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NEW QUESTION: 1
SysOps管理者は、クラスターでAmazon Linuxを実行しているノードが高いメモリ使用率によって制約されている場合、Auto Scalingグループがスケーリングに失敗したというレポートを受け取ります。
高いメモリ使用率によりよく適応するために、スケーリングを有効にするために管理者は何をすべきですか？
A. Amazon CloudWatchメモリモニタリングスクリプトをインストールし、スクリプトの結果に基づいてカスタムメトリックを作成します
B. Application Load Balancerをデプロイして、ノード間でトラフィックをより均等に分散します
C. クラスターの最小サイズを増やして、メモリとアプリケーションの負荷要求を満たします
D. メモリ使用率をAmazon S3にパイプするカスタムスクリプトを作成し、AWS Lambdaを使用したイベントでスケーリングする
Answer: A
NEW QUESTION: 2
A. Option B
B. Option C
C. Option D
D. Option A
Answer: D
NEW QUESTION: 3
You need to implement an Azure Databricks cluster that automatically connects to Azure Data Lake Storage Gen2 by using Azure Active Directory (Azure AD) integration.
How should you configure the new cluster? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: High Concurrency
Enable Azure Data Lake Storage credential passthrough for a high-concurrency cluster.
Incorrect:
Support for Azure Data Lake Storage credential passthrough on standard clusters is in Public Preview.
Standard clusters with credential passthrough are supported on Databricks Runtime 5.5 and above and are limited to a single user.
Box 2: Azure Data Lake Storage Gen1 Credential Passthrough
You can authenticate automatically to Azure Data Lake Storage Gen1 and Azure Data Lake Storage Gen2 from Azure Databricks clusters using the same Azure Active Directory (Azure AD) identity that you use to log into Azure Databricks. When you enable your cluster for Azure Data Lake Storage credential passthrough, commands that you run on that cluster can read and write data in Azure Data Lake Storage without requiring you to configure service principal credentials for access to storage.
References:
https://docs.azuredatabricks.net/spark/latest/data-sources/azure/adls-passthrough.html
