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[General] New Agentforce-Specialist Test Materials & Agentforce-Specialist Valid Brain

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【General】 New Agentforce-Specialist Test Materials & Agentforce-Specialist Valid Brain

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Salesforce Agentforce-Specialist Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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 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
  • 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 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 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.

Salesforce Certified Agentforce Specialist Sample Questions (Q292-Q297):NEW QUESTION # 292
Universal Containers wants to allow its service agents to query the current fulfillment status of an order with natural language. There is an existing auto launched flow to query the information from Oracle ERP, which is the system of record for the order fulfillment process.
How should An Agentforce apply the power of conversational AI to this use case?
  • A. Create a Flex prompt template in Prompt Builder.
  • B. Configure the Integration Flow Standard Action in Agent.
  • C. Create a custom copilot action which calls a flow.
Answer: C
Explanation:
To enableUniversal Containersservice agents to query the current fulfillment status of an order using natural language and leverage an existing auto-launched flow that queries Oracle ERP, the best solution is tocreate a custom copilot action that calls the flow. This action will allowAgentto interact with the flow and retrieve the required order fulfillment information seamlessly. Custom copilot actions can be tailored to call various backend systems or flows in response to user requests.
* Option Bis correct because it enables integration betweenAgentand the flow that connects to Oracle ERP.
* Option A(Flex prompt template) is more suited for static responses and not for invoking flows.
* Option C(Integration Flow Standard Action) is not directly related to creating a specific copilot action for this use case.
:
Salesforce Agent Actions:https://help.salesforce.com/s/ar ... copilot_actions.htm

NEW QUESTION # 293
Which feature in the Einstein Trust Layer helps to minimize the risks of jailbreaking and prompt injection attacks?
  • A. Secure Data Retrieval and Grounding
  • B. Prompt Defense
  • C. Data Masking
Answer: B
Explanation:
The Einstein Trust Layer is designed to ensure responsible and compliant AI usage. Data Masking (B) is the mechanism that directly addresses compliance with data protection regulations like GDPR by obscuring or anonymizing sensitive personal data (e.g., names, emails, phone numbers) before it is processed by AI models. This prevents unauthorized exposure of personally identifiable information (PII) and ensures adherence to privacy laws.
Salesforce documentation explicitly states that Data Masking is a core component of the Einstein Trust Layer, enabling organizations to meet GDPR requirements by automatically redacting sensitive fields during AI interactions. For example, masked data ensures that PII is not stored or used in AI model training or inference without explicit consent.
In contrast:
* Toxicity Scoring (A) identifies harmful or inappropriate content in outputs but does not address data privacy.
* Prompt Defense (C) guards against malicious prompts or injection attacks but focuses on security rather than data protection compliance.

NEW QUESTION # 294
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 in Einstein's Trust Layer, the next important step is to test and confirm that 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 B is 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 # 295
How does the AI Retriever function within Data Cloud?
  • A. It automatically extracts and reformats raw data from diverse sources into standardized datasets for use in historical trend analysis and forecasting.
  • 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 performs contextual searches over an indexed repository to quickly fetch the most relevant documents, enabling grounding AI responses with trustworthy, verifiable information.
Answer: C
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 # 296
Which statement explains why a company might prefer a hybrid search index in Data Cloud for Agentforce?
  • A. Hybrid search indexes support both literal keyword matches and semantic recall, useful when queries mix specific terms and intent.
  • B. Hybrid search indexes process queries faster than vector search because they eliminate the need for semantic embedding.
  • C. Vector embedding in hybrid search are prefiltered by keyword matches, reducing computational overhead and improving response accuracy.
Answer: A
Explanation:
According to the AgentForce Data Cloud Search Indexing Guide and RAG Optimization Framework, a hybrid search index combines both keyword-based (lexical) and vector-based (semantic) search capabilities.
This dual-mode retrieval enables AgentForce to interpret user intent while still honoring exact keyword matches.
In many enterprise scenarios, queries contain a mixture of specific terms (e.g., "contract ID 54321") and semantic intent (e.g., "renew my subscription"). A purely vector search might overlook exact keywords, while a keyword-only search might miss semantically relevant results. Hybrid indexing ensures that both types of retrieval are available simultaneously - providing the best balance of precision and contextual understanding.
Option A is incorrect because hybrid search still uses embeddings; it doesn't eliminate them. Option B partially describes the hybrid search process but oversimplifies its purpose - the primary goal isn't just prefiltering for performance, but combining semantic recall and exact matching for more relevant, balanced results.
Thus, per AgentForce documentation, hybrid search indexes are preferred when organizations need both literal keyword matching and semantic understanding for complex, natural-language queries.
Reference: AgentForce Data Cloud Documentation - "Hybrid Search Index: Combining Keyword and Semantic Retrieval."

NEW QUESTION # 297
......
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