quiz bu lëkkale ak tegtal · Diggante Niveau
Apache Airflow ngir ML
Jëfandikool dependency liggéey Airflow, jéemaat, ak backfills ci ML workflows, boole ci sàrti trigger yiy wane kañ la liggéey yi mëna dox.
Laaj 1 de 8
Ci Apache Airflow, lan mooy abstraksioŋ bu mag bi ñuy jëfandikoo ngir fësal ab def-liggéey?
Xoolaatal laaj bu nekk8 laaj
Sooy def quiz bi, tontu yi ak leeral yi dañuy feeñ.
- Ci Apache Airflow, lan mooy abstraksioŋ bu mag bi ñuy jëfandikoo ngir fësal ab def-liggéey?
- Manifest YAML
- ab gis-gis SQL
- DAG (Graaf asiklik buñ teg) ñu bind ko ci Python
- Kaye Jupyter
- Ban cër la Airflow gëna bari luñu koy def ci gasoduc ML?
- Dafay orkestre ak waajal liggéey yiy dox ci yeneen sistem
- Dafay tàggat ay xeetu jàng yu xóot ci kaw GPU yi
- Dafay denc vecteur yu am solo ci mémoire bi
- Dafay joxe xeetu xam-xam ci kaw HTTP
- Ban construct Airflow lañuy jëfandikoo ngir xaar ab anam bu bawoo ci biti, lu melni fichier buy yegsi ci dencukaay bi?
- ab variable
- Pool lëkkaloo
- Makro
- ab kaptër
- Lan lay may nga def ci Airflow?
- efaase DAG yu yàgg yi ba fàww
- Doxal ab DAG ci ay bis yu weesu ngir defaraat ay done yu njëkk
- Encrypter base de done
- Desactivate scheduler bi ci diir bu gàtt
- Which Airflow component determines when eligible task instances should be scheduled?
- Organisatër bi
- The graph view in the web UI
- A model’s feature encoder
- The artifact file itself
- Luy 'programming bu xam done' biñ dugal ci Airflow yu bees yi?
- Encrypter bépp done bu jaar ci diggante liggéey yi
- Etiketu done tàggat yaram ci saasi
- Wàññi limu GPU yi ci liggéey bu nekk
- Dekki DAGs suñu yeesal ab done moo gën ñu koy yeesal ci ab montuer bu takku
- An Airflow cleanup task must run after upstream tasks finish even if one fails. Which setting controls this behavior?
- The number of model layers
- The cleanup task’s trigger rule
- The feature normalization method
- The UI theme selected for the DAG
- A team backfills three months of ML feature data in Airflow. What makes repeating an interrupted task safer?
- Appending duplicate outputs each time it retries
- Ignoring the run’s historical date
- An idempotent operation with the correct historical inputs
- Changing the training labels on every retry