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NEW QUESTION: 1
-- Exhibit-

-- Exhibit -Refer to the exhibit.
An HTTP monitor always marks the nodes in the pool as down. The monitor's definition and the HTTP headers from the monitor request and response are provided.
What is the issue?
A. The monitor timeout is too short.
B. The monitor is NOT configured to follow the redirect.
C. The response is compressed.
D. The send string is incorrect.
Answer: D

NEW QUESTION: 2
A technician documents the findings of illegal material that was found on a workstation. Which of the following should the technician do NEXT when storing the workstation?
A. Adhere to the chain of custody
B. Destroy the hard drive
C. Reimage the workstation and deploy
D. Set all users to administrators
Answer: A
Explanation:
http://www.computer-forensics.net/FAQs/what-is-a-chain-of-custody.html

NEW QUESTION: 3
온 - 프레미스 워크로드는 Apache 인스턴스와 MYSQL 데이터베이스가 있는 단일 서버로 구성됩니다. 솔루션 설계자는 여러 가용 영역을 사용하여 온 프레미스 데이터베이스를 Amazon RDS의 MYSQL으로 마이그레이션 할 계획입니다.
남은 작업 부하의 가용성을 높이려면 어떤 솔루션이 필요합니까?
A. 최소 2 개의 서버가 있는 가용 영역에서 자동 확장 그룹의 작업 부하를 제공합니다. Route 53 DNS 간단한 라우팅 정책을 사용하여 트래픽을 정상적인 서버로 보내십시오.
B. 두 개의 서로 다른 지역에 최소 두 개의 EC2 인스턴스를 제공합니다. Application Load Balancer를 사용하여 인스턴스간에 트래픽을 유도하십시오.
C. 최소 두 개의 서버가 있는 Auto Scaling 그룹에서 작업 부하를 제공합니다. Amazon Route 53 DNS 가중 라우팅 정책을 사용하여 트래픽을 정상적인 서버로 보내십시오.
D. 최소 2 개의 Amazon EC2 인스턴스가 있는 가용 영역에서 자동 확장 그룹의 작업 부하를 제공하십시오. 자동 확장 그룹 앞에서 응용 프로그램로드 밸런서 사용
Answer: A

NEW QUESTION: 4
You have a multi-class image classification deep learning model that uses a set of labeled photographs. You create the following code to select hyperparameter values when training the model.

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:
Box 1: Yes
Hyperparameters are adjustable parameters you choose to train a model that govern the training process itself. Azure Machine Learning allows you to automate hyperparameter exploration in an efficient manner, saving you significant time and resources. You specify the range of hyperparameter values and a maximum number of training runs. The system then automatically launches multiple simultaneous runs with different parameter configurations and finds the configuration that results in the best performance, measured by the metric you choose. Poorly performing training runs are automatically early terminated, reducing wastage of compute resources. These resources are instead used to explore other hyperparameter configurations.
Box 2: Yes
uniform(low, high) - Returns a value uniformly distributed between low and high Box 3: No Bayesian sampling does not currently support any early termination policy.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters