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NEW QUESTION: 1
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::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.
B. 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.
C. 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.
D. 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.
Answer: A
Explanation:
Explanation
https://aws.amazon.com/blogs/mt/enable-self-service-secured-data-science-using-amazon-sagemaker-notebooks-
NEW QUESTION: 2
ある会社が、ソフトウェアテストに使用されるいくつかのラボ環境の移行を計画しています。さまざまなカスタムツールを使用して、各ラボのテスト実行を管理します。ラボでは、ソフトウェアのテスト実行に不変のインフラストラクチャを使用し、結果は可用性の高いSQLデータベースクラスターに保存されます。カスタムツールを完全に書き直すことは移行プロジェクトの範囲外ですが、同社は移行中のワークロードを最適化したいと考えています。
どのアプリケーション移行戦略がこの要件を満たしていますか？
A. 再ホスト
B. 引退
https://aws.amazon.com/blogs/enterprise-strategy/6-strategies-for-migrating-applications-to-the-cloud/
C. リファクタリング/リアーキテクト
D. プラットフォームの再構築
Answer: D
NEW QUESTION: 3
A data center has several business partners who want to have their compute resources installed. The data center uses one VLAN to support vendor equipment and requires limited visibility and connectivity between vendor servers. Which segmentation concept satisfies these requirements?
A. IP NAT
B. private VLANs
C. protected VLANs
D. LAN-to-LAN VPN
Answer: B
