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NEW QUESTION: 1
Select the three employee applications.
A. Siebel Call Center
B. Siebel Sales
C. Siebel eService
D. Siebel Partner Manager
E. Siebel Finance
Answer: A,B,D
NEW QUESTION: 2
現在、企業はソース管理にAWS CodeCommitを使用し、継続的な統合にAWS CodePipelineを使用しています。パイプラインには、アーティファクトを構築するためのビルドステージがあり、その後、Amazon S3バケットにステージングされます。
同社は、既存のプロセスにおけるさまざまな改善の機会を特定しており、ソリューションアーキテクトには次の要件が与えられています。
*機能開発をサポートする新しいパイプラインを作成する
*本番アプリケーションに影響を与えることなく機能開発をサポート
*単体テストに継続的なテストを組み込む
*開発成果物と実動成果物を分離する
*テスト済みのコードを製品コードにマージする機能をサポートします。
ソリューションアーキテクトはこれらの要件をどのように達成する必要がありますか？
A. CodeCommit機能ブランチとは別のパイプラインをトリガーします。 AWS CodeBuildを使用して単体テストを実行します。 CodeBuildを使用して、別のテストアカウントでS3バケット内のアーティファクトをステージングします。
B. CodeCommitタグとは別のパイプラインをトリガーするユニットテストの実行にJenkinsを使用します。別のテストアカウントでS3バケットを使用して成果物をステージングするためのターゲットとしてS3を使用して、パイプラインにステージを作成します。
C. CodeCommit機能ブランチとは別のパイプラインをトリガーします。ユニットテストの実行にAWS Lambdaを使用します。 AWS CodeDeployを使用して、別のテストアカウントでS3バケット内のアーティファクトをステージングします。
D. 機能開発用に別のCodeCommitリポジトリを作成し、それを使用してパイプラインをトリガーします。ユニットテストの実行にAWS Lambdaを使用します。 AWS CodeBuildを使用して、同じ本番アカウントの異なるS3バケット内のアーティファクトをステージングします。
Answer: D
Explanation:
Explanation
https://docs.aws.amazon.com/codebuild/latest/userguide/how-to-create-pipeline.html
NEW QUESTION: 3
You are designing an Azure Batch Al solution that will be used to train many different Azure Machine Learning models. The solution will perform the following:
* Image recognition
* Deep learning that uses convolutional neural networks
You need to select a compute infrastructure for each model. The solution must minimize the processing time.
What should you use for each model? To answer, drag the appropriate compute infrastructures to the correct models. Each compute infrastructure may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
References:
https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu