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NEW QUESTION: 1

A. Option C
B. Option B
C. Option D
D. Option A
Answer: B,D

NEW QUESTION: 2
Given:

and the code fragment:

Which two code fragments, when inserted at line n1independently, enable the code to print TruckCarBike?
A. .map (v -> v.getVid())
.sorted ()
B. .sorted((v1, v2) -> Integer.compare(v1.getVId(), v2.getVid()))
C. .sorted ((v1, v2) -> v1.getVId() < v2.getVId())
D. .sorted(Comparator.comparing ((Vehicle v) -> v.getVId()))
E. .sorted (Comparable.comparing (Vehicle: :getVName)).reversed ()
Answer: B

NEW QUESTION: 3
Azure Machine Learningのハイパードライブ機能を使用して、モデルをトレーニングするときに最適なハイパーパラメーター値を決定することを計画しています。
次のハイパーパラメータ値の組み合わせを試すには、ハイパードライブを使用する必要があります。早期終了ポリシーを適用してはなりません。
Learning_rate：0.001〜0.1の任意の値
* batch_size：16、32、または64
ハイパードライブ実験のサンプリング方法を構成する必要がありますどの2つのサンプリング方法を使用できますか？それぞれの正解は完全な解決策です。
注：正しい選択はそれぞれ1ポイントの価値があります。
A. サンプリングなし
B. グリッドサンプリング
C. ランダムサンプリング
C：ベイズサンプリングはベイズ最適化アルゴリズムに基づいており、次にサンプリングするハイパーパラメータ値をインテリジェントに選択します。新しいサンプルが報告されたプライマリメトリックを改善するように、以前のサンプルの実行方法に基づいてサンプルを選択します。
ベイジアンサンプリングは、早期終了ポリシーをサポートしていません
例：
azureml.train.hyperdriveからimportBayesianParameterSampling
azureml.train.hyperdriveからインポートユニフォーム、選択
param_sampling = BayesianParameterSampling( {
"learning_rate": uniform(0.05, 0.1),
"batch_size": choice(16, 32, 64, 128)
}
)
D：ランダムサンプリングでは、ハイパーパラメータ値は定義された検索空間からランダムに選択されます。ランダムサンプリングにより、検索スペースに離散ハイパーパラメータと連続ハイパーパラメータの両方を含めることができます。
不正解：
B：ハイパーパラメータ空間を離散値の中から選択して定義でき、定義された検索空間内のすべての値を徹底的に検索するのに十分な予算がある場合は、グリッドサンプリングを使用できます。さらに、パフォーマンスの低い実行の自動早期終了を使用できるため、リソースの浪費を減らすことができます。
たとえば、次のスペースには合計6つのサンプルがあります。
azureml.train.hyperdriveからインポートGridParameterSampling
azureml.train.hyperdriveインポート選択から
param_sampling = GridParameterSampling( {
"num_hidden_layers": choice(1, 2, 3),
"batch_size": choice(16, 32)
}
)
D. ベイジアンサンプリング
Answer: C,D
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters