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NEW QUESTION: 1
After installing a Steelhead appliance at a remote site, users started complaining about some optimized applications being slower. After some initial investigating, you discovered a backup job was consuming most of the bandwidth causing congestion. You also found that QoS is configured on the router and should have prevented this but it did not. What is the most likely cause?
A. QoS on the router identifies traffic using destination IP and ports while the Steelheads are configured to use correct addressing.
B. QoS on the router identifies traffic using destination IP and ports while the Steelheads are configured to use full transparency.
C. Simplified routing is not enable on the remote Steelhead appliance.
D. Asymmetric routing.
E. Simplified routing is not enable on the data center Steelhead appliance.
Answer: A
Explanation:
Explanation/Reference:
NEW QUESTION: 2
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
NEW QUESTION: 3
When planning the Avaya Session Border Controller for Enterprise (SBCE) for SIP Trunking, what is a good practice to adopt?
A. Use the same IP address on both, internal and external sides of the network.
B. Name Interfaces consistently, for example, A1 for Internal network to Call Server and B1 for external to Trunk Server.
C. Use one Avaya Session Border Controller for Enterprise on the internal and external sides of the network.
D. Name all internal and external interfaces exactly the same.
Answer: B
Explanation:
Explanation/Reference:
Use the same interface mapping throughout! Examples in this section use:
A1 as the internal interface (to the call server)
B1 as the external interface (to the trunk server)
References: Avaya Aura Session Border Controller Enterprise Implementation and Maintenance (2012), page 304
NEW QUESTION: 4
You need to visually identify whether outliers exist in the Age column and quantify the outliers before the outliers are removed.
Which three Azure Machine Learning Studio modules should you use in sequence? To answer, move the appropriate modules from the list of modules to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
You can use the Clip Values module in Azure Machine Learning Studio, to identify and optionally replace data values that are above or below a specified threshold. This is useful when you want to remove outliers or replace them with a mean, a constant, or other substitute value.
References:
https://blogs.msdn.microsoft.com/azuredev/2017/05/27/data-cleansing-tools-in-azure-machine-learning/
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clip-values