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NEW QUESTION: 1
イーサネットスイッチには8つのポートがあり、すべてのポートが月曜日から金曜日まで24時間稼働している必要があります。ただし、Port4の障害は次のように発生します。
*月曜日=午後8時から午後9時
*水曜日の午後7時から午後9時
*木曜日=午前6時から午前9時
*土曜日=午後4時から7時
ポート4のMTTRとは何ですか？
A. 3時間
B. 28時間
C. 38時間
D. 2時間
Answer: D
Explanation:
MTTR = Total downtime / Number of failures
MTTR = 1+2+3 / 3 = 2

NEW QUESTION: 2
Your network contains a server named Server1 that runs Windows Server 2008 R2. Server1 has the Web Server (IIS) role installed. You need to review the contents of the IIS-Configuration
Analytic event log on Server1. You configure Event Viewer to show the Analytic log. What should you do next?
A. Modify the Subscriptions list for the log.
B. Create a custom view to the log.
C. Attach a task to the log.
D. Modify the General properties of the log.
Answer: D
Explanation:
Analytic event logs, and not only for IIS are not enabled by default. You are enable it. You should enable them from "General Tab" of properties of log "Log of services and aplications\Microsoft\Windows\IIS-Configuration\Analytic" to start logging

NEW QUESTION: 3
A home office has a dozen devices that need a class based DHCP device to assign addresses. The subnet only has one octet for the host portion of each device. Which of the following IP addresses would be assigned to the default gateway?
A. 224.0.0.1
B. 169.254.0.1
C. 192.168.0.1
D. 10.0.0.1
Answer: C

NEW QUESTION: 4
Azure Machine Learning Serviceを使用して、ニューラルネットワーク分類モデルのハイパーパラメーター探索を自動化しています。
次の要件に従ってランダムサンプリングを使用してハイパーパラメーターを自動的に調整するには、ハイパーパラメーター空間を定義する必要があります。
*学習率は、平均値が10、標準偏差が3の正規分布から選択する必要があります。
*バッチサイズは16、32、64でなければなりません。
*維持確率は、0.05から0.1の範囲の均一な分布から選択された値でなければなりません。
Azure Machine Learning ServiceのPython APIのparam_samplingメソッドを使用する必要があります。
コードセグメントをどのように完成させる必要がありますか？ 回答するには、回答エリアで適切なオプションを選択します。
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

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