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NEW QUESTION: 1
You are developing an IoT solution.
The solution requires bidirectional communication between a client .NET application and Azure IoT Hub. A
.NET back-end application will connect to the IoT Hub to process information.
You need to collect the values required for the back-end application to connect with the newly created IoT Hub.
How should you complete the commands? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION: 2
A NameNode in Hadoop 2.0 manages ______________.
A. A single namespace
B. No namespaces
C. An arbitrary number of namespaces
D. Two namespaces: an active namespace and a backup namespace
Answer: A
NEW QUESTION: 3
トレーニングと検証のエラー値に大きな差があるモデルがあります。
新しいモデルを作成し、相互検証を実行する必要があります。
Azure Machine Learning Studioを使用して、新しいモデルのパラメーターセットを識別する必要があります。
各ステップで使用するモジュールはどれですか？答えるには、適切なモジュールを正しい手順にドラッグします。
各モジュールは、1回または複数回使用することも、まったく使用しないこともできます。コンテンツを表示するには、ペイン間で分割バーをドラッグするか、スクロールする必要がある場合があります。
注：それぞれの正しい選択には1ポイントの価値があります。
Answer:
Explanation:
Explanation:
Box 1: Split data
Box 2: Partition and Sample
Box 3: Two-Class Boosted Decision Tree
Box 4: Tune Model Hyperparameters
Integrated train and tune: You configure a set of parameters to use, and then let the module iterate over multiple combinations, measuring accuracy until it finds a "best" model. With most learner modules, you can choose which parameters should be changed during the training process, and which should remain fixed.
We recommend that you use Cross-Validate Model to establish the goodness of the model given the specified parameters. Use Tune Model Hyperparameters to identify the optimal parameters.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/partition-and-sample