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NEW QUESTION: 1
注文が発行されたときに販売注文を対応する顧客と組み合わせて、SAP HANA計算ビューをモデル化します。
どの結合プロパティに必要な結合タイプを割り当てますか？
正解を選択してください。
応答：
A. 動的結合プロパティを使用した参照結合
B. 一時的な結合プロパティを使用した参照結合
C. 動的結合プロパティを使用した内部結合
D. 時間的結合プロパティを使用した内部結合
Answer: B
NEW QUESTION: 2
WebApp1이라는 Azure 웹앱을 개발 중입니다. WebApp1은 B1 요금 계층을 사용하는 Plan1이라는 Azure App Service 계획을 사용합니다.
CPU 사용량이 10 분 동안 70 %를 초과하는 경우 앱의 추가 인스턴스를 추가하도록 WebApp1을 구성해야 합니다.
어떤 세 가지 작업을 순서대로 수행해야 합니까? 응답하려면 적절한 조치를 조치 목록에서 응답 영역으로 이동하고 올바른 순서로 정렬하십시오.
Answer:
Explanation:
설명
상자 1 : 수평 확장 (앱 서비스 계획) 설정 블레이드에서 가격 계층 변경 B1 가격 계층은 1 코어만 허용합니다. 다른 가격 등급을 선택해야 합니다.
상자 2 : 확장 (앱 서비스 계획) 설정 블레이드에서 자동 확장을 활성화 합니다
* 로그
http://portal.azure.com의 Azure Portal에
* 자동 확장하려는 앱 서비스로 이동하십시오.
* 메뉴에서 스케일 아웃 (앱 서비스 계획)을 선택하십시오.
* 자동 스케일 활성화를 클릭하십시오. 스케일링 규칙 편집기를 활성화 합니다.
상자 3 : 척도 모드에서 메트릭 기반 척도로, 규칙을 추가하고 인스턴스 제한을 설정하십시오.
규칙 추가를 클릭하십시오. 여기에는 규칙을 만들고 배율 세부 정보를 지정할수 있는 양식이 표시됩니다.
참고 문헌 :
https://azure.microsoft.com/en-us/pricing/details/app-service/windows/
https://blogs.msdn.microsoft.com/hsirtl/2017/07/03/autoscaling-azure-web-apps/
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. ベイジアンサンプリング
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. グリッドサンプリング
Answer: B,C
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters