Hybrid Search Quiz

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

1. Ndedzipi nzira mbiri dzekutsvagisa dzinowanzobatanidza kutsvaga kwakasanganiswa?
   • Sparse keyword kutsvaga (semuenzaniso, BM25) uye dense vector kutsvaga
   • Mhando mbiri dzemitauro mikuru dzakasiyana dzichivhotera mhinduro
   • SQL mibvunzo uye yakazara-zvinyorwa zvinyorwa chete
   • Tsvakiridzo yemifananidzo uye tsvaga yekuteerera
2. Nei Reciprocal Rank Fusion (RRF) ichiwanzo shandiswa kubatanidza mhinduro?
   • Inodzidzisa itsva neural network yemubvunzo wega wega
   • Inosanganisa magwaro nechinzvimbo pachinzvimbo chezvibodzwa zvinyoro, kudzivirira kukanganiswa kwechiyero
   • Inodzima chero gwaro rinowanikwa mumazita ese ari maviri
   • Iyo inongochengeta iyo imwechete yepamusoro mhedzisiro kubva kune yega retriever
3. Ndeipi mamiriro anonyanya kufarira sparse (keyword) chikamu chekutsvaga kwakasanganiswa?
   • Kutsvaga magwaro anodimikira mubvunzo nemashoko akasiyana
   • Kunzwisisa inzwi remanzwiro emutsara
   • Kufananidza nhamba chaiyo yechikamu kana kodhi yekukanganisa inofanirwa kuoneka sezvairi
   • Kushandura mubvunzo kuenda kune mumwe mutauro
4. Ndeipi kushaya simba kukuru kwekushandisa dense vector kutsvaga chete?
   • Haikwanise kunzwisisa masinonimimi kana zvirevo
   • Inoshanda chete pamifananidzo
   • Izvo hazvidi embeddings zvachose
   • Inogona kupotsa chaiyo isingawanzo ma tokeni senge macode chaiwo kana surnames
5. Chii chinodzidzwa sparse modhi seSPLADE inoita?
   • Inoburitsa uremu hwezwi rinodudzirwa uchishandisa neural network, kusanganisa sparse uye dense pfungwa
   • Inobvisa ese keyword kufananidzwa kubva pakutsvaga
   • Inongochengeta mifananidzo semavekita
   • Inotsiva kudiwa kwekutora chero kupi zvako
6. What can combining lexical and dense retrieval provide to a RAG system?
   • A guarantee that every generated statement is true
   • Potentially more varied candidate evidence, subject to evaluation of retrieval and answer quality
   • A replacement for grounding sources
   • An automatic legal review of generated answers
7. What tradeoff did BEIR report for reranking and late-interaction methods on average?
   • They always ran faster and used less compute than BM25
   • They made retrieval independent of the corpus
   • They often achieved stronger zero-shot performance but at higher computational cost
   • They guaranteed a correct generated answer
8. What did the BEIR benchmark conclude about BM25 in zero-shot retrieval?
   • It always beats every neural retriever
   • It cannot retrieve exact terms
   • It is a dense-vector model
   • It remains a robust baseline across diverse datasets