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NEW QUESTION: 1
After implementing mesh with an IP surveillance camera connected to the LAN port on a
RAP, the engineer notices that QoS is not being marked. In this setup, what device is responsible for marking upstream traffic from the camera?
A. IP Camera
B. First-Hop Router
C. RAP
D. MAP
E. Wireless Controller
Answer: A

NEW QUESTION: 2
注：この質問は、同じシナリオを提示する一連の質問の一部です。シリーズの各質問には、指定された目標を達成する可能性のある独自のソリューションが含まれています。一部の質問セットには複数の正しい解決策がある場合がありますが、他の質問セットには正しい解決策がない場合があります。
このセクションの質問に回答した後は、その質問に戻ることはできません。その結果、これらの質問はレビュー画面に表示されません。
AzureサブスクリプションのTraffic Analyticsを有効にするために、Admin1という名前のAzure Active Directory（Azure AD）ユーザーに必要なロールが割り当てられていることを確認する必要があります。
解決策：サブスクリプションレベルでのトラフィックマネージャーコントリビューターロールをAdmin1に割り当てます。
A. いいえ
B. はい
Answer: B
Explanation:
Explanation
With Traffic Manager Contributor role you can manage Traffic Manager profiles, do traffic analysis but does not let you control who has access to them.
Reference:
https://docs.microsoft.com/en-us/azure/network-watcher/traffic-analytics
https://docs.microsoft.com/en-us/azure/role-based-access-control/built-in-roles

NEW QUESTION: 3
You are using the Azure Machine Learning Service to automate hyperparameter exploration of your neural network classification model.
You must define the hyperparameter space to automatically tune hyperparameters using random sampling according to following requirements:
* The learning rate must be selected from a normal distribution with a mean value of 10 and a standard deviation of 3.
* Batch size must be 16, 32 and 64.
* Keep probability must be a value selected from a uniform distribution between the range of 0.05 and 0.1.
You need to use the param_sampling method of the Python API for the Azure Machine Learning Service.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:
In random sampling, hyperparameter values are randomly selected from the defined search space. Random sampling allows the search space to include both discrete and continuous hyperparameters.
Example:
from azureml.train.hyperdrive import RandomParameterSampling
param_sampling = RandomParameterSampling( {
"learning_rate": normal(10, 3),
"keep_probability": uniform(0.05, 0.1),
"batch_size": choice(16, 32, 64)
}
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-tune-hyperparameters