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NEW QUESTION: 1
A Business Process (BP) has been identified that failed due to an issue with the Lightweight JDBC adapter using a database that is unavailable. The database has been made available again. The BP has no built in OnFault logic. To reprocess the failed BP, which action to be executed is the most efficient for each of the failed instances?
A. Advanced Restart
B. Resume
C. Restart
D. Terminate and Resubmit
Answer: B

NEW QUESTION: 2
テスト要件に従ってデータを分割する方法を特定する必要があります。
どのプロパティを選択する必要がありますか？回答するには、回答エリアで適切なオプションを選択します。
注：それぞれの正しい選択には1ポイントの価値があります。

Answer:
Explanation:

Explanation

Scenario: Testing
You must produce multiple partitions of a dataset based on sampling using the Partition and Sample module in Azure Machine Learning Studio.
Box 1: Assign to folds
Use Assign to folds option when you want to divide the dataset into subsets of the data. This option is also useful when you want to create a custom number of folds for cross-validation, or to split rows into several groups.
Not Head: Use Head mode to get only the first n rows. This option is useful if you want to test a pipeline on a small number of rows, and don't need the data to be balanced or sampled in any way.
Not Sampling: The Sampling option supports simple random sampling or stratified random sampling. This is useful if you want to create a smaller representative sample dataset for testing.
Box 2: Partition evenly
Specify the partitioner method: Indicate how you want data to be apportioned to each partition, using these options:
Partition evenly: Use this option to place an equal number of rows in each partition. To specify the number of output partitions, type a whole number in the Specify number of folds to split evenly into text box.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/partition-and-sample