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NEW QUESTION: 1
Which tow options describe benefits of aggregated chassis technology? (Choose two.)
A. It supports redundant configuration files.
B. It reduces management overhead.
C. It requires only one IP address per VLAN.
D. It supports HSRP, VRRP, and GLBP.
E. Switches can be located anywhere regardless of their physical distance from one another.
F. It requires only three IP addresses per VLAN.
Answer: B,C

NEW QUESTION: 2
Which two options are valid for enabling Layer 3 control between Cisco Nexus 1000V Series VSM and VEM?
(Choose two)
A. A dedicated control interfaces
B. An out-of-band management interface
C. A dedicated packet interfaces
D. A virtual Ethernet interfaces
E. A VMWare vCenter virtual machine virtual interface
Answer: A,C

NEW QUESTION: 3
Azure Machine Learning Studioを使用して、バイナリ分類モデルを作成します。
モデルのパラメータースイープを実行して、ハイパーパラメーターを調整する必要があります。パラメータスイープは、次の要件を満たしている必要があります。
*ハイパーパラメーターの可能なすべての組み合わせを反復する
*スイープの実行に必要なコンピューティングリソースを最小限に抑える
*モデルのパラメータースイープを実行する必要があります。
どのパラメータースイープモードを使用する必要がありますか？
A. スイープクラスタリング
B. グリッド全体
C. ランダムシード
D. ランダムグリッド
E. ランダムスイープ
Answer: D
Explanation:
Maximum number of runs on random grid: This option also controls the number of iterations over a random sampling of parameter values, but the values are not generated randomly from the specified range; instead, a matrix is created of all possible combinations of parameter values and a random sampling is taken over the matrix. This method is more efficient and less prone to regional oversampling or undersampling.
If you are training a model that supports an integrated parameter sweep, you can also set a range of seed values to use and iterate over the random seeds as well. This is optional, but can be useful for avoiding bias introduced by seed selection.
Incorrect Answers:
B: If you are building a clustering model, use Sweep Clustering to automatically determine the optimum number of clusters and other parameters.
C: Entire grid: When you select this option, the module loops over a grid predefined by the system, to try different combinations and identify the best learner. This option is useful for cases where you don't know what the best parameter settings might be and want to try all possible combination of values.
E: If you choose a random sweep, you can specify how many times the model should be trained, using a random combination of parameter values.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/tune-model-hyperparameters

NEW QUESTION: 4
DRAG DROP


Answer:
Explanation:

Explanation:

References:
https://docs.microsoft.com/en-us/aspnet/core/publishing/azure-continuous-deployment