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NEW QUESTION: 1
Ein Kunde verlangt, dass eine Abwesenheitsart für einen Arzttermin nur für einen Teil eines Tages eingegeben werden kann und reduziert auch eine Quote. Wie konfigurieren Sie Abwesenheitseingabeprüfungen und Erfassungsbilder für die Abwesenheitsart im Customizing, um diese Anforderung zu erfüllen? (Wähle zwei)
A. Lassen Sie die Felder für die minimale und maximale Dauer leer.
B. Lassen Sie das Feld für die minimale Dauer leer und geben Sie 999 für die maximale Dauer ein.
C. Weisen Sie im Feld Mindestdauer einen Bruchteil eines Tages zu.
D. Bild 2001 für Kontingentabzug zuordnen.
Answer: A,D
NEW QUESTION: 2
カスタムリードフィールドは以下にマッピングできます。
A. 1つのケースコメント
B. 1つの勘定科目フィールド
C. 1つのソリューションフィールド
D. 2つの連絡先フィールド
Answer: B
NEW QUESTION: 3
AzureStorageアカウントのBLOBコンテナーを参照するtraining_dataという名前のデータストアを作成します。 blobコンテナーには、複数のコンマ区切り値（CSV）ファイルが格納されるという名前のフォルダーが含まれています。
./scriptという名前のローカルフォルダーにtrain.pyという名前のスクリプトがあり、推定器を使用して実験として実行する予定です。スクリプトには、csv_filesフォルダーからデータを読み取るための次のコードが含まれています。
次のスクリプトがあります。
スクリプトがtraining_dataデータストアのcsv_filesフォルダーを参照するdata_refという名前のデータ参照からデータを読み取ることができるように、実験の推定器を構成する必要があります。
Estimatorを構成するためにどのコードを使用する必要がありますか？
A. オプションE
B. オプションD
C. オプションC
D. オプションA
E. オプションB
Answer: E
Explanation:
Explanation
Besides passing the dataset through the inputs parameter in the estimator, you can also pass the dataset through script_params and get the data path (mounting point) in your training script via arguments. This way, you can keep your training script independent of azureml-sdk. In other words, you will be able use the same training script for local debugging and remote training on any cloud platform.
Example:
from azureml.train.sklearn import SKLearn
script_params = {
# mount the dataset on the remote compute and pass the mounted path as an argument to the training script
'--data-folder': mnist_ds.as_named_input('mnist').as_mount(),
'--regularization': 0.5
}
est = SKLearn(source_directory=script_folder,
script_params=script_params,
compute_target=compute_target,
environment_definition=env,
entry_script='train_mnist.py')
# Run the experiment
run = experiment.submit(est)
run.wait_for_completion(show_output=True)
Reference:
https://docs.microsoft.com/es-es/azure/machine-learning/how-to-train-with-datasets