Guide-yakabatana mibvunzo · Pakati Nhanho
Apache Airflow yeML Workflows Quiz
Shandisa Airflow basa kutsamira, kudzokorodza, uye kudzosera kumashure kuML workflows, kusanganisira iyo inokonzeresa mitemo inoona kana mabasa agadzirira kuita.
Mubvunzo 1 ye 8
MuApache Airflow, chii chinonzi musimboti abstraction chinoshandiswa kutsanangura mafambiro ebasa?
Tarisa mubvunzo wese8 mibvunzo
Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.
- MuApache Airflow, chii chinonzi musimboti abstraction chinoshandiswa kutsanangura mafambiro ebasa?
- A YAML manifest
- A SQL maonero
- A DAG (Yakatungamirirwa Acyclic Graph) yakanyorwa muPython
- Bhuku reJupyter
- Ndeipi basa rinowanzoitwa neAirflow mupombi yeML?
- Inoronga uye kuronga mabasa anomhanya pane mamwe masisitimu
- Inodzidzisa maitiro ekudzidza akadzama zvakananga paGPUs
- Inochengetedza mavectors mundangariro
- Inoshanda modhi yekufungidzira pamusoro peHTTP
- Ndeipi Airflow inovaka inoshandiswa kumirira mamiriro ekunze, senge faira rinosvika mudura?
- A variable
- Dziva rekubatanidza
- A macro
- Sensor
- Chii chinonzi Airflow 'backfill' chinokutendera kuti uite?
- Delete yekare DAG inomhanya zvachose
- Ita DAG pamusoro pemazuva ekare kuti ugadzirise zvakare data rekare
- Encrypt iyo metadata database
- Dzima mugadziri kwenguva
- Which Airflow component determines when eligible task instances should be scheduled?
- Murongi
- The graph view in the web UI
- A model’s feature encoder
- The artifact file itself
- Chii chinonzi 'data-aware kuronga' chakaunzwa mune nyowani Airflow shanduro?
- Encrypting data yese yakapfuura pakati pemabasa
- Kunyora otomatiki data rekudzidziswa
- Kuderedza huwandu hweGPUs pabasa
- Kukonzera maDAG kana dataset ichigadziridzwa kwete pawachi yakatarwa
- 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