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NEW QUESTION: 1
An ISP is peering with a new provider and wishes to disclose which autonomous system numbers should be allowed through BGP for network transport. Which of the following should contain this information?
A. Operating Level Agreement
B. Interconnection Security Agreement
C. Memorandum of Understanding
D. Service Level Agreement
Answer: B
NEW QUESTION: 2
You are logged in to the Junos Pulse Secure Access Service on your laptop using the wired connection on your docking station. You undock your machine and move over to a wireless network and your session does not reconnect.
Why did this happen?
A. The enable session extension was disabled.
B. The browser request follow through was disabled.
C. The persistent session was disabled.
D. The roaming session was disabled.
Answer: D
NEW QUESTION: 3
Which of the following is MOST important for an IS auditor to confirm when reviewing the effectiveness of an incident response program?
A. Lessons learned are incorporated into incident response processes
B. Incidents, are escalated to server management in a timely manner.
C. Incidents are categorized according to industry standards
D. The plan is reviewed and undated annually.
Answer: A
NEW QUESTION: 4
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. mapreduce.map.memoryパラメーターの値を増やします
B. hive.tez.container.sizeパラメーターの値を増やします
C. 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
D. ワーカーノードの数を増やす
E. 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
Answer: C,E
Explanation:
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