Databricks Certified Machine Learning Professional

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Q_01 domain/d1 · medium single discuss →

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.

Q_02 domain/d1 · medium single discuss →

A data scientist performs hyperparameter tuning with k-fold cross-validation and then retrains the best model on the full training set. Using MLflow, they need to track metrics/hyperparameters per configuration, associate the final model with the search process, and enable easy comparison in the MLflow UI. Which run structure best satisfies these needs?

A Flat runs with tags to indicate hyperparameter sets and fold numbers, storing all metrics and models at the same level.
B Nested runs with a parent run for the whole experiment, child runs per hyperparameter set logging aggregated CV metrics, and the final model evaluation as a separate run of the best set.
C Nested runs with a parent run for each cross-validation fold and a child run for the best-performing hyperparameter set.
D Nested runs with a parent run for the whole experiment and child runs only for the final model training, logging all CV results as metrics in the parent run.