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NEW QUESTION: 1
現在、単一のロードバランサーの背後にある単一のAZのマイクロインスタンスタイプのコレクションで実行されているWebアプリケーションがあります。 2から64インスタンスまでスケーリングするように構成されたAuto Scalingグループがあります。
CloudWatchメトリックスを確認すると、スケーリンググループが64個のマイクロインスタンスを実行していることがあります。 Webアプリケーションは、DyanamoDBで構成されたバックエンドの読み取りと書き込みを行い、800の書き込み容量ユニットと800の読み取り容量ユニットで構成されています。あなたの顧客は、あなたがあなたのウェブサイトを見るとき、彼らがロード時間を経験していると不満を言っています。 DynamoDB CloudWatchメトリックスを調査しました。プロビジョニングされた読み取りおよび書き込み容量ユニットの下にあり、スロットルはありません。
ロード時間を改善し、高可用性の原則を確保するために、サービスをどのようにスケーリングしますか？
A. Auto Scalingグループ構成を変更して、複数のAZを含める
B. 2番目のロードバランサーをAuto Scalingグループに追加して、1秒あたりの受信接続をより多くサポートできるようにします
C. Auto Scalingグループ構成を変更して複数のAZを含め、3つのAZからDynamoDBを呼び出す必要があるため、DynamoDBテーブルの読み取りキャパシティーユニットの数を3倍に増やします。
D. Auto Scalingグループ構成を変更して、より大きなインスタンスを使用し、1つではなく複数のAZを含める
Answer: D

NEW QUESTION: 2
In Cascade Profiler, which of the following is available as a Dashboard content block? (Select 3)
A. Top URLs
B. Current Events
C. Top Hosts
D. Watched Interfaces
Answer: B,C,D

NEW QUESTION: 3
You are performing feature scaling by using the scikit-learn Python library for x.1 x2, and x3 features.
Original and scaled data is shown in the following image.

Use the drop-down menus to select the answer choice that answers each question based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation

Box 1: StandardScaler
The StandardScaler assumes your data is normally distributed within each feature and will scale them such that the distribution is now centred around 0, with a standard deviation of 1.
Example:
All features are now on the same scale relative to one another.
Box 2: Min Max Scaler
Notice that the skewness of the distribution is maintained but the 3 distributions are brought into the same scale so that they overlap.
Box 3: Normalizer
References:
http://benalexkeen.com/feature-scaling-with-scikit-learn/