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NEW QUESTION: 1
ユーザーがAuto Scalingグループのスケーリングプロセスを一時停止しました。インスタンス数を増やすためのスケーリングアクティビティは既に進行中です。
停止はそのアクティビティにどのような影響を与えますか？
A. インスタンスを一時的に停止します
B. インスタンスの起動を一時停止し、Auto Scalingの再開後にのみ起動します
C. インスタンスを終了します
D. 影響なし。スケーリングアクティビティは継続します
Answer: D
Explanation:
The user may want to stop the automated scaling processes on the Auto Scaling groups either to perform manual operations or during emergency situations. To perform this, the user can suspend one or more scaling processes at any time. When this process is suspended, Auto Scaling creates no new scaling activities for that group. Scaling activities that were already in progress before the group was suspended continue until completed.
http://docs.aws.amazon.com/AutoScaling/latest/DeveloperGuide/AS_Concepts.html

NEW QUESTION: 2
You have a server named Print1 that runs Windows Server 2012 R2.On Print1, you share a printer named Printer1.
You need to ensure that only the members of the Server Operators group, the Administrators group, and the Print Operators group can send print jobs to Printer1.
What should you do?
A. Assign the Print permission to the Administrators group
B. Remove the permissions for the Everyone group
C. Remove the permissions for the Creator Owner group
D. Assign the Print permission to the Server Operators group
Answer: B
Explanation:
By default Everyone can print. This permissions need to be removed.

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B
B. オプションD
C. オプションA
D. オプションC
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