Idanwo ifibọ

Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

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. Metiriki wo ni a lo lati ṣe afiwe awọn ifibọ meji?
   • Iyatọ iwọn faili
   • Tito labidi
   • Iwọn ọrọ
   • Cosine ibajọra
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. Kini apẹẹrẹ 'ọba - ọkunrin + obinrin ≈ ayaba' ṣe afihan nipa awọn ifibọ?
   • Wọn le gba awọn ibatan nipasẹ iṣiro fekito
   • Wọn tọju awọn itumọ gangan ti awọn ọrọ
   • Wọn nikan ṣiṣẹ fun awọn ofin ọba
   • Wọn jẹ laileto ati asan
6. Ewo ni eewu gidi nigba lilo awọn ifibọ?
   • Wọn ko le wa ni ipamọ ni ibi ipamọ data
   • Wọn nikan ṣiṣẹ ni English
   • Wọn le jogun ati ṣe afihan awọn aiṣedeede ti o wa ninu data ikẹkọ wọn
   • Wọn ṣe gbogbo awọn wiwa ni deede-baramu nikan
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