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NEW QUESTION: 1
A user has launched an EC2 instance from an instance store backed AMI. If the user
restarts the instance, what will happen to the ephermal storage data?
A. It is not possible to restart an instance launched from an instance store backed AMI
B. The data is preserved
C. All data will be erased and the ephermal storage is released
D. All the data will be erased but the ephermal storage will stay connected
Answer: B
Explanation:
A user can reboot an EC2 instance using the AWS console, the Amazon EC2 CLI or the Amazon EC2 API. Rebooting an instance is equivalent to rebooting an operating system. However, it is recommended that the user use Amazon EC2 to reboot the instance instead of running the operating system reboot command from the instance. When an instance launched from an instance store backed AMI is rebooted all the ephermal storage data is still preserved.

NEW QUESTION: 2
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: 3

A. sn.exe
B. tlbImp.exe
C. gacutil.exe
D. regasm.exe
Answer: A
Explanation:
Strong Name Scenario
The following scenario outlines the process of signing an assembly with a strong name and later referencing it by that name.
Assembly A is created with a strong name using one of the following methods:
References: https://docs.microsoft.com/en-us/dotnet/framework/app-domains/create-and-use-strong- named-assemblies