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NEW QUESTION: 1
ある会社が、クレジットカードによる支払いを処理するために3層のWebアプリケーションを実行しています。フロントエンドのユーザーインターフェイスは、静的なWebページで構成されています。アプリケーション層は長時間実行されるプロセスを持つことができますデータベース層はMySQLを使用します。
アプリケーションは現在、単一の汎用の大規模なAmazon EC2インスタンスで実行されています。ソリューションアーキテクトは、サービスを分離してWebアプリケーションの高可用性を実現する必要があります。
どのソリューションが最高の可用性を提供しますか？
A. 静的アセットをAmazonCloudFrontに移動しますアプリケーションをEC2のAutoScalingグループのままにします。データベースをAmazonRDSに移動して、Multi-AZをデプロイします。
B. 静的アセットとアプリケーションをミディアムEC2インスタンスに移動します。データベースをラージインスタンスに残します。両方のインスタンスをAutoScalingグループに配置します。
C. 静的アセットをAmazonS3に移動します。同時実行制限を設定して、アプリケーションをAWSLambdaに移動します。
オンデマンドを有効にして、データベースをAmazonDynamoDBに移動します。
D. 静的アセットをAmazonS3に移動します。 AutoScalingが有効になっているAmazonElastic Container Service（Amazon ECS）コンテナーにアプリケーションを移動します。データベースをAmazonRDSに移動して、Multi-AZをデプロイします
Answer: D

NEW QUESTION: 2
You are designing a data processing solution that will run as a Spark job on an HDInsight cluster. The solution will be used to provide near real-time information about online ordering for a retailer.
The solution must include a page on the company intranet that displays summary information.
The summary information page must meet the following requirements:
* Display a summary of sales to date grouped by product categories, price range, and review scope.
* Display sales summary information including total sales, sales as compared to one day ago and sales as compared to one year ago.
* Reflect information for new orders as quickly as possible.
You need to recommend a design for the solution.
What should you recommend? To answer, select the appropriate configuration in the answer area.

Answer:
Explanation:

Explanation:
Box 1: DataFrame
DataFrames
Best choice in most situations.
Provides query optimization through Catalyst.
Whole-stage code generation.
Direct memory access.
Low garbage collection (GC) overhead.
Not as developer-friendly as DataSets, as there are no compile-time checks or domain object programming.
Box 2: parquet
The best format for performance is parquet with snappy compression, which is the default in Spark 2.x. Parquet stores data in columnar format, and is highly optimized in Spark.
Incorrect Answers:
DataSets
Good in complex ETL pipelines where the performance impact is acceptable.
Not good in aggregations where the performance impact can be considerable.
RDDs
You do not need to use RDDs, unless you need to build a new custom RDD.
No query optimization through Catalyst.
No whole-stage code generation.
High GC overhead.
References:
https://docs.microsoft.com/en-us/azure/hdinsight/spark/apache-spark-perf

NEW QUESTION: 3

/var/log/hostd.log
/var/log/vmkernel.log
/var/log/vpxa.log

A. Option C
B. Option B
C. Option A
D. Option D
Answer: B,C