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Databricks Certified Machine Learning Associate · Databricks
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- Correct answer : B — Each Databricks Runtime for Machine Learning release pins a tested, documented set of library versions, so re-running a job on the same runtime version reproduces the same dependency set. A . Databricks Runtime for Machine Learning does not silently auto-upgrade libraries on restart; a given runtime release keeps a fixed library set, which is what makes it reproducible rather than a moving target. B . Each Databricks Runtime for Machine Learning release ships a specific, tested, and documented set of library versions, so pinning a job to that runtime version reproduces the same scikit-learn, XGBoost, and dependency versions on a later run. C . Saving notebook output to Delta tables preserves data, not the software environment; it does not record or guarantee which library versions produced that output on a separate run. D . MLflow autologging can record library versions as part of a model's metadata, but it does not eliminate the need to track versions during training or reconstruct an environment on its own without a pinned runtime to install against.