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NEW QUESTION: 1
대기 오염의 결과를 해결하기 위해 도시의 대기 질을 모니터링하려고 합니다. 머신 러닝 전문가는 도시에서 다음 2 일 동안 오염 물질 백만 분의 1 단위로 대기 질을 예측해야 합니다. 이것은 프로토 타입이므로 작년 제공 Amazon SageMaker에서 가장 좋은 결과를 제공 할 가능성이 가장 높은 모델은 무엇입니까?
A. predictor_type의 회귀 변수가 있는 1 년 전체 데이터로 구성된 단일 시계열에 Amazon SageMaker 선형 학습자 알고리즘을 사용하십시오.
B. predictor_type 분류기를 사용하여 1 년 전체 데이터로 구성된 단일 시계열에 Amazon SageMaker 선형 학습자 알고리즘을 사용하십시오.
C. predictor_type이 회귀 형인 1 년 데이터로 구성된 단일 시계열에 Amazon SageMaker kNearest-Neighbors (kNN) 알고리즘을 사용하십시오.
D. 전체 연도 데이터로 구성된 단일 시계열에서 Amazon SageMaker 랜덤 컷 포레스트 (RCF)를 사용하십시오.
Answer: A
Explanation:
https://aws.amazon.com/blogs/machine-learning/build-a-model-to-predict-the-impact-of-weather- on-urban-air-quality-using-amazon-sagemaker/?ref=Welcome.AI
NEW QUESTION: 2
A. Option C
B. Option B
C. Option A
Answer: B
NEW QUESTION: 3
An administrator is configuring Storage I/O Control. After enabling, the administrator notices high device latency and poor storage performance.
Which two actions would likely reduce latency and improve functionality? (Choose two.)
A. Ensure that each datastore uses shared spindles
B. Set the congestion threshold to 15ms
C. Ensure that each datastore has independent spindles
D. Set the congestion threshold value to 5ms
Answer: B,C
Explanation:
Explanation/Reference:
