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NEW QUESTION: 1
各アダプタをその特性に合わせます。
Answer:
Explanation:
説明
NEW QUESTION: 2
A Dell EMC SC Series customer has purchased the Storage Optimization bundle and wants to ensure that Data Progression works efficiently. Which snapshot schedule conforms to best practice when using Data Progression?
A. Weekly snapshot with an expiration of exactly 7 days
B. Daily snapshot with an expiration of at least 25 hours
C. Weekly snapshot with an expiration of at least 8 days
D. Daily snapshot with an expiration of exactly 24 hours
Answer: D
NEW QUESTION: 3
Azure Machine Learningサービスを使用して、training.dataという名前の表形式のデータセットを作成します。このデータセットをトレーニングスクリプトで使用する予定です。
次のコードを使用して、データセットを参照する変数を作成します。
training_ds = workspace.datasets.get（ "training_data"）
スクリプトを実行する推定器を定義します。
スクリプトがtraining.dataデータセットにアクセスできるようにするには、推定器の正しいプロパティを設定する必要があります。どのプロパティを設定する必要がありますか？
A）
B）
C）
D）
A. オプションD
B. オプションC
C. オプションA
D. オプションB
Answer: C
Explanation:
Explanation
Example:
# Get the training dataset
diabetes_ds = ws.datasets.get("Diabetes Dataset")
# Create an estimator that uses the remote compute
hyper_estimator = SKLearn(source_directory=experiment_folder,
inputs=[diabetes_ds.as_named_input('diabetes')], # Pass the dataset as an input compute_target = cpu_cluster, conda_packages=['pandas','ipykernel','matplotlib'], pip_packages=['azureml-sdk','argparse','pyarrow'], entry_script='diabetes_training.py') Reference:
https://notebooks.azure.com/GraemeMalcolm/projects/azureml-primers/html/04%20-%20Optimizing%20Model