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NEW QUESTION: 1
会社は、20のPb HDFSデータベースで大きなオンプレイスApache Hadoopクラスタを持ちます。クラスタは約200例と1 pbで4分の1に成長している。同社の目標は、Hadoopデータの弾力性を有効にし、クラスタノードを失うことの影響を制限し、コストを大幅に削減することである。現在のクラスタ
24 / 7と対話的クエリやバッチ処理を含むさまざまな分析ワークロードをサポートします。
どちらの解決策は、これらの要件を満たしています。
A. Use AWS Snowmobile to migrate the existing cluster data to Amazon S3. Create a persistent Amazon EMR cluster initially sized to handle the interactive workload based on historical data from the on-premises cluster. Store the data on EMRFS. Minimize costs using Reserved Instances for master and core nodes and Spot Instances for task nodes, and auto scale task nodes based on Amazon CloudWatch metrics. Create job-specific, optimized clusters for batch workloads that are similarly optimized.
B. Use AWS Direct Connect to migrate the existing cluster data to Amazon S3. Create a persistent Amazon EMR cluster initially sized to handle the interactive workload based on historical data from the on-premises cluster. Store the data on EMRFS. Minimize costs using Reserved Instances for master and core nodes and Spot Instances for task nodes, and auto scale task nodes based on Amazon CloudWatch metrics. Create job-specific, optimized clusters for batch workloads that are similarly optimized.
C. Use AWS Snowball to migrate the existing cluster data to Amazon S3. Create a persistent Amazon EMR cluster initially sized to handle the interactive workloads based on historical data from the on-premises cluster. Store the on EMRFS. Minimize costs using Reserved Instances for master and core nodes and Spot Instances for task nodes, and auto scale task nodes based on Amazon CloudWatch metrics. Create job-specific, optimized clusters for batch workloads that are similarly optimized.
D. Use AWS Snowmobile to migrate the existing cluster data to Amazon S3. Create a persistent Amazon EMR cluster of similar size and configuration to the current cluster. Store the data on EMRFS.
Minimize costs by using Reserved Instances. As the workload grows each quarter, purchase additional Reserved Instances and add to the cluster.
Answer: A
Explanation:
Q: How should I choose between Snowmobile and Snowball?
To migrate large datasets of 10PB or more in a single location, you should use Snowmobile. For datasets less than 10PB or distributed in multiple locations, you should use Snowball. In addition, you should evaluate the amount of available bandwidth in your network backbone. If you have a high speed backbone with hundreds of Gb/s of spare throughput, then you can use Snowmobile to migrate the large datasets all at once. If you have limited bandwidth on your backbone, you should consider using multiple Snowballs to migrate the data incrementally.
NEW QUESTION: 2
You are working as a technical intern for Tailspin Toys. A product developer plans to attend a convention in France and wants to bring a company-owned Windows 7 Ultimate laptop. Your manager is concerned about the security of the information contained on the laptop and asks you to turn on Bitlocker.
You receive the error message shown in the following image:
Use the drop-down menus to select the answer choice that completes each statement. Each correct selection is worth one point
Answer:
Explanation:
Explanation
You check [answer choice] on the system to see if a TMP was disabled.
System Bios
If it is determined that the system does not have a TPM, you should use [answer choice] to allow you to enable BitLocker on the computer.
Local Security Policy
NEW QUESTION: 3
Each day, company plans to store hundreds of files in Azure Blob Storage and Azure Data Lake Storage. The company uses the parquet format.
You must develop a pipeline that meets the following requirements:
* Process data every six hours
* Offer interactive data analysis capabilities
* Offer the ability to process data using solid-state drive (SSD) caching
* Use Directed Acyclic Graph(DAG) processing mechanisms
* Provide support for REST API calls to monitor processes
* Provide native support for Python
* Integrate with Microsoft Power BI
You need to select the appropriate data technology to implement the pipeline.
Which data technology should you implement?
A. HDInsight Spark cluster
B. Azure Stream Analytics
C. HDInsight Apache Storm cluster
D. HDInsight Apache Hadoop cluster using MapReduce
E. Azure SQL Data Warehouse
Answer: C
Explanation:
Explanation
Storm runs topologies instead of the Apache Hadoop MapReduce jobs that you might be familiar with. Storm topologies are composed of multiple components that are arranged in a directed acyclic graph (DAG). Data flows between the components in the graph. Each component consumes one or more data streams, and can optionally emit one or more streams.
Python can be used to develop Storm components.
References:
https://docs.microsoft.com/en-us/azure/hdinsight/storm/apache-storm-overview
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