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NEW QUESTION: 1
A DevOps Engineer manages a web application that runs on Amazon EC2 instances behind an Application Load Balancer (ALB). The instances run in an EC2 Auto Scaling group across multiple Availability Zones.
The Engineer needs to implement a deployment strategy that:
Launches a second fleet of instances with the same capacity as the original fleet.
Maintains the original fleet unchanged while the second fleet is launched.
Transitions traffic to the second fleet when the second fleet is fully deployed.
Terminates the original fleet automatically 1 hour after transition.
Which solution will satisfy these requirements?
A. Use two AWS Elastic Beanstalk environments to perform a blue/green deployment from the original environment to the new one. Create an application version lifecycle policy to terminate the original environment in 1 hour.
B. Use an AWS CloudFormation template with a retention policy for the ALB set to 1 hour. Update the Amazon Route 53 record to reflect the new ALB.
C. Use AWS CodeDeploy with a deployment group configured with a blue/green deployment configuration. Select the option Terminate the original instances in the deployment group with a waiting period of 1 hour.
D. Use AWS Elastic Beanstalk with the configuration set to Immutable. Create an .ebextension using the Resources key that sets the deletion policy of the ALB to 1 hour, and deploy the application.
Answer: A

NEW QUESTION: 2
従業員は本番環境でEC2インスタンスを終了し続けます。これが起こらないようにする最善の方法は、インスタンスの終了に対する追加の防御層を追加することです。従業員が本番インスタンスを終了しないようにするための最良の方法は何ですか？以下のオプションから正しい答えを2つ選択してください選択してください：
A. 本番を識別するタグでインスタンスにタグを付け、従業員グループを変更して、開始と停止、および再起動のAPI呼び出しのみを許可し、インスタンスの終了呼び出しは許可しないようにします。
B. 実稼働を識別するタグでインスタンスをタグ付けし、実稼働タグを使用してインスタンスへの終了API呼び出しを明示的に拒否して、リソースレベルの権限を従業員ユーザーに追加します。 <
C. EC2インスタンスを削除する前にMFAを要求するようにユーザーのIAMポリシーを変更します
D. EC2インスタンスを削除する前にMFAを要求するようにユーザーのIAMポリシーを変更し、従業員へのMFAアクセスを無効にします
Answer: A,B
Explanation:
Explanation
Tags enable you to categorize your AWS resources in different ways, for example, by purpose, owner, or environment. This is useful when you have many resources of the same type - you can quickly identify a specific resource based on the tags you've assigned to it. Each tag consists of a key and an optional value, both of which you define Options C&D are incorrect because it will not ensure that the employee cannot terminate the instance.
For more information on tagging answer resources please refer to the below URL:
http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/Usins_Tags.htmll
The correct answers are: Tag the instance with a production-identifying tag and add resource-level permissions to the employe user with an explicit deny on the terminate API call to instances with the production tag.. Tag the instance with a production-identifying tag and modify the employees group to allow only start stop, and reboot API calls and not the terminate instance Submit your Feedback/Queries to our Experts

NEW QUESTION: 3
Azure Container Instanceにモデルをデプロイします。
モデルAPIを呼び出すには、Azure Machine Learning SDKを使用する必要があります。
ネイティブSDKクラスおよびメソッドを使用して、デプロイされたモデルを呼び出す必要があります。
コマンドをどのように完了する必要がありますか？回答するには、回答領域で適切なオプションを選択します。
注：それぞれの正しい選択は1ポイントの価値があります。

Answer:
Explanation:

Explanation:
Box 1: from azureml.core.webservice import Webservice
The following code shows how to use the SDK to update the model, environment, and entry script for a web service to Azure Container Instances:
from azureml.core import Environment
from azureml.core.webservice import Webservice
from azureml.core.model import Model, InferenceConfig
Box 2: predictions = service.run(input_json)
Example: The following code demonstrates sending data to the service:
import json
test_sample = json.dumps({'data': [
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
[10, 9, 8, 7, 6, 5, 4, 3, 2, 1]
]})
test_sample = bytes(test_sample, encoding='utf8')
prediction = service.run(input_data=test_sample)
print(prediction)
Reference:
https://docs.microsoft.com/bs-latn-ba/azure/machine-learning/how-to-deploy-azure-container-instance
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-deployment