Quiz bi gëna mag ci njariñu marginal

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

1. Lan mooy jafe-jafe bi njëkk bi MMR nara saafara?
   • Latency bu yeex ci base de done yu mag
   • Ortograafi bu jaarul yoon ci laaji jëfandikukat bi
   • Resultaa yu bari, yu jege ñaari yoon yu bawoo ci rang pertinance bu seer
   • Ñàkum done tàggat yaram ngir samp gi
2. Ci formul MMR, lan la valeur lambda bu jege 1 di fësal?
   • Pertinance bu sell ci laaj bi
   • wuute bu gëna mag ci diggante resultaa yi
   • Pondération bu tolloo ci kàndidaa yépp
   • Tanneef yu bari
3. naka lay def ba MMR di tann ay resultaam?
   • lépp benn yoon ci tànnal benn poñ
   • Ci anam wu mujjee ak jiwwu wu takku
   • ci xeetu alfabet bu këyit yi
   • Ak bëgg-bëgg, benn-benn, méngale ak mbir yuñ jota tànn
4. How can an embedding-based MMR implementation measure query relevance and document redundancy?
   • Using only a document's filename length
   • Using a similarity measure such as cosine, without MMR requiring that one measure universally
   • Using a mandatory audio-transcription score for all text
   • Using no relevance or inter-document comparison at all
5. Lan moo tax MMR am njariñ lu bari ci defar-yokkum (RAG)?
   • Dafay joxe ay firnde yuy mottali, yu dul redondance
   • Dafay wàññi diisaayu model bi
   • Dafay defar ci saasi modelu làkk wi
   • Dafay encrypter këyit yiñ jëlee
6. If MMR caches each candidate's maximum redundancy and updates it against each new selection, roughly how many similarities are needed for k picks from n candidates?
   • Always constant time independent of k and n
   • Exactly n factorial for every implementation
   • About k times n, ignoring vector dimension and initial setup
   • No document comparisons after the first query score
7. With lambda equal to 0.5, relevance 0.8, and maximum selected-item similarity 0.9, what is a candidate's MMR scoring term?
   • 0.85, from averaging the two similarities
   • Minus 0.05, from 0.5 times 0.8 minus 0.5 times 0.9
   • 0.72, from multiplying relevance and redundancy
   • 1.7, from adding the two similarities
8. Can an MMR reranker select a relevant document that the upstream retrieval candidate pool omitted?
   • Yes; diversity scoring invents missing source documents
   • Yes; a lower lambda automatically searches the whole corpus
   • Yes; reranking changes the document's original publication date
   • No; it chooses among the supplied candidates unless the upstream pool is expanded