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NEW QUESTION: 1
Amazon S3에 15GB 객체를 업로드하지 못했습니다. 오류 메시지는 "제안 된 업로드가 허용 된 최대 객체 크기를 초과합니다."입니다. 개발자가이 기술을 사용하여 어떤 기술을 업로드 할 수 있습니까?
A. 객체 크기 제한을 늘리려면 AWS Support에 문의하십시오.
B. 객체를 다른 AWS 리전에 업로드합니다.
C. 멀티 파트 업로드 API를 사용하여 객체를 업로드합니다.
D. AWS Direct Connect 연결을 통해 객체를 업로드하십시오.
Answer: C

NEW QUESTION: 2
You have a data set that you wish to use to train a machine learning model in Oracle Analytics Cloud. The attribute you are interested in predicting has one of two text values (e.g. Yes/No or True/False).
How should you perform this operation?
A. Create a data flow that includes a step to analyze sentiment and save the model for later use.
B. Create a data flow that includes a step to train a fuzzy prediction model and save the model for later use.
C. Create a data flow that includes a step to train a clustering model and save the model for later use.
D. Create a data flow that includes a step to train a binary classifier model and save the model for later use.
Answer: D
Explanation:
Explanation/Reference:
Reference: https://docs.oracle.com/en/middleware/bi/data-visualization-desktop/bidvd/creating-train- model-data-flow.html

NEW QUESTION: 3
You administer a Microsoft Dynamics 365 environment for a company.
You need to prevent one specific employee from synchronizing data by using Microsoft Dynamics 365 for
Outlook without preventing the employee from using Microsoft Dynamics 365 tor Outlook.
What should you do?
A. Set the Web Mail Merge settings to None Selected for the individual's security role.
B. Set the Use Dynamics 365 App for Outlook to None Selected for the individual's security role.
C. Set the Dynamics 365 Address Book settings to None Selected for the individual's security role.
D. Set the Sync to Outlook settings to None Selected for the individual's security role.
Answer: A

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、記載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もあれば、正しい解決策がない場合もあります。
このセクションの質問に回答すると、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。
Azure Machine Learning Studioを使用して、データセットの機能エンジニアリングを実行しています。
値を正規化して、ビンにグループ化されたフィーチャ列を作成する必要があります。
解決策：エントロピー最小記述長（MDL）ビニングモードを適用します。
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: A
Explanation:
説明
エントロピーMDLビニングモード：この方法では、予測する列と、ビンにグループ化する列を選択する必要があります。次に、データを渡して、エントロピーを最小化するビンの数を決定しようとします。つまり、データ列がターゲット列を最適に予測できるようにするビンの数を選択します。次に、<colname> quantizedという名前の列のデータの各行に関連付けられたビン番号を返します。
参照：
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/group-data-into-bins