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NEW QUESTION: 1
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
NEW QUESTION: 2
Ein Kunde muss mehrere virtuelle Maschinen auf seiner Design-Workstation ausführen, um mit plattformübergreifender Software arbeiten zu können. Welche der folgenden Optionen sollte maximiert werden, um die Leistung der Workstation zu verbessern? (Wählen Sie ZWEI)
A. RAM
B. Speicherplatz
C. Anzahl der CPU-Kerne
D. Lüfter
E. PCIe-Steckplätze
Answer: A,C
NEW QUESTION: 3
An ELB is diverting traffic across 5 instances. One of the instances was unhealthy only for 20 minutes.
What will happen after 20 minutes when the instance becomes healthy?
A. ELB terminates the instance once it is unhealthy. Thus, the instance cannot be healthy after 10
minutes
B. ELB will never divert traffic back to the same instance
C. ELB will not automatically send traffic to the same instance. However, the user can configure to start
sending traffic to the same instance
D. ELB starts sending traffic to the instance once it is healthy
Answer: D
Explanation:
AWS Elastic Load Balancing continuously checks the health of an instance. If one of the instances is
unhealthy it stops sending traffic to it and automatically reroutes the traffic to the remaining running EC2
instances. If the failed EC2 instance is restored, Elastic Load Balancing will again start sending traffic to
that instance.
Reference: http://docs.aws.amazon.com/ElasticLoadBalancing/latest/DeveloperGuide/SvcIntro.html