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NEW QUESTION: 1
Linuxノードで2種類のジョブを実行するためにAzureBatchを使用するコスト最適化ソリューションを設計しています。
最初のジョブタイプは、開発環境の短期間のタスクで構成されます。 2番目のjotタイプは、タイムリーなジョブ完了を必要とする実稼働環境向けの長時間実行されるMessage Passing Interface（MPI）アプリケーションで構成されます。
ジョブタイプごとにプールタイプとノードタイプを推奨する必要があります。このソリューションでは、計算料金を最小限に抑え、可能な限りAzure HybridBenefitを活用する必要があります。
あなたは何をお勧めしますか？回答するには、回答領域で適切なオプションを選択します。
注：正しい選択はそれぞれ1ポイントの価値があります。
Answer:
Explanation:
Explanation:
Box 1: User subscription and low-priority virtual machines
Azure Batch offers low-priority virtual machines (VMs) to reduce the cost of Batch workloads. Low-priority VMs make new types of Batch workloads possible by enabling a large amount of compute power to be used for a very low cost.
Some examples of batch processing use cases well suited to use low-priority VMs are:
Development and testing: In particular, if large-scale solutions are being developed, significant savings can be realized. All types of testing can benefit, but large-scale load testing and regression testing are great uses.
Supplementing on-demand capacity.
Flexible job execution time.
Box 2: Batch service and dedicate virtual machines
Reference:
https://docs.microsoft.com/en-us/azure/batch/batch-low-pri-vms
NEW QUESTION: 2
How does the system determine the proper storage bin during the putaway operation?
A. None of the above
B. The first step is determination of storage type, then the storage section and lastly the target storage bin
C. The first step is determination of storage section, then the storage type and lastly the target storage bin
D. The first step is determination of storage bin, then the storage section and lastly the target storage type
Answer: B
NEW QUESTION: 3
You are building recurrent neural network to perform a binary classification.
The training loss, validation loss, training accuracy, and validation accuracy of each training epoch has been provided. You need to identify whether the classification model is over fitted.
Which of the following is correct?
A. The training loss decreases while the validation loss increases when training the model.
B. The training loss stays constant and the validation loss decreases when training the model.
C. The training loss .stays constant and the validation loss stays on a constant value and close to the training loss value when training the model.
D. The training loss increases while the validation loss decreases when training the model.
Answer: A
Explanation:
Explanation
An overfit model is one where performance on the train set is good and continues to improve, whereas performance on the validation set improves to a point and then begins to degrade.
References:
https://machinelearningmastery.com/diagnose-overfitting-underfitting-lstm-models/