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databricks-machine-learning-professional_d1_12bc9deb7671 domain/d1 · medium single

A data scientist wants to run Optuna hyperparameter optimization in a distributed manner across a multi-node Databricks cluster, with each trial's outcome logged to an MLflow experiment, and with minimal setup. Which approach meets these requirements?

A Provide the MLflow Experiment ID to Optuna's MLflowCallback and use Python async functionality to trigger multiple parallel optimization runs.
B Provide the MLflow Experiment ID to MLflowStorage and initialize MLflowSparkStudy to distribute the trials across the multi-node Databricks cluster.
C Provide the MLflow Experiment ID to Optuna's MLflowCallback and use MlflowSparkStudy to distribute the trials across the multi-node Databricks cluster.
D Provide the MLflow Experiment path to Optuna's MLflowCallback and distribute trials across a multi-node Ray on Spark cluster.

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