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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 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 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 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.

Salesforce Certified Agentforce Specialist Sample Questions (Q80-Q85):

NEW QUESTION # 80
Universal Containers (UC) wants to ensure the effectiveness, reliability, and trust of its agents prior to deploying them in production. UC would like to efficiently test a large and repeatable number of utterances.
What should the Agentforce Specialist recommend?

A. Create a CSV file with UC's test cases in Agentforce Testing Center using the testing template.
B. Deploy the agent in a QA sandbox environment and review the Utterance Analysis reports to review effectiveness.
C. Leverage the Agent Large Language Model (LLM) UI and test UC's agents with different utterances prior to activating the agent.

Answer: A

Explanation:
The goal of Universal Containers (UC) is to test its Agentforce agents for effectiveness, reliability, and trust before production deployment, with a focus on efficiently handling a large and repeatable number of utterances. Let's evaluate each option against this requirement and Salesforce's official Agentforce tools and best practices.
* Option A: Leverage the Agent Large Language Model (LLM) UI and test UC's agents with different utterances prior to activating the agent.While Agentforce leverages advanced reasoning capabilities (powered by the Atlas Reasoning Engine), there's no specific "Agent Large Language Model (LLM) UI" referenced in Salesforce documentation for testing agents. Testing utterances directly within an LLM interface might imply manual experimentation, but this approach lacks scalability and repeatability for a large number of utterances. It's better suited for ad-hoc testing of individual responses rather than systematic evaluation, making it inefficient for UC's needs.
* Option B: Deploy the agent in a QA sandbox environment and review the Utterance Analysis reports to review effectiveness.Deploying an agent in a QA sandbox is a valid step in the development lifecycle, as sandboxes allow testing in a production-like environment without affecting live data.
However, "Utterance Analysis reports" is not a standard term in Agentforce documentation. Salesforce provides tools like Agent Analytics or User Utterances dashboards for post-deployment analysis, but these are more about monitoring live performance than pre-deployment testing. This option doesn't explicitly address how to efficiently test a large and repeatable number of utterances before deployment, making it less precise for UC's requirement.
* Option C: Create a CSV file with UC's test cases in Agentforce Testing Center using the testing template.The Agentforce Testing Center is a dedicated tool within Agentforce Studio designed specifically for testing autonomous AI agents. According to Salesforce documentation, Testing Center allows users to upload a CSV file containing test cases (e.g., utterances and expected outcomes) using a provided template. This enables the generation and execution of hundreds of synthetic interactions in parallel, simulating real-world scenarios. The tool evaluates how the agent interprets utterances, selects topics, and executes actions, providing detailed results for iteration. This aligns perfectly with UC's need for efficiency (bulk testing via CSV), repeatability (standardized test cases), and reliability (systematic validation), ensuring the agent is production-ready. This is the recommended approach per official guidelines.
Why Option C is Correct:
The Agentforce Testing Center is explicitly built for pre-deployment validation of agents. It supports bulk testing by allowing users to upload a CSV with utterances, which is then processed by the Atlas Reasoning Engine to assess accuracy and reliability. This method ensures UC can systematically test a large dataset, refine agent instructions or topics based on results, and build trust in the agent's performance-all before production deployment. This aligns with Salesforce's emphasis on testing non-deterministic AI systems efficiently, as noted in Agentforce setup documentation and Trailhead modules.
References:
Salesforce Trailhead: Get Started with Salesforce Agentforce Specialist Certification Prep - Details the use of Agentforce Testing Center for testing agents with synthetic interactions.
Salesforce Agentforce Documentation: Agentforce Studio > Testing Center - Explains how to upload CSV files with test cases for parallel testing.
Salesforce Help: Agentforce Setup > Testing Autonomous AI Agents - Recommends Testing Center for pre- deployment validation of agent effectiveness and reliability.

NEW QUESTION # 81
Choose 1 option.
Universal Containers (UC) is setting up a new Agentforce Service Agent. The company has sensitive medical product research stored internally and wants to ensure the agent cannot access it.
What should UC da?

A. Follow the principle of least privilege and avoid granting permission to view the Medical Product object or related
B. Assign the Agentforce Service Agent user the lowest possible role in the organization's hierarchy to block access.
C. Disable the Agentforce Service Agent's ability to use any Salesforce custom object or related fields.

Answer: A

Explanation:
The AgentForce Security and Access Control Best Practices Guide emphasizes the principle of least privilege, which means granting each agent only the permissions strictly necessary to perform its defined tasks.
To prevent unauthorized access to sensitive data such as medical research, administrators should exclude permissions for the Medical Product object and related records from the AgentForce Service Agent's permission set group. This approach ensures that even if the reasoning engine processes a related query, it cannot retrieve or expose data it lacks access to.
Option A is partially effective but not sufficient since Salesforce role hierarchy does not fully restrict record access. Option B is over-restrictive and would prevent legitimate operations involving other custom objects.
Thus, the correct answer is Option C - Follow the principle of least privilege and avoid granting permission to view the Medical Product object or related records.
Reference: AgentForce Administration and Security Guide - "Applying Least Privilege for Sensitive Data Protection."

NEW QUESTION # 82
Universal Containers implemented Agentforce for its users. One user complains that an Agent is not deleting activities from the past 7 days. What is the reason for this issue?

A. Agentforce does not have a standard Delete Record action.
B. Agentforce does not have the permission to delete the user's records.
C. Agentforce Delete Record Action permission is not associated to the user.

Answer: A

Explanation:
* Context of the QuestionUniversal Containers (UC) uses Agentforce, a specialized AI-driven assistant for Salesforce. A user reports that an Agent is unable to delete recent activities.
* Why Agentforce Cannot Delete Records
* Agentforce's Standard Actions: Agentforce typically has predefined or "standard" actions like Create, Update, or Summarize records. However, a standard Delete Record action is not part of the default set of Agentforce actions.
* Implication: If Agentforce has no built-in delete functionality, it cannot remove activities-even if the user has permission to delete them in the Salesforce UI.
* Why Other Options Are Incorrect
* Option A - Permission to Delete the User's Records: Standard Salesforce user permissions do not automatically extend to Agentforce's capabilities. Even if the user can delete records, that doesn't grant Agentforce a new action.
* Option B - Agentforce Delete Record Action Permission: There is no separate "Delete Record Action permission" for Agentforce to be toggled. The relevant issue is that the standard Delete Record action does not exist within Agentforce out of the box.
* ConclusionThe core reason for the issue is thatAgentforce does not support a standard Delete Record action(Choice C).
SalesforceAgentforce SpecialistReferences & Documents
* Salesforce Official Documentation - Agentforce(Note: Agentforce may be a pilot or specialized feature; check pilot release notes or official docs for standard actions.)
* SalesforceAgentforce SpecialistStudy GuideCovers the limitations of certain AI-enabled features regarding record operations.

NEW QUESTION # 83
The Agentforce Specialist of Northern Trail Outfitters reviewed the organization's data masking settings within the Configure Data Masking menu within Setup. Upon assessing all of the fields, a few additional fields were deemed sensitive and have been masked within Einstein's Trust Layer.
Which steps should the Agentforce Specialist take upon modifying the masked fields?

A. Test and confirm that the responses generated from prompts that utilize the data and masked data do not adversely affect the quality of the generated response
B. Turn on Einstein Feedback so that end users can report if there are any negative side effects on AI features.
C. Turn off the Einstein Trust Layer and turn it on again.

Answer: A

Explanation:
After modifying masked fields inEinstein's Trust Layer, the next important step is totest and confirmthat the responses generated by prompts utilizing the newly masked data still meet quality standards. This ensures that masking sensitive information does not negatively impact the usefulness or accuracy of the AI-generated content. Thorough testing helps identify any issues in prompt performance that could arise due to masking, and adjustments can be made if needed.
* Option Bis correct because testing the effects of masking on AI responses is a critical step in ensuring AI continues to function as expected.
* Option A(turning off and on the Einstein Trust Layer) is unnecessary after changing the masked fields.
* Option C(turning on Einstein Feedback) allows for user feedback but is not a direct step following field masking modifications.
:
Salesforce Einstein Trust Layer Overview:https://help.salesforce.com/s/articleView?id=sf.einstein_trust_layer.
htm

NEW QUESTION # 84
How does the AI Retriever function within Data Cloud?

A. It performs contextual searches over an indexed repository to quickly fetch the most relevant documents, enabling grounding AI responses with trustworthy, verifiable information.
B. It monitors and aggregates data quality metrics across various data pipelines to ensure only high- integrity data is used for strategic decision-making.
C. It automatically extracts and reformats raw data from diverse sources into standardized datasets for use in historical trend analysis and forecasting.

Answer: A

Explanation:
The AI Retriever is a key component in Salesforce Data Cloud, designed to support AI-driven processes like Agentforce by retrieving relevant data. Let's evaluate each option based on its documented functionality.
* Option A: It performs contextual searches over an indexed repository to quickly fetch the most relevant documents, enabling grounding AI responses with trustworthy, verifiable information.
The AI Retriever in Data Cloud uses vector-based search technology to query an indexed repository (e.
g., documents, records, or ingested data) and retrieve the most relevant results based on context. It employs embeddings to match user queries or prompts with stored data, ensuring AI responses (e.g., in Agentforce prompt templates) are grounded in accurate, verifiable information from Data Cloud. This enhances trustworthiness by linking outputs to source data, making it the primary function of the AI Retriever. This aligns with Salesforce documentation and is the correct answer.
* Option B: It monitors and aggregates data quality metrics across various data pipelines to ensure only high-integrity data is used for strategic decision-making.Data quality monitoring is handled by other Data Cloud features, such as Data Quality Analysis or ingestion validation tools, not the AI Retriever. The Retriever's role is retrieval, not quality assessment or pipeline management. This option is incorrect as it misattributes functionality unrelated to the AI Retriever.
* Option C: It automatically extracts and reformats raw data from diverse sources into standardized datasets for use in historical trend analysis and forecasting.Data extraction and standardization are part of Data Cloud's ingestion and harmonization processes (e.g., via Data Streams or Data Lake), not the AI Retriever's function. The Retriever works with already-indexed data to fetch results, not to process or reformat raw data. This option is incorrect.
Why Option A is Correct:
The AI Retriever's core purpose is to perform contextual searches over indexed data, enabling AI grounding with reliable information. This is critical for Agentforce agents to provide accurate responses, as outlined in Data Cloud and Agentforce documentation.
References:
Salesforce Data Cloud Documentation: AI Retriever - Describes its role in contextual searches for grounding.
Trailhead: Data Cloud for Agentforce - Explains how the AI Retriever fetches relevant data for AI responses.
Salesforce Help: Grounding with Data Cloud - Confirms the Retriever's search functionality over indexed repositories.

NEW QUESTION # 85
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Latest Test Agentforce-Specialist Experience - Valid Exam Agentforce-Specialist Blueprint

Valid Exam Agentforce-Specialist Blueprint, Exam Agentforce-Specialist Collection

Salesforce Agentforce-Specialist Exam Syllabus Topics:

Salesforce Certified Agentforce Specialist Sample Questions (Q80-Q85):

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