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NEW QUESTION: 1
Oracle Cloud Infrastructure Container Engine for KubernetesにPythonアプリケーションをデプロイしました。ただし、テスト中にバグを見つけて修正し、新しいDockerイメージを作成しました。この新しいイメージが機能しない場合は、前のバージョンにロールバックできることを確認する必要があります。
kubectlを使用して、どのデプロイ戦略を選択する必要がありますか？
A. Rolling Update
B. A/B Testing
C. Blue/Green Deployment
D. Canary Deployment
Answer: C
Explanation:
Explanation
Using Blue-Green Deployment to Reduce Downtime and Risk:
>Blue-green deployment is a technique that reduces downtime and risk by running two identical production environments called Blue and Green. At any time, only one of the environments is live, with the live environment serving all production traffic. For this example, Blue is currently live and Green is idle.
This technique can eliminate downtime due to app deployment. In addition, blue-green deployment reduces risk: if something unexpected happens with your new version on Green, you can immediately roll back to the last version by switching back to Blue.

>Canary deployments are a pattern for rolling out releases to a subset of users or servers. The idea is to first deploy the change to a small subset of servers, test it, and then roll the change out to the rest of the servers.
The canary deployment serves as an early warning indicator with less impact on downtime: if the canary deployment fails, the rest of the servers aren't impacted.
>A/B testing is a way to compare two versions of a single variable, typically by testing a subject's response to variant A against variant B, and determining which of the two variants is more effective
>Rolling update offers a way to deploy the new version of your application gradually across your cluster.
References:
https://docs.cloudfoundry.org/devguide/deploy-apps/blue-green.html

NEW QUESTION: 2
AWS Well-Architectedフレームワークの柱は次のうちどれですか？ （2つ選択）
A. セキュリティ
B. パフォーマンス効率
C. 高可用性
D. 複数のアベイラビリティーゾーン
E. 暗号化の使用
Answer: A,B

NEW QUESTION: 3
Sie müssen die erforderlichen Informationen für die Computer der Verkaufsabteilung erfassen, um die technischen Anforderungen zu erfüllen.
Welchen Windows PowerShell-Befehl sollten Sie zuerst ausführen?
A. Import-AutoPilotCSV
B. Install-Script Get-WindowsAutoPilotInfo
C. Get-WindowsAutoPilotInfo
D. Installationsmodul WindowsAutoPilotIntune
Answer: D
Explanation:
Explanation
References:
https://docs.microsoft.com/en-us/windows/deployment/windows-autopilot/existing-devices

NEW QUESTION: 4
한 회사에는 각각 크기가 약 20 바이트 (예보 당 20 기가 바이트) 인 10 억 개의 고유 위치의 출력 해상도로 15 분마다 업데이트 되는 일기 예보를 생성하는 애플리케이션이 있습니다. 매시간 예측 데이터는 전 세계적으로 약 5 백만 번 (초당 1,400 개 요청) 액세스되며 기상 이벤트 중에는 최대 10 배 더 많이 액세스됩니다. 업데이트 할 때마다 예측 데이터를 덮어 씁니다. 현재 일기 예보 애플리케이션의 사용자는 쿼리에 대한 응답이 각 요청에 대해 2 초 이내에 반환 될 것으로 기대합니다.
요구되는 요청 속도와 응답 시간을 충족하는 디자인은 무엇입니까?
A. 예측 위치를 Amazon S3에 개별 객체로 저장합니다. Auto Scaling EC2 인스턴스 집합의 Elastic Load Balancing 그룹을 대상으로하는 Amazon CloudFront 배포를 생성하여 S3 객체의 오리진을 쿼리합니다. CloudFront 배포에서 캐시 제어 제한 시간을 15 분으로 설정합니다.
B. Amazon ES 클러스터에 예측 위치를 저장합니다. 오리진으로 쿼리에 응답하는 AWS Lambda 함수와 함께 API Gateway 엔드 포인트를 대상으로하는 Amazon CloudFront 배포를 사용합니다. 15 분 동안 엣지 위치에서 로컬로 데이터를 캐시하는 Amazon Lambda @ Edge 함수를 생성합니다.
C. Amazon EFS 볼륨에 예측 위치를 저장합니다. Amazon EFS 볼륨을 탑재 한 Amazon EC2 인스턴스의 Auto Scaling 집합의 Elastic Load Balancing 그룹을 대상으로하는 Amazon CloudFront 배포를 생성합니다. CloudFront 배포에서 15 분 동안 설정된 캐시 제어 제한 시간을 설정합니다.
D. Amazon ES 클러스터에 예측 위치를 저장합니다. Amazon API Gateway 엔드 포인트를 대상으로하는 Amazon CloudFront 배포를 오리진으로 쿼리에 응답하는 AWS Lambda 함수와 함께 사용합니다. 캐시 제어 제한 시간을 15 분으로 설정하여 API Gateway 단계에서 API 캐싱을 활성화 합니다.
Answer: B
Explanation:
Explanation
https://aws.amazon.com/blogs/networking-and-content-delivery/lambdaedge-design-best-practices/