試験Agentforce-Specialist-JPN トピック3 問題171 スレッド
Salesforce Agentforce-Specialist-JPNのリアル試験問題集
問題 #: 171
トピック #: 3
問題 #: 171
トピック #: 3
Universal Containers では、製品ドキュメントが頻繁に変更されるため、検索の正確性を維持したいと考えています。
企業はどのようなアプローチを実施すべきでしょうか?
企業はどのようなアプローチを実施すべきでしょうか?
おすすめの解答：B 解答を投票する
Comprehensive and Detailed Explanation From Exact Extract of AgentForce Documents:
According to the official AgentForce implementation guidelines and RAG (Retrieval-Augmented Generation) architecture within Salesforce, maintaining retrieval accuracy depends on ensuring that embeddings and indexed content remain synchronized with the most recent data. When product documentation or knowledge base content changes, the underlying text used for vector embeddings must also be updated to reflect the new information.
The AgentForce documentation clearly specifies that when content is modified, the recommended practice is to rebuild the search index. This process regenerates the document chunks, re-embeds them using the latest model, and updates the index used by the retrieval system. This ensures that queries return the most current and relevant responses aligned with the updated content.
Leaving embeddings unchanged (Option A) would cause retrievals to surface outdated or irrelevant information, as the underlying semantic representations would no longer match the source material. Similarly, manually deleting stale data chunks (Option C) does not ensure a full refresh of vector data and can lead to incomplete or inconsistent results.
Therefore, as per AgentForce best practices, the correct approach is Option B - Rebuild the search index, ensuring that all embeddings, chunks, and indexed data are aligned with the latest version of the content.
Reference: AgentForce Implementation Guide - "Maintaining Retrieval Accuracy Through Index Rebuilding" section.
According to the official AgentForce implementation guidelines and RAG (Retrieval-Augmented Generation) architecture within Salesforce, maintaining retrieval accuracy depends on ensuring that embeddings and indexed content remain synchronized with the most recent data. When product documentation or knowledge base content changes, the underlying text used for vector embeddings must also be updated to reflect the new information.
The AgentForce documentation clearly specifies that when content is modified, the recommended practice is to rebuild the search index. This process regenerates the document chunks, re-embeds them using the latest model, and updates the index used by the retrieval system. This ensures that queries return the most current and relevant responses aligned with the updated content.
Leaving embeddings unchanged (Option A) would cause retrievals to surface outdated or irrelevant information, as the underlying semantic representations would no longer match the source material. Similarly, manually deleting stale data chunks (Option C) does not ensure a full refresh of vector data and can lead to incomplete or inconsistent results.
Therefore, as per AgentForce best practices, the correct approach is Option B - Rebuild the search index, ensuring that all embeddings, chunks, and indexed data are aligned with the latest version of the content.
Reference: AgentForce Implementation Guide - "Maintaining Retrieval Accuracy Through Index Rebuilding" section.
Morikawa 2026-08-23 09:41:07
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