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NEW QUESTION: 1
「XSLTスタイルシート」を使用して、次の「XMLドキュメント」のXSLT変換の出力結果を正しく説明しているものを次のうちどれを選択します。 XSLTプロセッサは、変換結果をドキュメントとして出力できることに注意してください。

A. 0
B. 1
C. 2
D. 012
E. 3
F. 01
Answer: A

NEW QUESTION: 2
企業のアプリケーションは、Application Load Balancer（ALB）の背後にあるAmazon EC2インスタンスで実行されます。インスタンスは、複数のアベイラビリティーゾーンにまたがるAmazon EC2 Auto Scalingグループで実行されます。毎月1日の真夜中、アプリケーションは、月末の財務計算が非常に遅くなります。バッチ実行これにより、EC2インスタンスのCPU使用率がすぐに100％にピークになります。アプリケーションを混乱させるものアプリケーションがワークロードを処理し、ダウンタイムを回避できるようにするためにソリューションアーキテクトが推奨すべきことは何ですか。
A. 月次スケジュールに基づいて、EC2 Auto Scalingのスケジュールされたスケーリングポリシーを構成します。
B. CPU使用率に基づいてEC2 Auto Scalingシンプルスケーリングポリシーを構成する
C. EC2インスタンスからワークロードの一部を削除するようにAmazon ElastiCacheを構成します
D. ALBの前にAmazon CloudFrontディストリビューションを設定します
Answer: A
Explanation:
Scheduled Scaling for Amazon EC2 Auto Scaling
Scheduled scaling allows you to set your own scaling schedule. For example, let's say that every week the traffic to your web application starts to increase on Wednesday, remains high on Thursday, and starts to decrease on Friday. You can plan your scaling actions based on the predictable traffic patterns of your web application. Scaling actions are performed automatically as a function of time and date.
https://docs.aws.amazon.com/autoscaling/ec2/userguide/schedule_time.html

NEW QUESTION: 3
You need to create a training dataset and validation dataset from an existing dataset.
Which module in the Azure Machine Learning designer should you use?
A. Add Rows
B. Split Data
C. Join Data
D. Select Columns in Dataset
Answer: B
Explanation:
A common way of evaluating a model is to divide the data into a training and test set by using Split Data, and then validate the model on the training data.
Use the Split Data module to divide a dataset into two distinct sets. The studio currently supports training/validation data splits Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-configure-cross-validation-data-splits2