Databricks-Machine-Learning-Associate 試験問題を無料オンラインアクセス

Which of the following tools can be used to parallelize the hyperparameter tuning process for single-node machine learning models using a Spark cluster?

• A.Autoscaling clusters
• B.Spark ML
• C.Autoscaling clusters
• D.MLflow Experiment Tracking
• E.Delta Lake

A machine learning engineering team has a Job with three successive tasks. Each task runs a single notebook. The team has been alerted that the Job has failed in its latest run.
Which of the following approaches can the team use to identify which task is the cause of the failure?

• A.Review the matrix view in the Job's runs
• B.Run each notebook interactively
• C.Change each Task's setting to use a dedicated cluster
• D.Migrate the Job to a Delta Live Tables pipeline

Which of the following is a benefit of using vectorized pandas UDFs instead of standard PySpark UDFs?

• A.The vectorized pandas UDFs work on distributed DataFrames
• B.The vectorized pandas UDFs allow for the use of type hints
• C.The vectorized pandas UDFs process data in memory rather than spilling to disk
• D.The vectorized pandas UDFs allow for pandas API use inside of the function
• E.The vectorized pandas UDFs process data in batches rather than one row at a time

A data scientist has replaced missing values in their feature set with each respective feature variable's median value. A colleague suggests that the data scientist is throwing away valuable information by doing this.
Which of the following approaches can they take to include as much information as possible in the feature set?

• A.Impute the missing values using each respective feature variable's mean value instead of the median value
• B.Refrain from imputing the missing values in favor of letting the machine learning algorithm determine how to handle them
• C.Create a binary feature variable for each feature that contained missing values indicating whether each row's value has been imputed
• D.Remove all feature variables that originally contained missing values from the feature set
• E.Create a constant feature variable for each feature that contained missing values indicating the percentage of rows from the feature that was originally missing

The implementation of linear regression in Spark ML first attempts to solve the linear regression problem using matrix decomposition, but this method does not scale well to large datasets with a large number of variables.
Which of the following approaches does Spark ML use to distribute the training of a linear regression model for large data?

• A.Least-squares method
• B.Iterative optimization
• C.Singular value decomposition
• D.Logistic regression
• E.Spark ML cannot distribute linear regression training