最新なDatabricks Databricks-Machine-Learning-Associate問題集（76題）、真実試験の問題を全部にカバー！

質問 1：
Which of the Spark operations can be used to randomly split a Spark DataFrame into a training DataFrame and a test DataFrame for downstream use?
A. TrainValidationSplit
B. TrainValidationSplitModel
C. DataFrame.where
D. DataFrame.randomSplit
E. CrossValidator
正解：D
解説: (Pass4Test メンバーにのみ表示されます)

質問 2：
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. Spark ML
B. Autoscaling clusters
C. Autoscaling clusters
D. MLflow Experiment Tracking
E. Delta Lake
正解：A
解説: (Pass4Test メンバーにのみ表示されます)

質問 3：
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. Iterative optimization
B. Spark ML cannot distribute linear regression training
C. Singular value decomposition
D. Least-squares method
E. Logistic regression
正解：A
解説: (Pass4Test メンバーにのみ表示されます)

質問 4：
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. Iterative optimization
B. Spark ML cannot distribute linear regression training
C. Singular value decomposition
D. Least-squares method
E. Logistic regression
正解：A

質問 5：
Which of the following tools can be used to distribute large-scale feature engineering without the use of a UDF or pandas Function API for machine learning pipelines?
A. pandas
B. Spark ML
C. PvTorch
D. Scikit-learn
E. Keras
正解：B
解説: (Pass4Test メンバーにのみ表示されます)

質問 6：
A machine learning engineer wants to parallelize the training of group-specific models using the Pandas Function API. They have developed the train_model function, and they want to apply it to each group of DataFrame df.
They have written the following incomplete code block:

Which of the following pieces of code can be used to fill in the above blank to complete the task?
A. train_model
B. groupedApplyIn
C. predict
D. applyInPandas
E. mapInPandas
正解：E
解説: (Pass4Test メンバーにのみ表示されます)