Databricks Databricks-Machine-Learning-Associate Exam With Confidence Using Practice Dumps

If the model object is passed to the estimator parameter ofCrossValidatorand the cross-validation object itself is placed as a stage in the pipeline, the feature engineering stages within the pipeline would be applied separately to each training and validation fold during cross-validation. This leads to a significant issue: the feature engineering stages would be computed using validation data, thereby leaking information from the validation set into the training process. This would potentially invalidate the cross-validation results by giving an overly optimistic performance estimate.References:

• Cross-validation and Pipeline Integration in MLlib (Avoiding Data Leakage in Pipelines).

To organize MLflow runs with one parent run for the tuning process and a child run for each unique combination of hyperparameter values, the data scientist can specifynested=Truewhen starting the child run. This approach ensures that each child run is properly nested under the parent run, maintaining a clear hierarchical structure for the experiment. This nesting helps in tracking and comparing different hyperparameter combinations within the same tuning process.References:

• MLflow Documentation (Managing Nested Runs).

For a data scientist using Spark SQL to import data and then performing machine learning tasks using Spark ML, the best-suited compute tool is a Standard cluster. A Standard cluster in Databricks provides the necessary resources and scalability to handle large datasets and perform distributed computing tasks efficiently, making it ideal for running Spark SQL and Spark ML operations.

References:

• Databricks documentation on clusters: Clusters in Databricks