Universal Containers wants to use an Al agent to answer questions about warranties, Warranty information has already been uploaded as unstructured data in Data Cloud. When answering user questions, th

Question

Options

• AUse the default retriever which automatically accounts for regency ranking.
• BBuild a custom retriever in Einstein Studio with product line filters and regency ranking.
• CApply semantic embeddings with default metadata filters to achieve the desired result

• A
  10% (7)
• B
  75% (50)
• C
  15% (10)

Explanation

The requirements here are specific and compound: results must be (1) filterable by a metadata field (product line) and (2) ranked by recency (most recently updated first). The default retriever in Agentforce/Einstein Search uses pure semantic similarity ranking and does not natively support custom metadata filters or recency-based re-ranking out of the box. To meet both requirements deterministically, the Agentforce Specialist must build a custom retriever in Einstein Studio, where they can configure metadata filter conditions (e.g., filter by product_line field) and apply a recency ranking signal to the results. Option A (default retriever) cannot be relied upon to apply regency ranking or product-line filtering without customization. Option C (semantic embeddings with default metadata filters) still relies on the default retriever's limited filter capabilities and does not address custom recency ranking, making it insufficient for these business requirements.

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