Quiz biy boole

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

1. What is an embedding in the numerical representation sense used here?
   • A dictionary definition that must be stored verbatim
   • A required compression format for image files
   • A vector representing an item for a model or comparison task
   • An exact database row identifier only
2. What can closeness in a trained semantic embedding space indicate?
   • Similarity learned under that model’s objective
   • A guarantee that two statements are equally true
   • An identical byte sequence in every case
   • A universal relation independent of training
3. Ban metric lañuy faral di jëfandikoo ngir méngale ñaari embedding?
   • wuute ci dayo fichier
   • Alfabet
   • lim baat
   • Nuru cosinus
4. What tradeoff can an approximate embedding index such as HNSW introduce?
   • A guarantee of complete exact retrieval in every configuration
   • Faster search with possible missed exact nearest neighbors
   • A required change of every vector to a word definition
   • Automatic removal of all training-data bias
5. Lan la misaalu 'buur - góor + jigéen ≈ reine' di wane ci wàllu embeddings?
   • Mën nañu jàpp diggante yi jaaraleko ci arithmétique vecteur
   • Dañuy denc lu leer ci kàddu yi
   • Dañuy liggéey ludul ngir am royalty
   • Dañuy nuru lu bari te amul benn njariñ
6. Lan mooy risk dëgg sooy jëfandikoo embeddings?
   • Mënu leen denc ci benn base de done
   • Angale rek lañuy liggéey
   • Mën nañu ër ak wane ay njuumte yu am ci seeni done tàggat
   • Dañuy def seetlu yépp ñu méngoo ndànk
7. Two unit-length embedding vectors have dot product 0.8. What is their squared Euclidean distance?
   • 0.2
   • 0.4
   • 0.8
   • 1.6
8. When does an inner-product score equal cosine similarity for two nonzero embedding vectors?
   • Whenever their dimensions are equal
   • Whenever they came from two unrelated model families
   • Only when their coordinates are all zero
   • When both vectors have been normalized to unit length