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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 1AM role.
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::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.
Answer: D

NEW QUESTION: 2
湿度とともに、温度はデータセンターにとって最適な操作と機器の保護のために重要です。
ASHRAEによって設定された最適な温度範囲は次のうちどれですか？
A. 華氏44.6度から60.8度（摂氏7度から16度）
B. 華氏64.4度から80.6度（摂氏18度から27度）
C. 華氏51.8から66.2度（摂氏11から19度）
D. 華氏69.8度から86.0度（摂氏21度から30度）
Answer: B
Explanation:
説明
American Society of Heating、Refrigeration、and Conditioning Engineers（ASHRAE）は、
データセンターの最適な温度範囲として、華氏64.4〜80.6度（摂氏18〜27度）。
これらのオプションはいずれもASHRAEからの推奨ではありません。

NEW QUESTION: 3
AzureIoTハブからデータをストリーミングするための異常検出ソリューションを設計しています。ソリューションは、次の要件を満たす必要があります。
*出力をAzureSynapseに送信します。
*時系列データの急上昇と急降下を特定します。
*開発と構成の労力を最小限に抑える
ソリューションにどちらを含める必要がありますか？
A. Azure SQL Database
B. Azure Databricks
C. Azure Stream Analytics
Answer: C
Explanation:
You can identify anomalies by routing data via IoT Hub to a built-in ML model in Azure Stream Analytics.
Reference:
https://docs.microsoft.com/en-us/learn/modules/data-anomaly-detection-using-azure-iot-hub/

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