DATABRICKS-MLA Question 14

Community Discussion

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• Chose A0

  - Correct answer : A — Promote the pipeline code into the production workspace so the production job retrains on production data and registers its own fresh model version. A . This matches Databricks' code-first promotion guidance: promoting the pipeline through environments and retraining in production keeps the deployed model consistent with production data and infrastructure rather than a possibly stale artifact. B . Copying a serialized artifact between environments bypasses retraining on production data and skips the governance and reproducibility benefits of running the pipeline where it will serve, so it is not the recommended approach when retraining is cheap. C . Re-uploading an artifact as a new run without rerunning the training code in production leaves the production environment without a reproducible pipeline, undermining lineage and future retraining. D . Having production depend on staging's tracking server couples environments together and removes the isolation MLOps practices rely on, since a change in staging would silently affect production.