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ハイパスレートのDatabricks-Machine-Learning-Associate日本語版参考資料 &合格スムーズDatabricks-Machine-Learning-Associate問題集 |素敵なDatabricks-Machine-Learning-Associate問題と解答Databricks Certified Machine Learning Associate Exam

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Databricks Certified Machine Learning Associate Exam 認定 Databricks-Machine-Learning-Associate 試験問題 (Q47-Q52):

質問 # 47
A data scientist uses 3-fold cross-validation when optimizing model hyperparameters for a regression problem. The following root-mean-squared-error values are calculated on each of the validation folds:
* 10.0
* 12.0
* 17.0
Which of the following values represents the overall cross-validation root-mean-squared error?

A. 17.0
B. 13.0
C. 39.0
D. 12.0
E. 10.0

正解：B

解説：
To calculate the overall cross-validation root-mean-squared error (RMSE), you average the RMSE values obtained from each validation fold. Given the RMSE values of 10.0, 12.0, and 17.0 for the three folds, the overall cross-validation RMSE is calculated as the average of these three values:
Overall CV RMSE=10.0+12.0+17.03=39.03=13.0Overall CV RMSE=310.0+12.0+17.0=339.0=13.0 Thus, the correct answer is 13.0, which accurately represents the average RMSE across all folds.
Reference:
Cross-validation in Regression (Understanding Cross-Validation Metrics).

質問 # 48
What is the name of the method that transforms categorical features into a series of binary indicator feature variables?

A. String indexing
B. Categorical
C. One-hot encoding
D. Leave-one-out encoding
E. Target encoding

正解：C

解説：
The method that transforms categorical features into a series of binary indicator variables is known as one-hot encoding. This technique converts each categorical value into a new binary column, which is essential for models that require numerical input. One-hot encoding is widely used because it helps to handle categorical data without introducing a false ordinal relationship among categories.
Reference:
Feature Engineering Techniques (One-Hot Encoding).

質問 # 49
A machine learning engineering team has a Job with three successive tasks. Each task runs a single notebook. The team has been alerted that the Job has failed in its latest run.
Which of the following approaches can the team use to identify which task is the cause of the failure?

A. Review the matrix view in the Job's runs
B. Run each notebook interactively
C. Migrate the Job to a Delta Live Tables pipeline
D. Change each Task's setting to use a dedicated cluster

正解：A

解説：
To identify which task is causing the failure in the job, the team should review the matrix view in the Job's runs. The matrix view provides a clear and detailed overview of each task's status, allowing the team to quickly identify which task failed. This approach is more efficient than running each notebook interactively, as it provides immediate insights into the job's execution flow and any issues that occurred during the run.
Reference:
Databricks documentation on Jobs: Jobs in Databricks

質問 # 50
A data scientist is using MLflow to track their machine learning experiment. As a part of each of their MLflow runs, they are performing hyperparameter tuning. The data scientist would like to have one parent run for the tuning process with a child run for each unique combination of hyperparameter values. All parent and child runs are being manually started with mlflow.start_run.
Which of the following approaches can the data scientist use to accomplish this MLflow run organization?

A. They can start each child run with the same experiment ID as the parent run
B. They can turn on Databricks Autologging
C. They can start each child run inside the parent run's indented code block using mlflow.start runO
D. They can specify nested=True when starting the child run for each unique combination of hyperparameter values
E. They can specify nested=True when starting the parent run for the tuning process

正解：D

解説：
To organize MLflow runs with one parent run for the tuning process and a child run for each unique combination of hyperparameter values, the data scientist can specify nested=True when starting the child run. This approach ensures that each child run is properly nested under the parent run, maintaining a clear hierarchical structure for the experiment. This nesting helps in tracking and comparing different hyperparameter combinations within the same tuning process.
Reference:
MLflow Documentation (Managing Nested Runs).

質問 # 51
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. Singular value decomposition
B. Iterative optimization
C. Logistic regression
D. Least-squares method

正解：B

解説：
For large datasets, Spark ML uses iterative optimization methods to distribute the training of a linear regression model. Specifically, Spark MLlib employs techniques like Stochastic Gradient Descent (SGD) and Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) optimization to iteratively update the model parameters. These methods are well-suited for distributed computing environments because they can handle large-scale data efficiently by processing mini-batches of data and updating the model incrementally.
Reference:
Databricks documentation on linear regression: Linear Regression in Spark ML

質問 # 52
......

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更新する-ハイパスレートのDatabricks-Machine-Learning-Associate日本語版参考資料試験-試験の準備方法Databricks-Machine-Learning-Associate問題集

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