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NEW QUESTION: 1
A. paramValues
B. headerValues
C. cookie
D. facesContext
Answer: D
NEW QUESTION: 2
회사에는 매일 1TB의 상태 경고를 총체적으로 생성하는 수천 개의 에지 장치가 있습니다. 각 경고의 크기는 약 2KB입니다. 솔루션 설계자는 향후 분석을 위해 경고를 수집하고 저장하는 솔루션을 구현해야 합니다.
회사는 고 가용성 솔루션을 원하지만 회사는 비용을 최소화 해야 하고 추가 인프라를 관리하고 싶지 않습니다. 또한 회사는 즉각적인 분석을 위해 14 일 동안의 데이터를 보관하고 14 일이 지난 모든 데이터를 보관하기를 원합니다. 이러한 요구 사항을 충족하는 효율적인 솔루션?
A. 두 가용 영역에서 Amazon EC2 인스턴스를 시작하고 Elastic Load Balancer 뒤에 배치하여 경고를 수집합니다. Amazon S3 버킷에 경고를 저장할 EC2 인스턴스에 스크립트 생성 데이터를 전환하도록 S3 수명주기 구성 설정 14 일 후 Amazon S3 Glacier로
B. Amazon Simple Queue Service (Amazon SQS) 표준 대기열을 생성하여 알림을 수집하고 메시지 보존 기간을 14 일로 설정합니다. SQS 대기열을 폴링하도록 소비자를 구성하여 메시지 수명을 확인하고 필요한 경우 메시지 데이터를 분석합니다. 메시지가 14 일이 지난 경우 소비자는 메시지를 Amazon S3 버킷에 복사하고 SQS 대기열에서 메시지를 삭제해야 합니다.
C. Amazon Kinesis Data Firehose 전송 스트림을 생성하여 경고를 수집합니다. Amazon Elasticsearch Service (Amazon ES) 클러스터에 경고를 전송하도록 Kinesis Data Firehose 스트림을 구성합니다. 매일 수동 스냅 샷을 생성하고 삭제하도록 Amazon ES 클러스터를 설정합니다. 다음보다 오래된 클러스터의 데이터
14 일
D. Amazon Kinesis Data Firehose 전송 스트림을 생성하여 알림을 수집합니다. Amazon S3 버킷에 알림을 전송하도록 Kinesis Data Firehose 스트림을 구성합니다. 14 일 후에 데이터를 Amazon S3 Glacier로 전환하도록 S3 LifecycJe 구성을 설정합니다.
Answer: B
NEW QUESTION: 3
Which three items represent features of VM-FEX with Cisco Nexus 1000V? (Choose three.)
A. High performance
B. Software packet switching
C. Feature rich
D. Hardware packet switching
E. VN-Tag (802.1 Qbh)
F. 802.1 Q
Answer: A,C,D
NEW QUESTION: 4
You are using the Azure Machine Learning Service to automate hyperparameter exploration of your neural network classification model.
You must define the hyperparameter space to automatically tune hyperparameters using random sampling according to following requirements:
* The learning rate must be selected from a normal distribution with a mean value of 10 and a standard deviation of 3.
* Batch size must be 16, 32 and 64.
* Keep probability must be a value selected from a uniform distribution between the range of 0.05 and
0.1.
You need to use the param_sampling method of the Python API for the Azure Machine Learning Service.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
In random sampling, hyperparameter values are randomly selected from the defined search space. Random sampling allows the search space to include both discrete and continuous hyperparameters.
Example:
from azureml.train.hyperdrive import RandomParameterSampling
param_sampling = RandomParameterSampling( {
"learning_rate": normal(10, 3),
"keep_probability": uniform(0.05, 0.1),
"batch_size": choice(16, 32, 64)
}
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-tune-hyperparameters
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