Databricks Certified Machine Learning Professional Practice Mode

Community Discussion

Written by other learners. Comments can disagree with the marked answer — check the official documentation when in doubt.

• Chose A0

  ,B,C - Correct answer s : A, B, C — Hyperopt has been removed from Databricks Runtime ML versions released after 16.4 LTS, so it is no longer available by default on current runtimes.; Optuna with `MlflowSparkStudy` provides a currently supported path for Spark-distributed hyperparameter trials with MLflow-backed shared state.; Ray Tune is named alongside Optuna as a currently recommended option for distributed hyperparameter tuning on Databricks. A . Correct: Hyperopt's removal from Databricks Runtime ML after version 16.4 LTS is exactly the documented reason current-runtime users lose access to it and must migrate. B . Correct: `MlflowSparkStudy` combined with `MlflowStorage` is the documented current replacement path for Spark-distributed trials with shared, MLflow-backed state. C . Correct: current guidance names Ray Tune, alongside Optuna, as a supported option for distributed hyperparameter tuning going forward. D . Incorrect: Hyperopt did support parallel trial execution through `SparkTrials`; the deprecation is about runtime removal, not an inherent inability to run trials in parallel. E . Incorrect: the removal of Hyperopt from Databricks Runtime ML after 16.4 LTS applies generally, not only to GPU clusters, so CPU-only clusters on current runtimes are affected too. F . Incorrect: Optuna integrates directly with MLflow through `MlflowStorage` and `MlflowSparkStudy`, so migrating away from Hyperopt does not require giving up MLflow tracking.