CI/CD ngir jàngat masin Quiz

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

1. Lan la CI/CD ngir jàngu masin yokk ginaaw losisel CI/CD buñ miin?
   • Otomatisasioŋ ci done ak model, du kode kese
   • Dara — dafay nuru losisel CI/CD
   • Dajale bi gëna gaaw kese
   • Làkku prograam bu bees
2. Lan mooy 'CT' ci biir pipeline ML?
   • Toxal Kod
   • Taggat bu wéy
   • Test conteneur
   • Telemetri bu digg bi
3. What is a common evaluation gate before promoting a candidate ML model?
   • Promote it whenever training completes
   • Compare only model file sizes
   • Compare it with the production baseline on agreed metrics and promote only if policy thresholds pass
   • Skip evaluation if the code tests pass
4. Ci yii, yan ci ñoom mooy jumtukaay bi ñuy gëna jëfandikoo ngir defar ay pipeline ML?
   • Word
   • Lecteur média VLC
   • WinRAR
   • Tuyo Kubeflow
5. Lan moo waral pipeline ML mëna am jéego buy saytu schema done?
   • jàpp done yu baaxul wala yu jaarul yoon balaa muy yàq tàggat yaram
   • Ngir GPU bi gëna sedd
   • ngir kompresse done yi
   • Ngir soppi tur kolon yi ci anam wu mucc
6. In Flux GitOps, what does reconciliation aim to do?
   • Retrain every model whenever code is committed
   • Make the deployed environment match the declared desired state
   • Automatically change a model’s evaluation labels
   • Replace version-controlled configuration with manual edits
7. What is a useful model-artifact versioning practice?
   • Ignore training inputs after a model is deployed
   • Replace validation with a deployment timestamp
   • Version model artifacts with source data, code, and evaluation metadata
   • Use a schema check as a guarantee of model quality
8. What is a typical trigger for continuous training in an ML pipeline?
   • Every inference request must retrain the model
   • A model file being downloaded by a user
   • A CPU core reaching a particular temperature
   • A scheduled run, new approved data, or a monitoring signal such as drift