be executed

Answer: B

Explanation:
In Einstein Copilot, the role of the Large Language Model (LLM) is to analyze user inputs and identify the best matching actions that need to be executed. It uses natural language understanding to break down the user' s request and determine the correct sequence of actions that should be performed.
By doing so, the LLM ensures that the tasks and actions executed are contextually relevant and are performed in the proper order. This process provides a seamless, AI-enhanced experience for users by matching their requests to predefined Salesforce actions or flows.
The other options are incorrect because:
A mentions finding similar requests, which is not the primary role of the LLM in this context.
C focuses on access and sorting by priority, which is handled more by security models and governance than by the LLM.
References:
Salesforce Einstein Documentation on Einstein Copilot Actions
Salesforce AI Documentation on Large Language Models


NEW QUESTION # 84
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