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NEW QUESTION: 1
会社のデータエンジニアリングソリューションを開発します。
プロジェクトには、メモリ内のバッチデータ処理ソリューションが必要です。
Microsoft Azureでデータをバッチ処理するためにHDInsightクラスターをプロビジョニングする必要があります。
PowerShellセグメントをどのように完成させ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:
Box 1: New-AzStorageContainer
# Example: Create a blob container. This holds the default data store for the cluster.
New-AzStorageContainer `
-Name $clusterName `
-Context $defaultStorageContext
$sparkConfig = New-Object "System.Collections.Generic.Dictionary``2[System.String,System.String]"
$sparkConfig.Add("spark", "2.3")
ボックス2：スパーク
Sparkは、インメモリクラスターコンピューティングのプリミティブを提供します。 Sparkジョブは、データをメモリにロードしてキャッシュし、繰り返しクエリを実行できます。メモリ内コンピューティングは、Hadoop分散ファイルシステム（HDFS）を介してデータを共有するHadoopなどのディスクベースのアプリケーションよりも、ディスクベースのアプリケーションよりもはるかに高速です。
ボックス3：New-AzureRMHDInsightCluster
# Create the HDInsight cluster. Example:
New-AzHDInsightCluster `
-ResourceGroupName $resourceGroupName `
-ClusterName $clusterName `
-Location $location `
-ClusterSizeInNodes $clusterSizeInNodes `
-ClusterType $"Spark" `
-OSType "Linux" `
Box 4: Spark
HDInsightは、マネージドHadoopサービスです。 AzureでHadoopクラスターを展開および管理します。バッチ処理には、Spark、Hive、Hive LLAP、MapReduceを使用できます。
参照：
https://docs.microsoft.com/bs-latn-ba/azure/hdinsight/spark/apache-spark-jupyter-spark-sql-use-powershell
https://docs.microsoft.com/bs-latn-ba/azure/hdinsight/spark/apache-spark-overview
NEW QUESTION: 2
You plan to use the Hyperdrive feature of Azure Machine Learning to determine the optimal hyperparameter values when training a model.
You must use Hyperdrive to try combinations of the following hyperparameter values. You must not apply an early termination policy.
learning_rate: any value between 0.001 and 0.1
* batch_size: 16, 32, or 64
You need to configure the sampling method for the Hyperdrive experiment Which two sampling methods can you use? Each correct answer is a complete solution.
NOTE: Each correct selection is worth one point.
A. Random sampling
B. Bayesian sampling
C. No sampling
D. Grid sampling
Answer: A,B
Explanation:
Explanation
C: Bayesian sampling is based on the Bayesian optimization algorithm and makes intelligent choices on the hyperparameter values to sample next. It picks the sample based on how the previous samples performed, such that the new sample improves the reported primary metric.
Bayesian sampling does not support any early termination policy
Example:
from azureml.train.hyperdrive import BayesianParameterSampling
from azureml.train.hyperdrive import uniform, choice
param_sampling = BayesianParameterSampling( {
"learning_rate": uniform(0.05, 0.1),
"batch_size": choice(16, 32, 64, 128)
}
)
D: In random sampling, hyperparameter values are randomly selected from the defined search space. Random sampling allows the search space to include both discrete and continuous hyperparameters.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters
NEW QUESTION: 3
Which is an advantage of stranded conductors over solid conductors?
A. More flexible
B. Simpler terminations
C. Better high frequency performance
D. Less costly
Answer: A
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