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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 assigned User permission set includes access to the prompt template used to access the Knowledge articles.
B. Create a new permission set that assigns "Manage Knowledge" and assign it to the Agentforce Service Agent User.
C. Ensure the Data Cloud User permission set has been assigned to the Agentforce Service Agent User.
Answer: C
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.
NEW QUESTION # 266
A company wants to retrieve patient history details to augment the AI agent response and plans to use the Data Cloud search index feature. What is best practice when considering retrieval- augmented generation (RAG) for information that may contain personally identifiable information (PII)?
A. Encrypt embeddings, but still index PII records
B. Depend on the agent's prompt to avoid exposing PII
C. Mask sensitive fields and index only non-PII data
Answer: C
NEW QUESTION # 267
Which statement explains why a company might prefer a hybrid search index in Data Cloud for Agentforce?
A. Vector embedding in hybrid search are prefiltered by keyword matches, reducing computational overhead and improving response accuracy.
B. Hybrid search indexes process queries faster than vector search because they eliminate the need for semantic embedding.
C. Hybrid search indexes support both literal keyword matches and semantic recall, useful when queries mix specific terms and intent.
Answer: C
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.
NEW QUESTION # 268
Universal Containers (UC) is Implementing Service AI Grounding to enhance its customer service operations. UC wants to ensure that its AI- generated responses are grounded in the most relevant data sources. The team needs to configure the system to include all supported objects for grounding. Which objects should UC select to configure Service AI Grounding?
A. Case, Knowledge, and Case Notes
B. Case and Knowledge
C. Case, Case Emails, and Knowledge
Answer: B
Explanation:
Universal Containers (UC) is implementing Service AI Grounding to enhance its customer service operations. They aim to ensure that AI-generated responses are grounded in the most relevant data sources and need to configure the system to include all supported objects for grounding.
Supported Objects for Service AI Grounding:
Case
Knowledge
Case Object:
Role in Grounding: Provides contextual data about customer inquiries, including case details, status, and history.
Benefit: Grounding AI responses in case data ensures that the information provided is relevant to the specific customer issue being addressed.
Knowledge Object:
Role in Grounding: Contains articles and documentation that offer solutions and information related to common issues.
Benefit: Utilizing Knowledge articles helps the AI provide accurate and helpful responses based on verified information.
Exclusion of Other Objects:
Case Notes and Case Emails:
Not Supported for Grounding: While useful for internal reference, these objects are not included in the supported objects for Service AI Grounding.
Reason: They may contain sensitive or unstructured data that is not suitable for AI grounding purposes.
NEW QUESTION # 269
Once a data source is chosen for an Agentforce Data Library, what is true about changing that data source later?
A. The Data Retriever can be reconfigured to use a different data source.
B. The data source can be changed through the Data Cloud settings.
C. The data source cannot be changed after it is selected.
Answer: C
Explanation:
Why is "The data source cannot be changed after it is selected" the correct answer?
When configuring an Agentforce Data Library, the data source selection is permanent. Once a data source is set, it cannot be modified or replaced. This design ensures data consistency, security, and reliability within Salesforce's AI-driven environment.
Key Considerations in Agentforce Data Library
Data Source Lock-In
The chosen data source remains fixed to maintain data integrity and avoid inconsistencies.
Any updates or modifications require creating a new Data Library instead of modifying the existing one.
Why Can't the Data Source Be Changed?
The data source defines the foundation of AI-driven workflows, and any modification would disrupt processing logic.
Agentforce tools rely on structured datasets to enable AI-powered recommendations, and changing data sources could lead to inconsistencies in grounding techniques.
Workarounds for Changing Data Sources
If an organization needs to use a different data source, a new Agentforce Data Library must be created and configured from scratch.
Old data can be manually migrated into the new data source for continuity.
NEW QUESTION # 270
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