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NEW QUESTION: 1
顧客の要望：
組織のvSphere 6.5データセンター設計を作成する必要があります。組織は、3つの定義された仮想マシンのパフォーマンスレベルを必要としています。
-ゴールドティア-高ワークロードVM
-シルバー層-中程度のワークロードVM
-ブロンズ層-開発ワークロードVM
組織には、設計で使用できる8つのESXiホストがあります。 5つのホストは古い「中程度のパフォーマンス」ホストであり、最後の3つは他のホストと比較してより優れたリソースを提供する新しい「ハイパフォーマンス」ホストです。組織は、設計が満たさなければならない要件のリストを提供しています。
-ゴールドティア仮想マシンは、使用可能な高性能サーバーがない場合を除き、高性能サーバーでのみ実行する必要があります。また、配置に関係なく、利用可能なリソース全体の75％を割り当てる必要があります。
-中程度のパフォーマンスのサーバーが利用できない場合を除き、Silver Tier仮想マシンは中程度のパフォーマンスのサーバーでのみ実行する必要があります。また、配置に関係なく、利用可能なリソース全体の25％を割り当てる必要があります。
-ブロンズ層の仮想マシンは、中パフォーマンスのサーバーでのみ実行する必要があります。また、シルバー層に割り当てられたリソースから35％のリソースのサブセットを受け取る必要があります。
設計要件：
顧客の要件を満たすために必要なリソース割り当てとクラスターポリシーを示す論理設計を作成します。設計には以下を含める必要があります。
-必要なすべてのサーバー
-すべての必要なリソース
必要なDRSグループにホストを配置します。仮想マシンを適切なリソースプールに配置します。必要に応じて、親を子リソースプールコネクタに接続します。必要に応じて、適切なアフィニティコネクタを接続します。
















A. 以下の解決策を参照してください
解答については以下を確認してください






B. 以下の解決策を参照してください
解答については以下を確認してください









Answer: B

NEW QUESTION: 2
Suppose you have a dataset of images that are each labeled as to whether or not they contain a human face. To create a neural network that recognizes human faces in images using this labeled dataset, what approach would likely be the most effective?
A. Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.
B. Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
C. Use K-means Clustering to detect faces in the pixels.
D. Use feature engineering to add features for eyes, noses, and mouths to the input data.
Answer: B
Explanation:
Traditional machine learning relies on shallow nets, composed of one input and one output layer, and at most one hidden layer in between. More than three layers (including input and output) qualifies as "deep" learning. So deep is a strictly defined, technical term that means more than one hidden layer.
In deep-learning networks, each layer of nodes trains on a distinct set of features based on the previous layer's output. The further you advance into the neural net, the more complex the features your nodes can recognize, since they aggregate and recombine features from the previous layer.
A neural network with only one hidden layer would be unable to automatically recognize high-level features of faces, such as eyes, because it wouldn't be able to "build" these features using previous hidden layers that detect low-level features, such as lines. Feature engineering is difficult to perform on raw image data.
K-means Clustering is an unsupervised learning method used to categorize unlabeled data.
Reference: https://deeplearning4j.org/neuralnet-overview

NEW QUESTION: 3
You are designing a real-time processing solution for maintenance work requests that are received via email.
The solution will perform the following actions:
* Store all email messages in an archive.
* Access weather forecast data by using the Python SDK for Azure Open Datasets.
* Identify high priority requests that will be affected by poor weather conditions and store the requests in an Azure SQL database.
The solution must minimize costs.
How should you complete the solution? To answer, drag the appropriate services to the correct locations. Each service may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation

Box 1: Azure Storage
Azure Event Hubs enables you to automatically capture the streaming data in Event Hubs in an Azure Blob storage or Azure Data Lake Storage Gen 1 or Gen 2 account of your choice, with the added flexibility of specifying a time or size interval. Setting up Capture is fast, there are no administrative costs to run it, and it scales automatically with Event Hubs throughput units. Event Hubs Capture is the easiest way to load streaming data into Azure, and enables you to focus on data processing rather than on data capture.
Box 2: Azure Logic Apps
You can monitor and manage events sent to Azure Event Hubs from inside a logic app with the Azure Event Hubs connector. That way, you can create logic apps that automate tasks and workflows for checking, sending, and receiving events from your Event Hub.
Reference:
https://docs.microsoft.com/en-us/azure/event-hubs/event-hubs-capture-overview
https://docs.microsoft.com/en-us/azure/connectors/connectors-create-api-azure-event-hubs

NEW QUESTION: 4

A. Option B
B. Option D
C. Option C
D. Option A
Answer: B,D