Databricks-Machine-Learning-Associate無料問題集「Databricks Certified Machine Learning Associate」

質問 1

A data scientist is attempting to tune a logistic regression model logistic using scikit-learn. They want to specify a search space for two hyperparameters and let the tuning process randomly select values for each evaluation.
They attempt to run the following code block, but it does not accomplish the desired task:

Which of the following changes can the data scientist make to accomplish the task?

（A）Replace the GridSearchCV operation with RandomizedSearchCV

（B）Replace the random_state=0 argument with random_state=1

（C）Replace the GridSearchCV operation with cross_validate

（D）Replace the penalty= ['12', '11'] argument with penalty=uniform ('12', '11')

（E）Replace the GridSearchCV operation with ParameterGrid

正解：A 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 2

A data scientist wants to parallelize the training of trees in a gradient boosted tree to speed up the training process. A colleague suggests that parallelizing a boosted tree algorithm can be difficult.
Which of the following describes why?

（A）Gradient boosting requires access to all data at once which cannot happen during parallelization.

（B）Gradient boosting is an iterative algorithm that requires information from the previous iteration to perform the next step.

（C）Gradient boosting calculates gradients in evaluation metrics using all cores which prevents parallelization.

（D）Gradient boosting is not a linear algebra-based algorithm which is required for parallelization

正解：B 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 3

Which of the following evaluation metrics is not suitable to evaluate runs in AutoML experiments for regression problems?

（A）R-squared

（B）F1

（C）MAE

（D）MSE

正解：B 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 4

A data scientist is developing a single-node machine learning model. They have a large number of model configurations to test as a part of their experiment. As a result, the model tuning process takes too long to complete. Which of the following approaches can be used to speed up the model tuning process?

（A）Enable autoscaling clusters

（B）Implement MLflow Experiment Tracking

（C）Parallelize with Hyperopt

（D）Scale up with Spark ML

正解：C 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 5

A data scientist has created a linear regression model that uses log(price) as a label variable. Using this model, they have performed inference and the predictions and actual label values are in Spark DataFrame preds_df.
They are using the following code block to evaluate the model:
regression_evaluator.setMetricName("rmse").evaluate(preds_df)
Which of the following changes should the data scientist make to evaluate the RMSE in a way that is comparable with price?

（A）They should exponentiate the computed RMSE value

（B）They should evaluate the MSE of the log predictions to compute the RMSE

（C）They should exponentiate the predictions before computing the RMSE

（D）They should take the log of the predictions before computing the RMSE

正解：C 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 6

Which of the following describes the relationship between native Spark DataFrames and pandas API on Spark DataFrames?

（A）pandas API on Spark DataFrames are made up of Spark DataFrames and additional metadata

（B）pandas API on Spark DataFrames are less mutable versions of Spark DataFrames

（C）pandas API on Spark DataFrames are single-node versions of Spark DataFrames with additional metadata

（D）pandas API on Spark DataFrames are more performant than Spark DataFrames

正解：A 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 7

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 model will take longer to train for each unique combination of hvperparameter values

（B）The cross-validation process will no longer be

（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 reproducible

正解：C 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 8

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）It does not fit the imputer on the data to create an ImputerModel.

（B）The inputCols and outputCols need to be exactly the same.

（C）It does not impute both the training and test data sets.

（D）The fit method needs to be called instead of transform.

正解：A 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

Databricks-Machine-Learning-Associate 無料問題集「Databricks Certified Machine Learning Associate」

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