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NEW QUESTION: 1
HDInsightクラスターで毎日のWebトラフィックログの分析を実行するプロセスを管理します。 250のWebサーバーのそれぞれが、毎日約ギガバイト（GB）のログデータを生成します。すべてのログデータは、Microsoft Azure Data Lake Storage Gen 2の単一のフォルダーに保存されます。
プロセスのパフォーマンスを改善する必要があります。
どちらの変更を加える必要がありますか？それぞれの正解は完全な解決策を提示します。
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。
A. ワーカーノードの数を増やす
B. すべてのサーバーの毎日のログファイルを1つのファイルに結合します
C. mapreduce.map.memoryパラメーターの値を増やします
D. 毎日のログが独自のフォルダーにあるように、ログファイルをフォルダーに移動します
E. hive.tez.container.sizeパラメーターの値を増やします
Answer: B,D
Explanation:
A: Typically, analytics engines such as HDInsight and Azure Data Lake Analytics have a per-file overhead. If you store your data as many small files, this can negatively affect performance. In general, organize your data into larger sized files for better performance (256MB to 100GB in size). Some engines and applications might have trouble efficiently processing files that are greater than 100GB in size.
C: For Hive workloads, partition pruning of time-series data can help some queries read only a subset of the data which improves performance.
Those pipelines that ingest time-series data, often place their files with a very structured naming for files and folders. Below is a very common example we see for data that is structured by date:
\DataSet\YYYY\MM\DD\datafile_YYYY_MM_DD.tsv
Notice that the datetime information appears both as folders and in the filename.
References:
https://docs.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-performance-tuning-guidance
NEW QUESTION: 2
A. Mirage Web Management Server
B. Mirage Management Server
C. SQL 2008 Express R2 Server
D. MongoDB File Database Server
Answer: C
NEW QUESTION: 3
A. Option E
B. Option B
C. Option A
D. Option C
E. Option D
Answer: B,C