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NEW QUESTION: 1
Drag the GRE tunnel state from the left onto its corresponding scenario on the right.

Answer:
Explanation:

NEW QUESTION: 2
팀에는 Amazon S3 버킷으로 업로드되는 새로운 이젝트를 감지하는 애플리케이션이 있습니다. 업로드는 Lambda 함수를 트리거하여 객체 메타 데이터를 Amazon DynamoDB 테이블 및 RDS PostgreSQL 데이터베이스에 씁니다. 고 가용성을 보장하기 위해 팀은 어떤 조치를 취해야 합니까?
A. Amazon S3 버킷에서 교차 리전 복제 활성화
B. RDS PostgreSQL 데이터베이스에서 다중 AZ 사용
C. 응용 프로그램이 배포 된 각 가용 영역에 대한 Lambda 함수를 만듭니다.
D. DynamoDB 테이블에 대한 DynamoDB 스트림 생성
Answer: B

NEW QUESTION: 3
デザイナーを使用して新しいAzure Machine Learningパイプラインを作成しています。
パイプラインは、Webサイトで公開されているコンマ区切り値（CSV）ファイルのデータを使用してモデルをトレーニングする必要があります。このファイルのデータセットを作成していません。
最小限の管理作業で、CSVファイルからデザイナーパイプラインにデータを取り込む必要があります。
Designerのどのモジュールをパイプラインに追加する必要がありますか？
A. データのインポート
B. データセット
C. CSVに変換
D. データを手動で入力します
D
Answer: B
Explanation:
The preferred way to provide data to a pipeline is a Dataset object. The Dataset object points to data that lives in or is accessible from a datastore or at a Web URL. The Dataset class is abstract, so you will create an instance of either a FileDataset (referring to one or more files) or a TabularDataset that's created by from one or more files with delimited columns of data.
Example:
from azureml.core import Dataset
iris_tabular_dataset = Dataset.Tabular.from_delimited_files([(def_blob_store, 'train-dataset/iris.csv')]) Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-your-first-pipeline