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NEW QUESTION: 1
머신 러닝 전문가가 Amazon SageMaker 내장 알고리즘을 사용하여 모델을 구축했으며 정확한 결과를 얻지 못합니다. 전문가는 하이퍼 파라미터 최적화를 사용하여 모델의 정확도를 높이려고 합니다. 가장 반복 가능한 방법은 무엇입니까? ?
A. 하이퍼 파라미터 튜닝 작업을 생성하고 정확도를 목표 메트릭으로 설정하십시오.
B. 각 개별 하이퍼 파라미터에 사용해야 하는 값 범위를 반복하기 위해 매개 변수 공간에서 임의의 간격을 만듭니다.
C. 다른 하이퍼 파라미터와 동시에 여러 교육 작업을 시작하십시오
D. Amazon CloudWatch Logs의 정확도를 모니터링하고 정의 된 하이퍼 파라미터 목록으로 교육 작업을 다시 시작하는 AWS Step Functions 워크 플로우 생성
Answer: D

NEW QUESTION: 2



A. 209.114.111.1
B. 192.168.10.7
C. 241.199.164.101
D. 132.163.4.103
E. 108.61.73.243
F. 204.2.134.164
Answer: B

NEW QUESTION: 3
Your customer has a large Exchange implementation running on many servers and dedicated SAN storage with 600GB disk drives. They are interested in moving to the latest version of Exchange. What would you recommend to help them reduce the Exchange footprint in the data center1?
A. They upgrade their storage to 2 TB disk drives, so that they need fewer drives to handle the required storage capacity.
B. They migrate to a virtualized environment with HP 3PAR StoreServ 7000 and use Thin Provisioning to reduce physical storage requirements
C. They migrate to a dedicated HP 3PAR StoreServ array and use large capacity drives to handle the reduced IOP workload.
D. They refresh the servers with more powerful HP ProLiant servers so that they need fewer servers to handle the workload.
Answer: B