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NEW QUESTION: 1
You plan to use the Hyperdrive feature of Azure Machine Learning to determine the optimal hyperparameter values when training a model.
You must use Hyperdrive to try combinations of the following hyperparameter values. You must not apply an early termination policy.
learning_rate: any value between 0.001 and 0.1
* batch_size: 16, 32, or 64
You need to configure the sampling method for the Hyperdrive experiment Which two sampling methods can you use? Each correct answer is a complete solution.
NOTE: Each correct selection is worth one point.
A. Random sampling
C: Bayesian sampling is based on the Bayesian optimization algorithm and makes intelligent choices on the hyperparameter values to sample next. It picks the sample based on how the previous samples performed, such that the new sample improves the reported primary metric.
Bayesian sampling does not support any early termination policy
Example:
from azureml.train.hyperdrive import BayesianParameterSampling
from azureml.train.hyperdrive import uniform, choice
param_sampling = BayesianParameterSampling( {
"learning_rate": uniform(0.05, 0.1),
"batch_size": choice(16, 32, 64, 128)
}
)
D: 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.
Incorrect Answers:
B: Grid sampling can be used if your hyperparameter space can be defined as a choice among discrete values and if you have sufficient budget to exhaustively search over all values in the defined search space. Additionally, one can use automated early termination of poorly performing runs, which reduces wastage of resources.
Example, the following space has a total of six samples:
from azureml.train.hyperdrive import GridParameterSampling
from azureml.train.hyperdrive import choice
param_sampling = GridParameterSampling( {
"num_hidden_layers": choice(1, 2, 3),
"batch_size": choice(16, 32)
}
)
B. No sampling
C. Bayesian sampling
D. Grid sampling
Answer: A,C
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters

NEW QUESTION: 2
会社には、VPCのプライベートサブネット内のAmazon EC2インスタンスで実行されるアプリケーションがあります。インスタンスは、同じAWSリージョンのAmazon S3バケットのデータにアクセスします。 VPCには、S3バケットにアクセスするためのパブリックサブネットにNATゲートウェイが含まれています。会社は、セキュリティや冗長性を損なうことなくNATゲートウェイを交換することでコストを削減したいと考えています。これらの要件を満たすソリューションはどれですか。
A. NATゲートウェイをインターネットゲートウェイに置き換えます。
B. NATゲートウェイをAWS Direct Connect接続に置き換えます
C. NATゲートウェイをゲートウェイVPCエンドポイントに置き換えます
D. NATゲートウェイをNATインスタンスに置き換えます
Answer: C
Explanation:

NEW QUESTION: 3
計算ビューで、ユーザー定義関数から値を導出する入力パラメーターを作成する場合、何を考慮する必要がありますか？
この質問には2つの正解があります。
応答：
A. 関数はスカラー型である必要があります。
B. 生成された値は実行時に上書きできます。
C. 関数は複数の値を返すことができます。
D. 入力パラメーターは必須に設定する必要があります。
Answer: A,B