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NEW QUESTION: 1
Azure loT Edgeデバイスからデータを取り込み、Azure Machine Learningでデータを前処理し、その後の処理のためにデータをAzure HDInsightに移動するソリューションを設計しています。
ソリューションに何を含めるべきですか？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:
ボックス1：データのエクスポート
Azure Machine Learning Studioのデータのエクスポートモジュールの[データをHiveにエクスポート]オプション。このオプションは、非常に大きなデータセットを使用しており、機械学習実験データをHadoopクラスターまたはHDInsight分散ストレージに保存する場合に役立ちます。
ボックス2：Apache Hive
Apache Hiveは、Apache Hadoop用のデータウェアハウスシステムです。 Hiveは、データの要約、クエリ、およびデータの分析を可能にします。 Hiveクエリは、SQLに似たクエリ言語であるHiveQLで記述されています。
ボックス3：Azure Data Lake
HDInsightクラスターのHDFSファイルシステムの既定のストレージは、AzureストレージアカウントまたはAzure Data Lake Storageに関連付けることができます。
参照：
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/export-to-hive-query
https://docs.microsoft.com/en-us/azure/hdinsight/hadoop/hdinsight-use-hive

NEW QUESTION: 2

A. Executor
B. ExecutorService
C. Runnable
D. Future
E. Thread
F. Callable
Answer: A,B,F
Explanation:
The java.util.concurrent package defines three executor interfaces:
*(F) Executor, a simple interface that supports launching new tasks.
*(A) ExecutorService, a subinterface of Executor, which adds features that help manage the lifecycle, both of the individual tasks and of the executor itself.
*ScheduledExecutorService, a subinterface of ExecutorService, supports future and/or periodic execution of tasks.
Typically, variables that refer to executor objects are declared as one of these three interface types, not with an executor class type.
D: The ExecutorService interface supplements execute with a similar, but more versatile submit method. Like execute, submit accepts Runnable objects, but also accepts Callable objects, which allow the task to return a value.
Reference: The Java Tutorials, Executor Interfaces

NEW QUESTION: 3
Liquidity of a company is based on its ability to
A. meet short term commitments.
B. buy back shares.
C. meet long term commitments.
D. pay dividends to shareholders.
Answer: A