매달, 우리는 1000명 이상의 사람들이 시험 준비를 잘하고 시험을 잘 통과할 수 있도록 도와줍니다.
Salesforce Agentforce Specialist 온라인 연습
최종 업데이트 시간: 2025년06월06일
당신은 온라인 연습 문제를 통해 Salesforce Salesforce Agentforce Specialist 시험지식에 대해 자신이 어떻게 알고 있는지 파악한 후 시험 참가 신청 여부를 결정할 수 있다.
시험을 100% 합격하고 시험 준비 시간을 35% 절약하기를 바라며 Salesforce Agentforce Specialist 덤프 (최신 실제 시험 문제)를 사용 선택하여 현재 최신 182개의 시험 문제와 답을 포함하십시오.
정답:
Explanation:
In this scenario, the Einstein Copilot capability that best helps the agent is its ability to execute tasks based on available actions and answer questions using data from Knowledge articles. Einstein Copilot can assist the service agent by providing relevant Knowledge articles on canceling and rebooking flights, ensuring that the agent has access to the correct steps and procedures directly within the workflow.
This feature leverages the agent’s existing context (the travel itinerary) and provides actionable insights or next steps from the relevant Knowledge articles to help the agent quickly resolve the customer’s needs.
The other options are incorrect:
B refers to invoking a flow to create a Knowledge article, which is unrelated to the task of retrieving existing Knowledge articles.
C focuses on generating Knowledge articles, which is not the immediate need for this situation where the agent requires guidance on existing procedures.
Reference: Salesforce Documentation on Einstein Copilot
Trailhead Module on Einstein for Service
정답:
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.
Reference: Salesforce Einstein Documentation on Einstein Copilot Actions Salesforce AI Documentation on Large Language Models
정답:
Explanation:
The Einstein Trust Layer ensures that sensitive data is protected while generating useful and meaningful responses by masking sensitive data before it is sent to the Large Language Model (LLM) and then de-masking it during the response journey.
How It Works:
Data Masking in the Request Journey:
Sensitive Data Identification: Before sending the prompt to the LLM, the Einstein Trust Layer scans the input for sensitive data, such as personally identifiable information (PII), confidential business
information, or any other data deemed sensitive.
Masking Sensitive Data: Identified sensitive data is replaced with placeholders or masks. This ensures that the LLM does not receive any raw sensitive information, thereby protecting it from potential exposure.
Processing by the LLM:
Masked Input: The LLM processes the masked prompt and generates a response based on the masked data.
No Exposure of Sensitive Data: Since the LLM never receives the actual sensitive data, there is no risk of it inadvertently including that data in its output.
De-masking in the Response Journey:
Re-insertion of Sensitive Data: After the LLM generates a response, the Einstein Trust Layer replaces the placeholders in the response with the original sensitive data.
Providing Meaningful Responses: This de-masking process ensures that the final response is both meaningful and complete, including the necessary sensitive information where appropriate.
Maintaining Data Security: At no point is the sensitive data exposed to the LLM or any unintended recipients, maintaining data security and compliance.
Why Option A is Correct:
De-masking During Response Journey: The de-masking process occurs after the LLM has generated its response, ensuring that sensitive data is only reintroduced into the output at the final stage, securely and appropriately.
Balancing Security and Utility: This approach allows the system to generate useful and meaningful responses that include necessary sensitive information without compromising data security.
Why Options B and C are Incorrect:
Option B (Masked data will be de-masked during request journey):
Incorrect Process: De-masking during the request journey would expose sensitive data before it reaches the LLM, defeating the purpose of masking and compromising data security.
Option C (Responses that do not meet the relevance threshold will be automatically rejected):
Irrelevant to Data Protection: While the Einstein Trust Layer does enforce relevance thresholds to filter out inappropriate or irrelevant responses, this mechanism does not directly relate to the protection of sensitive data. It addresses response quality rather than data security.
Reference: Salesforce Agentforce Specialist Documentation - Einstein Trust Layer Overview:
Explains how the Trust Layer masks sensitive data in prompts and re-inserts it after LLM processing to protect data privacy.
Salesforce Help - Data Masking and De-masking Process:
Details the masking of sensitive data before sending to the LLM and the de-masking process during the response journey.
Salesforce Agentforce Specialist Exam Guide - Security and Compliance in AI:
Outlines the importance of data protection mechanisms like the Einstein Trust Layer in AI implementations.
Conclusion:
The Einstein Trust Layer ensures sensitive data is protected by masking it before sending any prompts to the LLM and then de-masking it during the response journey. This process allows Salesforce to generate useful and meaningful responses that include necessary sensitive information without exposing that data during the AI processing, thereby maintaining data security and compliance.
정답:
Explanation:
Einstein Copilot 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, Einstein Copilot 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. Einstein Copilot 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.
Reference: Salesforce Agentforce Specialist Documentation - Einstein Copilot Overview: Details how Einstein Copilot employs LLMs to interpret user inputs and generate responses within the Salesforce ecosystem.
Salesforce Help - How Einstein Copilot Works: Explains the underlying mechanisms of how Einstein Copilot processes user requests using AI technologies.
정답:
Explanation:
Dynamic grounding with secure data retrieval is a key feature in Salesforce's Einstein Trust Layer, which provides enhanced data protection and ensures that AI-generated outputs are both accurate and securely sourced. This feature allows relevant Salesforce data to be merged into the AI-generated responses, ensuring that the AI outputs are contextually aware and aligned with real-time CRM data.
Dynamic grounding means that AI models are dynamically retrieving relevant information from Salesforce records (such as customer records, case data, or custom object data) in a secure manner. This ensures that any sensitive data is protected during AI processing and that the AI model’s outputs are trustworthy and reliable for business use.
The other options are less aligned with the requirement:
Data masking refers to obscuring sensitive data for privacy purposes and is not related to merging Salesforce records into prompts.
Zero-data retention policy ensures that AI processes do not store any user data after processing, but this does not address the need to merge Salesforce record information into a prompt.
Reference: Salesforce Developer Documentation on Einstein Trust Layer Salesforce Security Documentation for AI and Data Privacy
정답:
Explanation:
To access externally-hosted models, such as a large language model (LLM) hosted on AWS, the Model Builder in Salesforce is the appropriate tool. Model Builder allows teams to integrate and deploy external AI models into the Salesforce platform, making it possible to leverage models hosted outside of Salesforce infrastructure while still benefiting from the platform's native AI capabilities.
Option B, App Builder, is primarily used to build and configure applications in Salesforce, not to integrate AI models.
Option C, Copilot Builder, focuses on building assistant-like tools rather than integrating external AI models.
Model Builder enables seamless integration with external systems and models, allowing Salesforce users to use external LLMs for generating AI-driven insights and automation.
Salesforce Agentforce Specialist
Reference: For more details, check the Model Builder guide here:
https://help.salesforce.com/s/articleView?id=sf.model_builder_external_models.htm
정답:
Explanation:
When the preview button is greyed out in a Flex prompt template, it is often because the records related to the prompt have not been selected. Flex prompt templates pull data dynamically from Salesforce records, and if there are no records specified for the prompt, it can't be previewed since there is no content to generate based on the template.
Option B, not saving or activating the prompt, would not necessarily cause the preview button to be
greyed out, but it could prevent proper functionality.
Option C, missing a merge field, would cause issues with the output but would not directly grey out the preview button.
Ensuring that the related records are correctly linked is crucial for testing and previewing how the prompt will function in real use cases.
Salesforce Agentforce Specialist
Reference: Refer to the documentation on troubleshooting Flex templates here:
https://help.salesforce.com/s/articleView?id=sf.flex_prompt_builder_troubleshoot.htm
정답:
Explanation:
When Universal Containers' AI data masking rules do not meet organizational privacy and security standards, the Agentforce Specialist should configure the data masking rules within the Einstein Trust Layer. The Einstein Trust Layer provides a secure and compliant environment where sensitive data can be masked or anonymized to adhere to privacy policies and regulations.
Option A, enabling data masking for sandbox refreshes, is related to sandbox environments, which are separate from how AI interacts with production data.
Option C, adding masking rules in the LLM setup, is not appropriate because data masking is managed through the Einstein Trust Layer, not the LLM configuration.
The Einstein Trust Layer allows for more granular control over what data is exposed to the AI model and ensures compliance with privacy regulations.
Salesforce Agentforce Specialist
Reference: For more information, refer to:
https://help.salesforce.com/s/articleView?id=sf.einstein_trust_layer_data_masking.htm
정답:
Explanation:
When the "Enrich event logs with conversation data" setting is enabled in Einstein Copilot, it allows An Agentforce or admin to view session data, including both the user input and copilot responses from interactions over the past 7 days. This data is crucial for monitoring how the copilot is being used, analyzing its performance, and improving future interactions based on past inputs.
This setting enriches the event logs with detailed conversational data for better insights into the interaction history, helping Agentforce Specialists track AI behavior and user engagement.
Option A, viewing the user click path, focuses on navigation but is not part of the conversation data enrichment functionality.
Option C, generating detailed reports over any time period, is incorrect because this specific feature is limited to data for the past 7 days.
Salesforce Agentforce Specialist
Reference: You can refer to this documentation for further insights:
https://help.salesforce.com/s/articleView?id=sf.einstein_copilot_event_logging.htm
정답:
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 about masked data― which ensures sensitive information is not exposed―and the toxicity score, which evaluates the generated content for inappropriate or harmful language.
Reference: Salesforce Agentforce Specialist Documentation - Einstein Trust Layer: Details the auditing capabilities, including logging of masked data and evaluation of generated responses for toxicity to maintain compliance and trust.
정답:
Explanation:
UC’s sales reps need an AI action to draft personalized emails based on past successful communications, reducing manual review time. Let’s evaluate the standard Agent actions.
Option A: Agent Action: Summarize Record
"Summarize Record" generates a summary of a record (e.g., Opportunity, Contact), useful for overviews but not for drafting emails or leveraging past communications. This doesn’t meet the requirement, making it incorrect.
Option B: Agent Action: Find Similar Opportunities
"Find Similar Opportunities" identifies past deals to inform strategy, not to draft emails. It provides data, not text generation, making it incorrect.
Option C: Agent Action: Draft or Revise Sales Email
The "Draft or Revise Sales Email" action in Agentforce for Sales (sometimes styled as "Draft Sales Email") uses the Atlas Reasoning Engine to generate personalized email content. It can analyze past successful communications (e.g., via Opportunity or Contact history) to tailor emails for renewals or deals, saving reps time. This directly addresses UC’s need, making it the correct answer.
Why Option C is Correct:
"Draft or Revise Sales Email" is a standard action designed for personalized email generation based on historical data, aligning with UC’s productivity goal per Salesforce documentation.
Reference: Salesforce Agentforce Documentation: Agentforce for Sales > Draft Sales Email C Details email generation.
Trailhead: Explore Agentforce Sales Agents C Covers email drafting with past data.
Salesforce Help: Sales Features in Agentforce C Confirms personalization capabilities.
정답:
Explanation:
UC wants to route SMS text messages from an Agentforce Service Agent to a service rep using a flow.
Let’s identify the correct Service Channel.
Option A: Messaging
In Salesforce, the "Messaging" Service Channel (part of Messaging for In-App and Web or SMS) handles text-based interactions, including SMS. When integrated with Omni-Channel Flow, the "Route Work" action uses this channel to route SMS messages to agents. This aligns with UC’s requirement for SMS routing, making it the correct answer.
Option B: Route Work Action
"Route Work" is an action in Omni-Channel Flow, not a Service Channel. It uses a channel (e.g., Messaging) to route work, so this is a component, not the channel itself, making it incorrect.
Option C: Live Agent
"Live Agent" refers to an older chat feature, not the current Messaging framework for SMS. It’s outdated and unrelated to SMS routing, making it incorrect.
Option D: SMS Channel
There’s no standalone "SMS Channel" in Salesforce Service Channels―SMS is encompassed within the "Messaging" channel. This is a misnomer, making it incorrect.
Why Option A is Correct:
The "Messaging" Service Channel supports SMS routing in Omni-Channel Flow, ensuring proper handoff from the Agentforce Service Agent to a rep, per Salesforce documentation.
Reference: Salesforce Agentforce Documentation: Omni-Channel Integration > Messaging C Details SMS in Messaging channel.
Trailhead: Omni-Channel Flow Basics C Confirms Messaging for SMS.
Salesforce Help: Service Channels C Lists Messaging for text-based routing.
정답:
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.
Reference: Salesforce Agentforce Documentation: Service Agent Setup > Permissions C Requires Knowledge access.
Trailhead: Set Up Agentforce Service Agents C Lists "Allow View Knowledge" need.
Salesforce Help: Knowledge in Agentforce C Confirms permission necessity.
정답:
Explanation:
UC requires an Agentforce Service Agent to deliver accurate, up-to-date policy and compliance info with specific criteria. Let’s evaluate.
Option A: Enable the agent to search all internal records and past customer inquiries.
Searching all records and inquiries risks irrelevant or outdated responses, conflicting with the need for published Knowledge grounding and immediate updates. This lacks specificity, making it
incorrect.
Option B: Set up an Agentforce Data Library to store and index policy documents for AI retrieval.
The Agentforce Data Library integrates with Salesforce Knowledge, indexing HR policies, compliance guidelines, and procedures for semantic search. It ensures grounding in published Knowledge articles, and updates (e.g., new article versions) are reflected instantly without reconfiguration, as the library syncs with Knowledge automatically. This meets all UC requirements, making it the correct answer.
Option C: Manually add policy responses into the AI model to prevent hallucinations.
Manually embedding responses into the model isn’t feasible―Agentforce uses pretrained LLMs, not custom training. It also doesn’t support real-time updates, making this incorrect.
Why Option B is Correct:
The Data Library meets all criteria―semantic search, Knowledge grounding, and instant updates― per Salesforce’s recommended approach.
Reference: Salesforce Agentforce Documentation: Data Library > Knowledge Integration C Details indexing and updates.
Trailhead: Build Agents with Agentforce C Covers Data Library for accurate responses.
Salesforce Help: Grounding with Knowledge C Confirms real-time sync.
정답:
Explanation:
The Agentforce Data Library enhances AI accuracy by grounding responses in curated, indexed data.
Let’s assess the scenarios.
Option A: When the AI agent must provide answers based on a curated set of policy documents that are stored, regularly updated, and indexed in the data library.
The Data Library is designed to store and index structured content (e.g., Knowledge articles, policy documents) for semantic search and grounding. It excels when an agent needs accurate, up-to-date responses from a managed corpus, like policy documents, ensuring relevance and reducing hallucinations. This is a prime use case per Salesforce documentation, making it the correct answer.
Option B: When the AI agent needs to combine data from disparate sources based on mutually common data, such as Customer Id and Product Id for grounding.
Combining disparate sources is more suited to Data Cloud’s ingestion and harmonization capabilities, not the Data Library, which focuses on indexed content retrieval. This scenario is less aligned, making it incorrect.
Option C: When data is being retrieved from Snowflake using zero-copy for vectorization and retrieval.
Zero-copy integration with Snowflake is a Data Cloud feature, but the Data Library isn’t specifically tied to this process―it’s about indexed libraries, not direct external retrieval. This is a different context, making it incorrect.
Why Option A is Correct:
The Data Library shines in curated, indexed content scenarios like policy documents, improving agent accuracy, as per Salesforce guidelines.
Reference: Salesforce Agentforce Documentation: Data Library > Use Cases C Highlights curated content grounding.
Trailhead: Ground Your Agentforce Prompts C Describes Data Library accuracy benefits.
Salesforce Help: Agentforce Data Library C Confirms policy document scenario.