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New Salesforce Agentforce-Specialist Exam Fee | Agentforce-Specialist Free Learn

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New Salesforce Agentforce-Specialist Exam Fee | Agentforce-Specialist Free Learn

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Salesforce Agentforce-Specialist Exam Syllabus Topics:
TopicDetails
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 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
  • 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 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.

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Salesforce Certified Agentforce Specialist Sample Questions (Q156-Q161):NEW QUESTION # 156
Coral Cloud Resorts needs to ensure its booking agent executes actions in a specific sequence: first retrieve available sessions, then verify customer eligibility, and finally create the booking. The current implementation allows the large language model (LLM) to execute these actions in any order, causing booking failures.
Which approach should an AgentForce Specialist implement?
  • A. Configure topic, classification description, and action instructions with priority levels and sequence indicators to guide the reasoning engine in selecting the correct action order automatically.
  • B. Write comprehensive topic instructions detailing the exact sequence of actions using numbered steps and explicit ordering requirements for the reasoning engine to follow during booking workflows.
  • C. Create custom variables that store completion status for each step, then implement conditional filters on subsequent actions requiring previous variables to be populated, ensuring deterministic execution order.
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract of AgentForce Documents:
According to the AgentForce Orchestration and Action Sequencing Guidelines in the official documentation, deterministic execution order is best achieved by using custom state variables and conditional logic rather than relying solely on LLM reasoning or topic instructions.
AgentForce's orchestration framework allows developers to define variables that represent the successful completion of specific actions (e.g., "sessionsRetrieved," "eligibilityVerified," etc.). Subsequent actions can then include conditional filters that only allow execution if prior steps have been completed. This approach ensures that actions execute in a strict, logical sequence - preventing the LLM from reordering steps arbitrarily.
Option A (relying on topic instructions) provides guidance to the LLM but does not enforce execution order programmatically, which means errors can still occur if the reasoning engine interprets steps differently.
Option C (priority and sequence indicators) assists in contextual selection but does not create dependency- based control between actions.
Therefore, per AgentForce best practices, the correct approach is Option B - using custom variables with conditional filters. This guarantees deterministic workflow sequencing, prevents premature action execution, and aligns with the "Action Dependency and Conditional Execution Model" described in the AgentForce Implementation Guide.
Reference: AgentForce Orchestration Framework - "Ensuring Deterministic Action Sequences with Variables and Conditional Logic."

NEW QUESTION # 157
Universal Containers (UC) is using Einstein Generative AI to generate an account summary. UC aims to ensure the content is safe and inclusive, utilizing the Einstein Trust Layer's toxicity scoring to assess the content's safety level.
In the score of 1 indicate?
  • A. The response is the most toxic.
  • B. The response is the least toxic Einstein Generative AI Toxicity Scoring system, what does a toxicity category.
  • C. The response is not toxic.
Answer: A
Explanation:
Einstein Trust Layer's Toxicity Scoring categorizes content on a scale of 0 to 1, where 1 indicates the highest level of toxicity (e.g., harmful, biased, or inappropriate language). This scoring helps organizations filter unsafe AI-generated content. A score of 1 triggers mitigation actions, such as blocking the response or alerting administrators.
* A score of 0 would indicate no toxicity (B is incorrect).
* The scoring system does not use "least toxic" as a category (A is misleading).
Reference:
Salesforce Help Article: Einstein Trust Layer - Toxicity Scoring ("Interpreting Toxicity Scores" section).
Einstein GPT Safety Overview: "Mitigating Harmful Content with Toxicity Detection."

NEW QUESTION # 158
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. Ensure the assigned User permission set includes access to the prompt template used to access the Knowledge articles.
  • C. Create a new permission set that assigns "Manage Knowledge" and assign it to the Agentforce Service Agent User.
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 # 159
Universal Containers wants to assign agents to improve department efficiency.
Which configuration ensures the right tasks are handled by the right agents?
  • A. SDR Agent for lead qualification, Service Agent for support tickets, Employee Agent for HR requests
  • B. One Service Agent to efficiently handle each of these scenarios, which reduces the number of agent types needed for support
  • C. Sales Coach Agent for lead and service Agent for HR requests, and Support tickets to ensure cases are available
Answer: A
Explanation:
According to the AgentForce Product Overview and Deployment Guide, Salesforce recommends using purpose-built agents to maximize efficiency across departments. The documentation states:
"Each AgentForce agent type is optimized for a specific function - SDR Agent for sales development and lead nurturing, Service Agent for customer service and support cases, and Employee Agent for internal HR, IT, and productivity tasks." This separation ensures that each team benefits from a domain-specific agent equipped with the correct data access and actions.
Option B incorrectly assigns agent types to mismatched use cases, and Option C reduces efficiency and control by using a single generic agent for multiple domains, which goes against Salesforce's modular AI design principle.
Thus, Option A best aligns with Salesforce's guidance for role-based AgentForce deployment.
References (AgentForce Documents / Study Guide):
AgentForce Product Overview: "Agent Types and Use Cases"
AgentForce Implementation Guide: "Aligning Agents to Departmental Functions" AgentForce Study Guide: "Optimizing Team Efficiency with Specialized Agents"

NEW QUESTION # 160
Universal Containers is considering leveraging the Einstein Trust Layer in conjunction with Einstein Generative AI Audit Data.
Which audit data is available using the Einstein Trust Layer?
  • A. Hallucination score and bias score
  • B. Response accuracy and offensiveness score
  • C. Masked data and toxicity score
Answer: C
Explanation:
Universal Containers is considering the use of the Einstein Trust Layer along with Einstein Generative AI Audit Data. The Einstein Trust Layer provides a secure and compliant way to use AI by offering features like data masking and toxicity assessment.
The audit data available through the Einstein Trust Layer includes information aboutmasked data-which ensures sensitive information is not exposed-and thetoxicity score, which evaluates the generated content for inappropriate or harmful language.
References:
* SalesforceAgentforce SpecialistDocumentation - Einstein Trust Layeretails the auditing capabilities, including logging of masked data and evaluation of generated responses for toxicity to maintain compliance and trust.

NEW QUESTION # 161
......
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