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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. DynamoDB 테이블에 대한 DynamoDB 스트림 생성
B. Amazon S3 버킷에서 교차 리전 복제 활성화
C. RDS PostgreSQL 데이터베이스에서 다중 AZ 사용
D. 응용 프로그램이 배포 된 각 가용 영역에 대한 Lambda 함수를 만듭니다.
Answer: C

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. データを手動で入力します
D
C. データセット
D. CSVに変換
Answer: C
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