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NEW QUESTION: 1
プロジェクトでは、発注書の価格条件は特定のユーザーにのみ表示される必要があります。何を構成して割り当てる必要がありますか？
A. ユーザーパラメータEFB
B. ユーザーパラメータEVO
C. トランザクションのフィールド選択キー
D. アクティビティカテゴリのフィールド選択キー
Answer: A

NEW QUESTION: 2
あなたはホテル予約のウェブサイト会社で働いているデータサイエンティストです。 Azure Machine Learningサービスを使用して、不正なトランザクションを識別するモデルをトレーニングします。
Azure Machine Learning SDKのModel.deployメソッドを使用して、モデルをAzure Machine LearningリアルタイムWebサービスとしてデプロイする必要があります。デプロイされたWebサービスは、トランザクションデータ入力に基づいて不正のリアルタイム予測を返す必要があります。
モデルのデプロイに使用されるInferenceConfigクラスのentry_scriptパラメーターとして指定されるスクリプトを作成する必要があります。
入力スクリプトは何をすべきですか？
A. モデルを読み込み、それを使用して入力データからラベルを予測します。
B. 適切なタグとプロパティを使用してモデルを登録します。
C. 推論の計算に必要なコアの数とメモリの量を指定します。
D. Webサービスコンピューティング用のConda環境を作成し、必要なPythonパッケージをインストールします。
E. Webサービスがデプロイされている推論クラスターでノードを起動します。
Answer: A
Explanation:
Explanation
The entry script receives data submitted to a deployed web service and passes it to the model. It then takes the response returned by the model and returns that to the client. The script is specific to your model. It must understand the data that the model expects and returns.
The two things you need to accomplish in your entry script are:
Loading your model (using a function called init())
Running your model on input data (using a function called run())
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-and-where

NEW QUESTION: 3
On analyzing your time series data you suspect that the data represented as y1, y2, y3, ... , yn-1, yn may have a trend component that is quadratic in nature. Which pattern of data will indicate that the trend in the time series data is quadratic in nature?
A. (y4-y2) - (y3-y1) = .........= (yn-yn-2)-(yn-1-yn-3)
B. (y2-y1) = (y3-y2) = ....... = (yn-yn-1)
C. (y3-y2) - (y2-y1) = .........= (yn-yn-1)-(yn-1-yn-2)
D. ((y2-y1) /y1 ) * 100% = .......((yn-yn-1)/yn-1) * 100%
Answer: C