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NEW QUESTION: 1
Which parameter do you change on a switch to increase the probability that it becomes the root bridge in STP.
A. Decrease the Cost value
B. Decrease the Priority value
C. Increase the Cost value
D. Increase the Priority value
Answer: B

NEW QUESTION: 2
A user has created a photo editing software and hosted it on EC2. The software accepts requests from the user about the photo format and resolution and sends a message to S3 to enhance the picture accordingly. Which of the below mentioned AWS services will help make a scalable software with the AWS infrastructure in this scenario?
A. AWS Glacier
B. AWS Simple Notification Service
C. AWS Simple Queue Service
D. AWS Elastic Transcoder
Answer: C
Explanation:
Explanation
Amazon Simple Queue Service (SQS. is a fast, reliable, scalable, and fully managed message queuing service.
SQS provides a simple and cost-effective way to decouple the components of an application. The user can configure SQS, which will decouple the call between the EC2 application and S3. Thus, the application does not keep waiting for S3 to provide the data.

NEW QUESTION: 3
머신 러닝 전문가가 애플리케이션을 위한 맞춤형 비디오 권장 모델을 개발 중입니다.이 모델을 훈련시키는 데 사용되는 데이터 세트는 수백만 개의 데이터 포인트로 매우 커서 Amazon S3 버킷에 호스팅됩니다. 전문가는이 모든 데이터를 Amazon에로드하지 않기를 원합니다 이동하는 데 몇 시간이 걸리고 노트북 인스턴스에 연결된 5GB Amazon EBS 볼륨을 초과하므로 SageMaker 노트북 인스턴스.
전문가가 모델을 교육하기 위해 모든 데이터를 사용할 수 있는 방법은 무엇입니까?
A. 더 작은 데이터 하위 세트를 SageMaker 노트북에 로드하고 로컬로 학습합니다. 훈련 코드가 실행 중이고 모델 매개 변수가 합리적으로 보이는지 확인하십시오. AWS Deep Learning AMI로 Amazon EC2 인스턴스를 시작하고 S3 버킷을 연결하여 전체 데이터 세트를 교육하십시오.
B. 작은 데이터 하위 집합을 SageMaker 노트북에 로드하고 로컬로 학습합니다. 훈련 코드가 실행 중이고 모델 매개 변수가 합리적으로 보이는지 확인하십시오. 파이프 입력 모드를 사용하여 S3 버킷의 전체 데이터 세트를 사용하여 SageMaker 교육 작업을 시작하십시오.
C. AWS Deep Learning AMI로 Amazon EC2 인스턴스를 시작하고 S3 버킷을 인스턴스에 연결하십시오. 소량의 데이터를 학습하여 훈련 코드 및 하이퍼 파라미터를 확인하십시오. Amazon SageMaker로 돌아가 전체 데이터 세트를 사용하여 교육
D. AWS Glue를 사용하여 작은 데이터 하위 집합을 사용하여 모델을 훈련시켜 데이터가 Amazon SageMaker와 호환되는지 확인합니다. 파이프 입력 모드를 사용하여 S3 버킷의 전체 데이터 세트를 사용하여 SageMaker 교육 작업을 시작하십시오.
Answer: B