Databricks Databricks-Machine-Learning-Associate 問題集

質問 1：
A data scientist has written a data cleaning notebook that utilizes the pandas library, but their colleague has suggested that they refactor their notebook to scale with big data.
Which of the following approaches can the data scientist take to spend the least amount of time refactoring their notebook to scale with big data?
A. They can refactor their notebook to process the data in parallel.
B. They can refactor their notebook to use the PySpark DataFrame API.
C. They can refactor their notebook to utilize the pandas API on Spark.
D. They can refactor their notebook to use the Scala Dataset API.
E. They can refactor their notebook to use Spark SQL.
正解：C
解説: (Topexam メンバーにのみ表示されます)

質問 2：
A data scientist is using Spark ML to engineer features for an exploratory machine learning project.
They decide they want to standardize their features using the following code block:

Upon code review, a colleague expressed concern with the features being standardized prior to splitting the data into a training set and a test set.
Which of the following changes can the data scientist make to address the concern?
A. Utilize the Pipeline API to standardize the test data according to the training data's summary statistics
B. Utilize the MinMaxScaler object to standardize the test data according to global minimum and maximum values
C. Utilize a cross-validation process rather than a train-test split process to remove the need for standardizing data
D. Utilize the Pipeline API to standardize the training data according to the test data's summary statistics
E. Utilize the MinMaxScaler object to standardize the training data according to global minimum and maximum values
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 3：
A data scientist wants to use Spark ML to impute missing values in their PySpark DataFrame features_df. They want to replace missing values in all numeric columns in features_df with each respective numeric column's median value.
They have developed the following code block to accomplish this task:

The code block is not accomplishing the task.
Which reasons describes why the code block is not accomplishing the imputation task?
A. The fit method needs to be called instead of transform.
B. It does not impute both the training and test data sets.
C. The inputCols and outputCols need to be exactly the same.
D. It does not fit the imputer on the data to create an ImputerModel.
正解：D
解説: (Topexam メンバーにのみ表示されます)

質問 4：
A machine learning engineer is trying to scale a machine learning pipeline by distributing its feature engineering process.
Which of the following feature engineering tasks will be the least efficient to distribute?
A. Target encoding categorical features
B. Imputing missing feature values with the true median
C. Imputing missing feature values with the mean
D. Creating binary indicator features for missing values
E. One-hot encoding categorical features
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 5：
A data scientist is utilizing MLflow Autologging to automatically track their machine learning experiments. After completing a series of runs for the experiment experiment_id, the data scientist wants to identify the run_id of the run with the best root-mean-square error (RMSE).
Which of the following lines of code can be used to identify the run_id of the run with the best RMSE in experiment_id?
A.

B.

C.

D.

正解：C
解説: (Topexam メンバーにのみ表示されます)

質問 6：
Which of the following approaches can be used to view the notebook that was run to create an MLflow run?
A. Click the "Start Time" link in the row corresponding to the run in the MLflow experiment page
B. Click the "Models" link in the row corresponding to the run in the MLflow experiment paqe
C. Open the MLmodel artifact in the MLflow run paqe
D. Click the "Source" link in the row corresponding to the run in the MLflow experiment page
正解：D
解説: (Topexam メンバーにのみ表示されます)

質問 7：
Which of the following statements describes a Spark ML estimator?
A. An estimator is an alqorithm which can be fit on a DataFrame to produce a Transformer
B. An estimator chains multiple alqorithms toqether to specify an ML workflow
C. An estimator is an evaluation tool to assess to the quality of a model
D. An estimator is a trained ML model which turns a DataFrame with features into a DataFrame with predictions
E. An estimator is a hyperparameter arid that can be used to train a model
正解：A
解説: (Topexam メンバーにのみ表示されます)