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NEW QUESTION: 1
次の表のリソースを含むSubscription1という名前のAzureサブスクリプションがあります。

VM1およびVM2は、次の表のWebサイトを実行します。

AppGW1には、次の表にバックエンドプールがあります。

DNSは、site1.contoso.com、site2.contoso.com、およびsite3.contoso.comをAppGW1のIPアドレスに解決します。
AppGW1には、次の表のリスナーがあります。

AppGW1には、次の表のルールがあります。

以下の各ステートメントについて、ステートメントが真である場合は「はい」を選択します。それ以外の場合は、「いいえ」を選択します。
注：各正しい選択には1ポイントの価値があります。

Answer:
Explanation:

Explanation:
Vm1 is in Pool1. Rule2 applies to Pool1, Listener 2, and site2.contoso.com

NEW QUESTION: 2
Ihr Unternehmen ist aufgrund einer unglücklichen Aktion eines Ihrer Mitarbeiter in den Nachrichten. Die Telefone klingeln, wenn Kunden ihre Verträge kündigen möchten. Wie nennen wir diese Art von Schaden?
A. Indirekter Schaden
B. Direkter Schaden
Answer: A

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

NEW QUESTION: 4
Which LAN topology below is MOST vulnerable to a single point of
failure?
A. Physical Star
B. Logical Ring
C. Ethernet Bus
D. FDDI
Answer: C
Explanation:
Ethernet bus topology was the first commercially viable network
topology, and consists of all workstations connected to a single coaxial cable. Since the cable must be properly terminated on both ends, a break in the cable stops all communications on the bus.
* the physical star topology acts like a logical bus, but provides better fault tolerance, as a cable break only disconnects the workstation or hub directly affected.
* logical ring topology, is used by Token Ring and FDDI and is highly resilient. Token Ring employs a beacon frame, which, in case of a cable break, initiates auto reconfiguration and attempts to reroute the network around the failed mode. Also, the Token Ring active monitor station performs ring maintenance functions, like removing continuously circulating frames from the
ring. FDDI employs a second ring to provide redundancy. Sources:
Virtual LANs by Mariana Smith (McGraw-Hill, 1998) and Internetworking
Technologies Handbook, Second Edition (Cisco Press, 1998).