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NEW QUESTION: 1
必要なOSアクセスのみをソフトウェアアプリケーションに公開するために使用できる展開の概念は次のうちどれですか？
A. ステージング環境
B. サンドボックス
C. 安全なベースライン
D. トラステッドOS
Answer: B
NEW QUESTION: 2
How does the Management Console perform commands on the ProxySG?
A. None of the above.
B. By using Content Policy Language to control the ProxySG.
C. By using proprietary APIs to perform function calls on the ProxySG.
D. By generating the appropriate CLI commands and executing them.
Answer: D
NEW QUESTION: 3
ソリューションアーキテクトは、Amazon Kinesis Streamsからのレコードを処理するマイクロサービスを設計しています。メタデータはAmazon DynamoDBに保存する必要があります。マイクロサービスは、Kinesisストリームで到着するレコードを毎日10,000件同時に処理できる必要があります。
マイクロサービスを設計するMOSTスケーラブルな方法は次のとおりです。
A. EC2インスタンスのDockerコンテナーとして。
B. AWS Lambda関数として。
C. Amazon EC2インスタンスのプロセスとして。
D. Amazon ECSで実行されるDockerコンテナーとして。
Answer: D
Explanation:
Explanation
Lambda has concurrent limit of 3000 but the question says 10000 for which Docker with Amazon ECS is capable of https://docs.aws.amazon.com/lambda/latest/dg/scaling.html
NEW QUESTION: 4
You are developing a solution using a Lambda architecture on Microsoft Azure.
The data at test layer must meet the following requirements:
Data storage:
*Serve as a repository (or high volumes of large files in various formats.
*Implement optimized storage for big data analytics workloads.
*Ensure that data can be organized using a hierarchical structure.
Batch processing:
*Use a managed solution for in-memory computation processing.
*Natively support Scala, Python, and R programming languages.
*Provide the ability to resize and terminate the cluster automatically.
Analytical data store:
*Support parallel processing.
*Use columnar storage.
*Support SQL-based languages.
You need to identify the correct technologies to build the Lambda architecture.
Which technologies should you use? To answer, select the appropriate options in the answer area NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Data storage: Azure Data Lake Store
A key mechanism that allows Azure Data Lake Storage Gen2 to provide file system performance at object storage scale and prices is the addition of a hierarchical namespace. This allows the collection of objects/files within an account to be organized into a hierarchy of directories and nested subdirectories in the same way that the file system on your computer is organized. With the hierarchical namespace enabled, a storage account becomes capable of providing the scalability and cost-effectiveness of object storage, with file system semantics that are familiar to analytics engines and frameworks.
Batch processing: HD Insight Spark
Aparch Spark is an open-source, parallel-processing framework that supports in-memory processing to boost the performance of big-data analysis applications.
HDInsight is a managed Hadoop service. Use it deploy and manage Hadoop clusters in Azure. For batch processing, you can use Spark, Hive, Hive LLAP, MapReduce.
Languages: R, Python, Java, Scala, SQL
Analytic data store: SQL Data Warehouse
SQL Data Warehouse is a cloud-based Enterprise Data Warehouse (EDW) that uses Massively Parallel Processing (MPP).
SQL Data Warehouse stores data into relational tables with columnar storage.
References:
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
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