quiz bu lëkkale ak tegtal · Diggante Niveau

Quiz Pinecone

Dafay muur seetlu vecteur ak këyitu dokimaa, metrics yu nuru, index yu amul serwër, ak li tax contexte seetlu ba leegi mingi soxla jàngat.

Yooni njiit yu jëm ciPinecone
Laaj 1 de 8

Lan mooy Pinecone njëkka denc ak seetee?

Xoolaatal laaj bu nekk8 laaj

Sooy def quiz bi, tontu yi ak leeral yi dañuy feeñ.

  1. Lan mooy Pinecone njëkka denc ak seetee?
    • Vecteur embeddings yiy wane li done yi di tekki
    • Tablo SQL yu ñor yu am ligne ak kolon
    • diisaayu xeetu jàngu masin buñ dajale
    • Baatu-jàll jëfandikukat buñ enkripte
  2. Lan moo waral ñuy boole Pinecone ak làkk yu mag?
    • Dafay tàggat xeetu làkk wi ci noonu
    • Dafay joxe memory bu biti suko defee model bi mëna joxe leeral yu am solo (RAG)
    • Dafay kompresse model bi ngir mu dox ci telefon yi
    • Dafay tekki li model bi génne ci yeneen làkk
  3. How does Pinecone describe its serverless index algorithms?
    • It uses HNSW for every serverless index
    • Users must manually select one universal algorithm
    • It selects proprietary algorithms such as Ananas, PQFS, or IVF with PQFS by slab size
    • It searches only by exact string match
  4. What architecture change does Pinecone highlight in its serverless database?
    • Every vector must be stored in one local in-memory graph
    • Each user must operate a separate physical database
    • Queries are answered without reading or scoring vectors
    • Storage and compute are decoupled so relevant index portions can be loaded for a query
  5. naka lañuy faral di nattee nuru gi am ci digganté ñaari vecteur?
    • ci diggante cosinus wala produit point
    • ci seeni dayo fichier ci megabyte
    • Bisu ñu leen sos
    • Ni ñuy lim baatu-caabi yiñ bokk
  6. Ban liggéey moo méngoo ak Pinecone?
    • Denc ak joxe nataali jeu video 3D
    • Wut dokimaa yu nuru luñuy tekki ak ab laaj
    • Doxal formul këyitu xayma
    • Dajale kodu cosaan
  7. What is a key step before using retrieved Pinecone matches in a generated answer?
    • Assume the nearest vector is always a correct source
    • Ignore metadata and access controls
    • Evaluate whether the retrieved records are relevant and current for the query
    • Treat retrieval as proof the model’s response is correct
  8. What kinds of searchable data do current Pinecone serverless indexes support?
    • Only image files with no metadata
    • Only one fixed embedding model
    • Only SQL tables with no text fields
    • Depending on schema, dense vectors, sparse vectors, and full-text-search fields