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NEW QUESTION: 1
Which 2 statements about SNMP are true? (choose two)
A. The SNMP manager is responsible for collecting data for the MIB
B. SNMPv3 uses the 3DES encryption algorithm to provide data privacy
C. The SNMP agent is responsible for collecting data from the MIB
D. SNMPv2c supports only 32-bit counters
E. SNMPv3 uses the SHA encryption algorithm to provide authorization
F. SNMPv2c can provide authentication as well as encryption
Answer: B,C

NEW QUESTION: 2
What information can be used to reliably determine the type of an uploaded file?
A. File name extension
B. MIME type
C. Contents of the file
Answer: C

NEW QUESTION: 3
注：この質問は、同じシナリオを提示する一連の質問の一部です。シリーズの各質問には、指定された目標を達成する可能性のある独自のソリューションが含まれています。一部の質問セットには複数の正しい解決策がある場合がありますが、他の質問セットには正しい解決策がない場合があります。
このセクションの質問に回答した後は、その質問に戻ることはできません。その結果、これらの質問はレビュー画面に表示されません。
スクリプトという名前のローカルフォルダーにtrain.pyという名前のPythonスクリプトがあります。スクリプトは、scikit-learnを使用して回帰モデルをトレーニングします。スクリプトには、やはりスクリプトフォルダーにあるトレーニングデータファイルを読み込むコードが含まれています。
スクリプトは、aml-computeという名前の計算クラスターでAzure ML実験として実行する必要があります。
モデルトレーニングに必要なパッケージが環境に含まれるように、実行を構成する必要があります。ターゲットの計算クラスターを参照するaml-computeという名前の変数をインスタンス化しました。
解決策：次のコードを実行します。

ソリューションは目標を達成していますか？
A. いいえ
B. はい
Answer: A
Explanation:
The scikit-learn estimator provides a simple way of launching a scikit-learn training job on a compute target. It is implemented through the SKLearn class, which can be used to support single-node CPU training.
Example:
from azureml.train.sklearn import SKLearn
}
estimator = SKLearn(source_directory=project_folder,
compute_target=compute_target,
entry_script='train_iris.py'
)
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-scikit-learn