Databricks Databricks-Machine-Learning-Associate 問題集

質問 1：
A data scientist wants to use Spark ML to one-hot encode the categorical features in their PySpark DataFrame features_df. A list of the names of the string columns is assigned to the input_columns variable.
They have developed this code block to accomplish this task:

The code block is returning an error.
Which of the following adjustments does the data scientist need to make to accomplish this task?
A. They need to use Stringlndexer prior to one-hot encodinq the features.
B. They need to use VectorAssembler prior to one-hot encoding the features.
C. They need to remove the line with the fit operation.
D. They need to specify the method parameter to the OneHotEncoder.
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 2：
A machine learning engineer is trying to scale a machine learning pipeline pipeline that contains multiple feature engineering stages and a modeling stage. As part of the cross-validation process, they are using the following code block:

A colleague suggests that the code block can be changed to speed up the tuning process by passing the model object to the estimator parameter and then placing the updated cv object as the final stage of the pipeline in place of the original model.
Which of the following is a negative consequence of the approach suggested by the colleague?
A. The cross-validation process will no longer be reproducible
B. The model will take longer to train for each unique combination of hvperparameter values
C. The feature engineering stages will be computed using validation data
D. The model will be refit one more per cross-validation fold
E. The cross-validation process will no longer be
正解：C
解説: (Topexam メンバーにのみ表示されます)

質問 3：
An organization is developing a feature repository and is electing to one-hot encode all categorical feature variables. A data scientist suggests that the categorical feature variables should not be one-hot encoded within the feature repository.
Which of the following explanations justifies this suggestion?
A. One-hot encoding is not supported by most machine learning libraries.
B. One-hot encoding is a potentially problematic categorical variable strategy for some machine learning algorithms.
C. One-hot encoding is dependent on the target variable's values which differ for each application.
D. One-hot encoding is computationally intensive and should only be performed on small samples of training sets for individual machine learning problems.
E. One-hot encoding is not a common strategy for representing categorical feature variables numerically.
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 4：
A machine learning engineer would like to develop a linear regression model with Spark ML to predict the price of a hotel room. They are using the Spark DataFrame train_df to train the model.
The Spark DataFrame train_df has the following schema:

The machine learning engineer shares the following code block:

Which of the following changes does the machine learning engineer need to make to complete the task?
A. They need to call the transform method on train df
B. They need to convert the features column to be a vector
C. They do not need to make any changes
D. They need to split the features column out into one column for each feature
E. They need to utilize a Pipeline to fit the model
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 5：
A machine learning engineer wants to parallelize the inference of group-specific models using the Pandas Function API. They have developed the apply_model function that will look up and load the correct model for each group, and they want to apply it to each group of DataFrame df.
They have written the following incomplete code block:

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

質問 6：
A data scientist has developed a machine learning pipeline with a static input data set using Spark ML, but the pipeline is taking too long to process. They increase the number of workers in the cluster to get the pipeline to run more efficiently. They notice that the number of rows in the training set after reconfiguring the cluster is different from the number of rows in the training set prior to reconfiguring the cluster.
Which of the following approaches will guarantee a reproducible training and test set for each model?
A. Manually partition the input data
B. Write out the split data sets to persistent storage
C. Set a speed in the data splitting operation
D. Manually configure the cluster
正解：B
解説: (Topexam メンバーにのみ表示されます)