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NEW QUESTION: 1
It has come to your notice that there is little or no reuse of SOA Services in production. After some investigation, you have been made aware that project development teams are struggling with Service interoperability challenges.
What course of action do you take to address these challenges?
A. A single service engineering team is employed to build all Services which ensure implementation consistency.
B. A single repository is employed to ensure Services are consistently discoverable.
C. A single registry is employed to ensure Services are consistently published.
D. A single technology (for example, Java) is employed to build all Services which ensure standards consistency.
E. Design-time governance quality points are employed to ensure that Services following agreed enterprise standards and guidelines.
Answer: C
Explanation:
Explanation/Reference:
Explanation:
NEW QUESTION: 2
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. CSVに変換
B. データを手動で入力します
D
C. データセット
D. データのインポート
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
NEW QUESTION: 3
DRAG DROP
Answer:
Explanation: