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Salesforce Agentforce-Specialist PDF Questions - Ensure Your Success In Exam

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Salesforce Agentforce-Specialist Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 3

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 4

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 5

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.

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Salesforce Certified Agentforce Specialist Sample Questions (Q134-Q139):

NEW QUESTION # 134
When a customer chat is initiated, which functionality in Salesforce provides generative AI replies or draft emails based on recommended Knowledge articles?

A. Einstein Grounding
B. Einstein Service Replies
C. Einstein Reply Recommendations

Answer: B

Explanation:
When acustomer chat is initiated,Einstein Service Repliesprovidesgenerative AI replies ordraft emails based on recommendedKnowledge articles. This feature uses the information from theSalesforce Knowledge baseto generate responses that are relevant to the customer's query, improving the efficiency and accuracy of customer support interactions.
* Option Bis correct becauseEinstein Service Repliesis responsible for generating AI-driven responses based on knowledge articles.
* Option A(Einstein Reply Recommendations) is focused on recommending replies but does not generate them.
* Option C(Einstein Grounding) refers to grounding responses in data but is not directly related to drafting replies.
References:
* Einstein Service Replies Overview:https://help.salesforce.com/s/articleView?id=sf.
einstein_service_replies.htm

NEW QUESTION # 135
After creating a foundation model in Einstein Studio, which hyperparameter should An Agentforce use to adjust the balance between consistency and randomness of a response?

A. Presence Penally
B. Temperature
C. Variability

Answer: B

Explanation:
The Temperature hyperparameter controls the randomness of model outputs:
* Low Temperature (e.g., 0.2): More deterministic, consistent responses.
* High Temperature (e.g., 1.0): More creative, varied responses.
* Presence Penalty (Option A): Discourages repetition of tokens, unrelated to randomness.
* Variability (Option B): Not a standard hyperparameter in Einstein Studio.
References:
* Einstein Studio Documentation: Model Hyperparameters
* Explicitly states "Temperature adjusts the balance between predictable and random outputs."

NEW QUESTION # 136
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?

A. Service Replies
B. Knowledge Replies
C. Case Replies

Answer: B

Explanation:
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 existingKnowledge articles, which are a core component of Salesforce Knowledge. TheKnowledge Repliescapability 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)

NEW QUESTION # 137
How does an Agent respond when it can't understand the request or find any requested information?

A. With a generated error message.
B. With a general message asking the user to rephrase the request.
C. With a preconfigured message, based on the action type.

Answer: B

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 # 138
What is the role of the large language model (LLM) in understanding intent and executing an Agent Action?

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

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation:In Agentforce, the large language model (LLM), powered by the Atlas Reasoning Engine, interprets user requests and drives Agent Actions. Let's evaluate its role.
* Option A: Find similar requested topics and provide the actions that need to be executed.While the LLM can identify similar topics, its role extends beyond merely finding them-it matches intents to specific topics and determines execution. This option understates the LLM's responsibility for ordering actions, making it incomplete and incorrect.
* Option B: Identify the best matching topic and actions and correct order of execution.The LLM analyzes user input to understand intent, matches it to the best-fitting topic (configured in Agent Builder), and selects associated actions. It also determines the correct sequence of execution based on the agent's plan (e.g., retrieve data before updating a record). This end-to-end process-from intent recognition to action orchestration-is the LLM's core role in Agentforce, making this the correct answer.
* Option C: Determine a user's topic access and sort actions by priority to be executed.Topic access is governed by Salesforce permissions (e.g., user profiles), not the LLM. While the LLM prioritizes actions within its plan, its primary role is intent matching and executionordering, not access control, making this incorrect.
Why Option B is Correct:The LLM's role in identifying topics, selecting actions, and ordering execution is central to Agentforce's autonomous functionality, as detailed in Salesforce documentation.
References:
* Salesforce Agentforce Documentation: Atlas Reasoning Engine- Outlines LLM's intent and action handling.
* Trailhead: Understand Agentforce Technology- Explains topic matching and execution.
* Salesforce Help: Agentforce Actions- Confirms LLM's role in orchestrating responses.

NEW QUESTION # 139
......

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Will Stone Will Stone

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