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A data scientist has developed a random forest regressor rfr and included it as the final stage in a Spark MLPipeline pipeline. They then set up a cross-validation process with pipeline as the estimator in the following code block:

Which of the following is a negative consequence of includingpipelineas the estimator in the cross-validation process rather thanrfras the estimator?

A. The process will have a longer runtime because all stages of pipeline need to be refit or retransformed with each mode

B. The process will leak data from the training set to the test set during the evaluation phase

C. The process will be unable to parallelize tuning due to the distributed nature of pipeline

D. The process will leak data prep information from the validation sets to the training sets for each model

Which of the following describes the relationship between native Spark DataFrames and pandas API on Spark DataFrames?

A. pandas API on Spark DataFrames are single-node versions of Spark DataFrames with additional metadata

B. pandas API on Spark DataFrames are more performant than Spark DataFrames

C. pandas API on Spark DataFrames are made up of Spark DataFrames and additional metadata

D. pandas API on Spark DataFrames are less mutable versions of Spark DataFrames

A data scientist is wanting to explore summary statistics for Spark DataFrame spark_df. The data scientist wants to see the count, mean, standard deviation, minimum, maximum, and interquartile range (IQR) for each numerical feature.

Which of the following lines of code can the data scientist run to accomplish the task?

A. spark_df.summary ()

B. spark_df.stats()

C. spark_df.describe().head()

D. spark_df.printSchema()

E. spark_df.toPandas()

A data scientist is using MLflow to track their machine learning experiment. As a part of each of their MLflow runs, they are performing hyperparameter tuning. The data scientist would like to have one parent run for the tuning process with a child run for each unique combination of hyperparameter values. All parent and child runs are being manually started with mlflow.start_run.

Which of the following approaches can the data scientist use to accomplish this MLflow run organization?

A. Theycan turn on Databricks Autologging

B. Theycan specify nested=True when startingthe child run for each unique combination of hyperparameter values

C. Theycan start each child run inside the parentrun's indented code block usingmlflow.start runO

D. They can start each child run with the same experiment ID as the parent run

E. They can specify nested=True when starting the parent run for the tuningprocess

A machine learning engineer has been notified that a new Staging version of a model registered to the MLflow Model Registry has passed all tests. As a result, the machine learning engineer wants to put this model into production by transitioning it to the Production stage in the Model Registry.

From which of the following pages in Databricks Machine Learning can the machine learning engineer accomplish this task?

A. The home page of the MLflow Model Registry

B. The experiment page in the Experiments observatory

C. The model version page in the MLflow ModelRegistry

D. The model page in the MLflow Model Registry