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NEW QUESTION: 1
A. Amazon FPS
B. Amazon SWF
C. Amazon SES
D. Amazon SNS
Answer: B
Explanation:
Amazon Simple Workflow (Amazon SWF) is a task coordination and state management service for cloud applications. With Amazon SWF, you can stop writing complex glue-code and state machinery and invest more in the business logic that makes your applications unique.
Reference: http://aws.amazon.com/swf/
NEW QUESTION: 2
An enterprise company's data science team wants to provide a safe, cost-effective way to provide easy access to Amazon SageMaker. The data scientists have limited AWS knowledge and need to be able to launch a Jupyter notebook instance. The notebook instance needs to have a preconfigured AWS KMS key to encrypt data at rest on the machine learning storage volume without exposing the complex setup requirements.
Which approach will allow the company to set up a self-service mechanism for the data scientists to launch Jupyter notebooks in its AWS accounts with the LEAST amount of operational overhead?
A. Create an AWS CloudFormation template to launch a Jupyter notebook instance using the AWS::SaqeMaker::Notebooklnstance resource type with a preconfigured KMS key. Add a user-friendly name to the CloudFormation template. Display the URL to the notebook using the Outputs section. Distribute the CloudFormation template to the data scientists using a shared Amazon S3 bucket.
B. Create a serverless front end using a static Amazon S3 website to allow the data scientists to request a Jupyter notebook instance by filling out a form. Use Amazon API Gateway to receive requests from the S3 website and trigger a central AWS Lambda function to make an API call to Amazon SageMaker that launch a notebook instance with a preconfigured KMS key for the data scientists. Then call back to the front-end website to display the URL to the notebook instance.
C. Create an AWS CLI script that the data scientists can run locally. Provide step-by-step instructions about the parameters to be provided while executing the AWS CLI script to launch a Jupyter notebook with a preconfigured KMS key. Distribute the CLI script to the data scientists using a shared Amazon S3 bucket.
D. Create an AWS CloudFormation template to launch a Jupyter notebook instance using the AWS::SageMaker::Notebooklnstance resource type with a preconfigured KMS key. Simplify the parameter names, such as the instance size, by mapping them to Small, Large, and X-Large using the Mappings section in CloudFormation. Display the URL to the notebook using the Outputs section, then upload the template into an AWS Service Catalog product in the data scientist's portfolio, and share it with the data scientist's IAM role.
Answer: D
Explanation:
https://aws.amazon.com/blogs/mt/enable-self-service-secured-data-science-using-amazon-sagemaker-notebooks-and-aws-service-catalog/
NEW QUESTION: 3
どのUnity機能がストレージ使用率を向上させることでOracle OLTP環境にメリットをもたらしますか
A. SPキャッシュサイズの増加
B. 自動ストレージ階層化
C. VVOLS
D. 4ブロックフォーマット
Answer: B
Explanation:
説明
UnityVSAは、ブロックデータとファイルデータの両方に対して、仮想プールの完全自動ストレージ階層化（FAST VP）機能をサポートしています。
FAST VPは、ストレージ階層間およびストレージ階層内でデータを自動的に移動することによって、ストレージ使用率を最適化します。
参照先：https://www.emc.com/collateral/TechnicalDocument/docu69316.pdf（11ページ）
NEW QUESTION: 4
Solution monitoring three monitoring sections within the SAP solution manager: (Choose three)
A. Service level management.
B. Business Process monitoring
C. System monitoring central system
Answer: A,B,C
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