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NEW QUESTION: 1
Which of the following statements about OSPF neighbor state transition are true? (Select 3 answers)
A. The neighbor state transitions from "Exchange" to "Loading when an ExchangeDone event occurs.
B. The neighbor state transitions from "Loading" to "Full" when a LoadingDone event occurs.
C. The neighbor state transitions from the "Exstart" to "full" when a Negotiation Done event occurs.
D. The neighbor state transitions from the "Exchange" to "Full" when an ExchangeDone event occurs.
Answer: A,B,D
NEW QUESTION: 2
머신 러닝 전문가는 맞춤형 ResNet 모델을 Docker 컨테이너에 패키징하여 회사에서 Amazon SageMaker를 활용하여 교육 할 수 있습니다. 전문가는 Amazon EC2 P3 인스턴스를 사용하여 모델을 학습하고 있으며 NVIDIA GPU를 활용하도록 Docker 컨테이너를 올바르게 구성해야 합니다.
전문가는 무엇을 해야 합니까?
A. Amazon SageMaker CreateTrainingJob 요청 본문에서 GPU 플래그 설정
B. Docker 컨테이너를 NVIDIA-Docker와 호환되도록 빌드하십시오.
C. GPU 인스턴스에서 실행되도록 Docker 컨테이너의 파일 구조를 구성하십시오.
D. NVIDIA 드라이버를 Docker 이미지와 번들로 묶습니다.
Answer: B
Explanation:
https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-dg.pdf
If you plan to use GPU devices, make sure that your containers are nvidia-docker compatible.
Only the CUDA toolkit should be included on containers. Don't bundle NVIDIA drivers with the image.
For more information about nvidia-docker, see NVIDIA/nvidia-docker.
NEW QUESTION: 3
An ArcGIS Online user is beginning to work on a project that will involve multiple organizations. Each
organization gas its own ArcGIS Online subscription. The project requires that only the project team have
access to the data, and any team member should be able to contribute to the project.
Which sharing option should the user select?
A. Group
B. Organization
C. Organization and Group
D. Everyone
Answer: A
NEW QUESTION: 4
회사는 온 프레미스 데이터 센터에 HPC (High Performance Computing) 클러스터를 보유하고 있으며,이 시스템은 매달 1 주일 동안 수천 개의 작업을 병렬로 실행하여 페타 바이트의 이미지를 처리합니다. 이미지는 네트워크 파일 서버에 저장되며 재해 복구 사이트로 복제됩니다. 온-프레미스 데이터 센터가 용량에 도달하여 클러스터를 더 잘 활용하기 위해 한 달 동안 작업을 분산시키기 시작하여 작업 완료가 지연되었습니다.
이 회사는 솔루션 아키텍트에게 현재 5,000 개의 코어 용량과 10 페타 바이트의 데이터 용량을 넘어서 확장 할 수있는 AWS에서 비용 효율적인 솔루션을 설계하도록 요청했습니다. 솔루션에는 최소한의 관리 오버 헤드가 필요하고 현재 내구성 수준을 유지해야 합니다.
회사의 요구 사항을 충족시키는 솔루션은 무엇입니까?
A. 원시 데이터를 Amazon S3에 저장하고 관리 형 컴퓨팅 환경과 함께 AWS Batch를 사용하여 스팟 집합을 생성합니다. 작업을 AWS Batch Job Queues에 제출하여 Amazon S3에서 Amazon EBS 볼륨으로 객체를 가져와 임시 스토리지를 처리한 다음 결과를 Amazon S3에 다시 씁니다.
B. 작업의 실행 파일을 사용하여 Amazon Elastic Container Registry에 컨테이너를 만듭니다. Auto Scaling 그룹에서 스팟 집합과 함께 Amazon ECS를 사용하십시오. 원시 데이터를 Amazon EBS SC1 볼륨에 저장하고 출력을 Amazon S3에 씁니다.
C. 처리해야 할 작업을 대기열에 추가하기 위해 처리 할 작업 목록을 Amazon SQS에 제출합니다.
스팟 집합을 사용하여 대기열 깊이에 따라 자동으로 확장되는 다양한 Amazon EC2 작업자 인스턴스 클러스터를 생성합니다. Amazon EFS를 사용하여 클러스터의 모든 인스턴스에서 공유하는 모든 데이터를 저장하십시오.
D. Spark를 사용하여 Amazon S3에서 데이터를 가져 오는 온 디맨드 인스턴스와 예약 인스턴스 작업 노드의 조합으로 Amazon EMR 클러스터를 생성합니다. Amazon EMR 클러스터에서 처리해야 하는 작업 목록을 유지하려면 Amazon DynamoDB를 사용하십시오.
Answer: A