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NEW QUESTION: 1
ホットスポット
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。

Answer:
Explanation:

参照：
https://docs.python.org/2.0/ref/try.html

NEW QUESTION: 2
Server A hosts a mission-critical business application. The server has one disk and one NIC. The administrator has been asked to implement a solution to meet the business requirement that the application must maintain high availability, even in case of a hardware failure. Which of the following BEST meets this requirement?
A. Configure failover clustering using Server A and a new server.
B. Move the application to another server that has more resources.
C. Add an additional NIC and configure the NIC as standby.
D. Add multiple disks on the server and configure RAID 5.
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