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NEW QUESTION: 1
ソリューションアーキテクトは、Amazon S3に大量のイベントデータを保存するソリューションを設計しています。アーキテクトは、ワークロードが常に毎秒100リクエストを超えると予想しています。
パフォーマンスを最適化するために、アーキテクトはAmazon S3で何をすべきですか？
A. キー名のプレフィックスをランダム化します。
B. イベントデータを個別のバケットに保存します。
C. Amazon S3 Transfer Accelerationを使用します。
D. キー名のサフィックスをランダム化します。
Answer: A
Explanation:
Explanation
https://docs.aws.amazon.com/AmazonS3/latest/dev/request-rate-perf-considerations.html

NEW QUESTION: 2
You have an Azure subscription that contains two virtual networks named VNet1 and VNet2. Virtual machines connect to the virtual networks.
The virtual networks have the address spaces and the subnets configured as shown in the following table.

You need to add the address space of 10.33.0.0/16 to VNet1. The solution must ensure that the hosts on VNet1 and VNet2 can communicate.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:
Explanation:

Explanation:
Step 1: Remove peering between Vnet1 and VNet2.
You can't add address ranges to, or delete address ranges from a virtual network's address space once a virtual network is peered with another virtual network. To add or remove address ranges, delete the peering, add or remove the address ranges, then re-create the peering.
Step 2: Add the 10.44.0.0/16 address space to VNet1.
Step 3: Recreate peering between VNet1 and VNet2
References:
https://docs.microsoft.com/en-us/azure/virtual-network/virtual-network-manage-peering

NEW QUESTION: 3
Note: This question is part of a series of questions that use the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question in this series.
You plan to create a predictive analytics solution for credit risk assessment and fraud prediction in Azure Machine Learning. The Machine Learning workspace for the solution will be shared with other users in your organization. You will add assets to projects and conduct experiments in the workspace.
The experiments will be used for training models that will be published to provide scoring from web services.
The experiment for fraud prediction will use Machine Learning modules and APIs to train the models and will predict probabilities in an Apache Hadoop ecosystem.
You plan to configure the resources for part of a workflow that will be used to preprocess data from files stored in Azure Blob storage. You plan to use Python to preprocess and store the data in Hadoop.
You need to get the data into Hadoop as quickly as possible.
Which three actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. Create an Azure virtual machine (VM), and then configure MapReduce on the VM.
B. Process the files by using Python to store the data to a Hadoop instance.
C. Create an Azure virtual machine (VM), and then install an IPython Notebook server.
D. Create an Azure HDInsight Hadoop cluster.
E. Create the Machine learning experiment, and then add an Execute Python Script module.
Answer: B,D,E
Explanation:
Explanation/Reference:
Explanation: