Universal Containers wants an AI agent to answer questions about warranties using unstructured data stored...

Comprehensive and Detailed Explanation From Exact Extract:

The guide on RAG and search indexes indicates that if you need fine‑tuned retrieval behaviour (such as filtering by product line and ranking by recency), you should build a custom retriever. The documentation states: “You can add ranking factors such as recency and popularity at the time of index creation … Use pre‑filter fields and ranking factors.” Also: “When you create a search index, Data 360 automatically creates a default retriever … you can create custom retrievers in Einstein Studio to refine search criteria.” Hence to satisfy filtering by product line and recency ranking, the correct answer is B. Option A (default retriever) does not guarantee the filter/ranking customization; Option C (semantic embeddings with default metadata filters) may offer some filter capability but doesn’t explicitly provide ranking by recency and fine filter by product line. Thus B is correct.