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NEW QUESTION: 1
Refer to the Exhibit.
IBM 000-605 Exam
An integration developer needs to get the 'output' value of the operation 'invoke' from within the 'getOutputCustom' primitive of the response flow shown in the exhibit. Which of the following code snippets must be used?
A. String output = smo.getDataObject("invoke").getString("output");
B. String output = smo.getDataObject("invokeResponse").getString("output");
C. String output = smo.getDataObject("body").getDataObject("invoke").getString("output");
D. String output = smo.getDataObject("body").getDataObject("invokeResponse").getString("output");String output = smo.getDataObject("body").getDataObject("invokeResponse").getString("output");
Answer: D
NEW QUESTION: 2
Based on the information in the high-level design extract from
question 2
, what storage protocol is unsuitable?
A. FC
B. iSCSI
C. NFS
Answer: C
Explanation:
The requirements state that the application uptime requirements would not be met if using vSphere HA alone. vSphere HA would invoke a restart of guest virtual machines after at least 10 seconds. VMware fault tolerance would meet the uptime requirements; a failover would result in zero downtime of theapplication. This technology can be used only with VMFS; therefore, NFS cannot be used in this design. Chapter 4, NetWork - Logical and Physical Design to allow applications to flow
NEW QUESTION: 3
IKEv2デフォルトプロポーザルに含まれるトランスフォームセットの2つの例はどれですか？ （2つ選択してください。）
A. 3des, sha1, 1
B. aes-cbc-192, sha256, 14
C. aes-cbc-128, sha, 5
D. 3des, md5, 5
Answer: C,D
NEW QUESTION: 4
Microsoft AzureでLambdaアーキテクチャを使用してソリューションを開発しています。
テストレイヤーのデータは、次の要件を満たしている必要があります。
データストレージ：
*リポジトリ（またはさまざまな形式の大量の大容量ファイル）として機能します。
*ビッグデータ分析ワークロード用に最適化されたストレージを実装します。
*階層構造を使用してデータを整理できるようにします。
バッチ処理：
*メモリ内の計算処理にはマネージドソリューションを使用します。
* Scala、Python、およびRプログラミング言語をネイティブにサポートします。
*クラスターを自動的にサイズ変更および終了する機能を提供します。
Analytical data store:
* Support parallel processing.
* Use columnar storage.
* Support SQL-based languages.
Lambdaアーキテクチャを構築するには、正しいテクノロジーを特定する必要があります。
どのテクノロジーを使用する必要がありますか？回答するには、回答エリアで適切なオプションを選択します
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。
Answer:
Explanation:
Explanation:
データストレージ：Azure Data Lake Store
Azure Data Lake Storage Gen2がオブジェクトストレージの規模と価格でファイルシステムのパフォーマンスを提供できるようにする重要なメカニズムは、階層的な名前空間の追加です。これにより、アカウント内のオブジェクト/ファイルのコレクションを、コンピューターのファイルシステムを整理するのと同じ方法で、ディレクトリとネストされたサブディレクトリの階層に整理できます。階層型ネームスペースを有効にすると、ストレージアカウントは、分析エンジンとフレームワークに馴染みのあるファイルシステムセマンティクスを使用して、オブジェクトストレージのスケーラビリティと費用対効果を提供できるようになります。
バッチ処理：HD Insight Spark
Aparch Sparkは、ビッグデータ分析アプリケーションのパフォーマンスを向上させるインメモリ処理をサポートするオープンソースの並列処理フレームワークです。
HDInsightは、マネージドHadoopサービスです。 AzureでHadoopクラスターを展開および管理します。バッチ処理には、Spark、Hive、Hive LLAP、MapReduceを使用できます。
言語：R、Python、Java、Scala、SQL
分析データストア：SQL Data Warehouse
SQL Data Warehouseは、超並列処理（MPP）を使用するクラウドベースのエンタープライズデータウェアハウス（EDW）です。
SQL Data Warehouseは、列状ストレージを使用してデータをリレーショナルテーブルに格納します。
参照：
https://docs.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-namespace
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/batch-processing
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-overview-what-is