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：
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 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：
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 4：
A data scientist has produced three new models for a single machine learning problem. In the past, the solution used just one model. All four models have nearly the same prediction latency, but a machine learning engineer suggests that the new solution will be less time efficient during inference.
In which situation will the machine learning engineer be correct?
A. When the new solution requires the use of fewer feature variables than the original model
B. When the new solution's models have an average size that is larger than the size of the original model
C. When the new solution requires if-else logic determining which model to use to compute each prediction
D. When the new solution requires that each model computes a prediction for every record
E. When the new solution's models have an average latency that is larger than the size of the original model
正解：D
解説: (Pass4Test メンバーにのみ表示されます)

質問 5：
Which of the following describes the relationship between native Spark DataFrames and pandas API on Spark DataFrames?
A. pandas API on Spark DataFrames are more performant than Spark DataFrames
B. pandas API on Spark DataFrames are single-node versions of Spark DataFrames with additional metadata
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
正解：C
解説: (Pass4Test メンバーにのみ表示されます)

質問 6：
A data scientist is working with a feature set with the following schema:

The customer_id column is the primary key in the feature set. Each of the columns in the feature set has missing values. They want to replace the missing values by imputing a common value for each feature.
Which of the following lists all of the columns in the feature set that need to be imputed using the most common value of the column?
A. customer_id
B. customer_id, loyalty_tier
C. spend
D. loyalty_tier
E. units
正解：D
解説: (Pass4Test メンバーにのみ表示されます)