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NEW QUESTION: 1
Based on this interface configuration, what is the expected state of OSPF adjacency?
A. Full on both routers
B. FULL/BDR on R1 and FULL/BDR on R2
C. 2WAY/DROTHER on both routers
D. not established
Answer: D
Explanation:
Explanation
On Ethernet interfaces the OSPF hello intervl is 10 second by default so in this case there would be a Hello interval mismatch -> the OSPF adjacency would not be established.
NEW QUESTION: 2
Based on the network diagram (click on exhibit), R2 wants to connect to the networks behind CR1 using a static-route. What is the correct syntax given the choices below? R1 has a valid static-route defined for networks behind CR1.
A. config router static-route 192.168.2.0/24 next-hop 10.2.3.2
B. config router static-route 192.168.2.0/24 next-hop 10.1.1.1
C. none of the above
D. config router static-route 192.168.2.0/24 next-hop 10.1.1.2
E. config router static-route 192.168.2.0/24 next-hop 10.2.3.1
Answer: B
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