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NEW QUESTION: 1
Agile Teams use Planning Poker for estimation. Which of the following is NOT true about Planning Poker?
A. The team for Planning Poker should not exceed more than 10 participants
B. It combines expert opinion, analogy, and disaggregation into an enjoyable approach to estimating that results in quick but reliable estimates.
C. The highest estimate is considered the final estimate
D. Participants include all of the developers on the team
Answer: C
NEW QUESTION: 2
You need to center and indent a quote on your Web page. Which is the best way to accomplish this using HTML5?
A. Use the <blockquote> element.
B. Use the style attribute 1%Iith the <p> tag.
C. Use the text-align attribute with the <p> tag.
D. Use the <pre> element.
Answer: A
NEW QUESTION: 3
You need to build a function that meets the following requirements:
How should you complete the Transact-SQL statement? To answer, drag the appropriate Transact-SQL statements to the correct locations. Each Transact-SQL segment may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
Answer:
Explanation:
Explanation
References: https://technet.microsoft.com/en-us/library/ms189294(v=sql.105).aspx
NEW QUESTION: 4
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. はい
B. いいえ
Answer: B
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