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NEW QUESTION: 1
企業は、アプリケーションロードバランサーを使用して3つのAWSリージョンにアプリケーションをデプロイしています。AmazonRoute 53は、これらのリージョン間でトラフィックを分散するために使用されます。
ソリューションアーキテクトが最も高性能なエクスペリエンスを提供するには、どのRoute 53構成を使用する必要がありますか？
A. 地理的近接性ポリシーを使用してCNAMEレコードを作成します。
B. 地理位置情報ポリシーを使用してAレコードを作成します。
C. レイテンシポリシーを使用してAレコードを作成します。
D. フェイルオーバーポリシーを使用してCNAMEレコードを作成します。
Answer: C
NEW QUESTION: 2
Azureサービスレベル契約（SLA）で保証されているものは何ですか？
A. 機能の可用性
B. パフォーマンス
C. アップタイム
D. 帯域幅
Answer: A
Explanation:
Explanation
References:
https://azure.microsoft.com/en-us/support/legal/sla/summary/
NEW QUESTION: 3
You've migrated a Hadoop job from an on-prem cluster to dataproc and GCS. Your Spark job is a complicated analytical workload that consists of many shuffing operations and initial data are parquet files (on average 200-400 MB size each). You see some degradation in performance after the migration to Dataproc, so you'd like to optimize for it. You need to keep in mind that your organization is very cost- sensitive, so you'd like to continue using Dataproc on preemptibles (with 2 non-preemptible workers only) for this workload.
What should you do?
A. Switch from HDDs to SSDs, copy initial data from GCS to HDFS, run the Spark job and copy results back to GCS.
B. Switch from HDDs to SSDs, override the preemptible VMs configuration to increase the boot disk size.
C. Switch to TFRecords formats (appr. 200MB per file) instead of parquet files.
D. Increase the size of your parquet files to ensure them to be 1 GB minimum.
Answer: B
Explanation:
In order to increase performance switch to SSD which will be costly, so to tackle this increase the boot disk size, bootsize is worker node cache size 100 Gb.
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