quiz bu lëkkale ak tegtal · Diggante Niveau

Jàngat Topp Quiz

Muur yi dañuy doxal metadone yi, metrik yi, mbiri art, dàntite done yi, jàpp environmaa bi, ak li dokimaa jàngat yi mëna jàppale.

Yooni njiit yu jëm ciTopp Jàngat
Laaj 1 de 8

Lan mooy jubluwaay bi njëkk ci topp jàngat ci jàngu masin?

Xoolaatal laaj bu nekk8 laaj

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

  1. Lan mooy jubluwaay bi njëkk ci topp jàngat ci jàngu masin?
    • Def runs yuñ mëna defaraat ak méngale ci enregistre seen contexte yépp
    • Ngir model yi gëna gaaw ci GPU bi
    • Ngir dugal ci saasi ay model ci liggéey bi
    • ngir kompresse poid model ngir denc
  2. Ban boole moo gëna am solo ngir mëna defar benn daw tàggat yaram?
    • Nimero dëggu bu mujj bi kese
    • Weersioŋu kod, ensemble done, ak hiperparametre
    • Tamperatiiru GPU bi ak gaawaayu ventilatër bi
    • Turu gëstukatu done yi
  3. Ci yii, yan ci ñoom mooy jumtukaay bi gëna siiw ci topp jàngat?
    • Fotoshop
    • Kubernet
    • MLflow
    • PostgreSQL
  4. Naka la toppukaayu jàngat yu bari di jàppee metrik yi ci diiru tàggat yaram?
    • Soo kopie nimero yi ci këyitu xayma ginaaw ga
    • Soo jëlee nataali terminal bi
    • Soo jàngee kodu source model bi
    • Jaaraleko ci dugal ay woote API yuñ dugal ci boucle de formation biy dem ci backend bi
  5. Fan la ay mbiri art yu mag yu melni diisaayu model yuñ denc di faral di denc ci sistemu toppu bi?
    • Ci dencukaay mbir/artefakt, ak royuwaay yiñ denc ci dencukaay metadone
    • Ci biir liiñ base de done
    • ci mémoire GPU bi ba fàww
    • Duñu leen denc dara
  6. Ak naka la toppu jàngat di doxee ci jamonoy misaali làkk yu mag yi?
    • Dañu koy bàyyi ndax LLM yi soxla wuñu ñu leen di topp
    • Leegi dafay jàpp itam ay verisioŋ yu gaaw, ay tombi jàngat ak ay génne yu baax
    • Leegi du topp ludul njëgu GPU
    • Dafa boole lépp ci losisel biy soppi wideo
  7. Why record a dataset version or content hash with an ML run?
    • To prove the model is unbiased
    • To guarantee identical GPU arithmetic
    • So the run can be linked to the exact data snapshot used
    • To replace the code version
  8. In MLflow Tracking, where are large model files typically stored relative to parameters and metrics?
    • Only inside the metric names
    • Always embedded in the database row for each parameter
    • They are not recorded by a tracking system
    • In an artifact store, while run metadata is kept in a backend store