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NEW QUESTION: 1
자동 크기 조정은 3 개의 AZ로 구성됩니다. 각 영역에는 5 개의 인스턴스가 실행 중입니다. AutoScaling이 정책 작업을 기반으로 인스턴스를 종료하려면 어떤 인스턴스가 먼저 종료됩니까?
A. AWS로드가 높지 않은 AZ에서 인스턴스를 종료합니다.
B. 시작한 첫 번째 인스턴스 종료
C. 결제 시간 근처에서 실행중인 인스턴스가있는 AZ에서 인스턴스 종료
D. 종료 할 인스턴스를 임의로 선택하십시오.
Answer: D
Explanation:
설명:
자동 확장은 종료 할 인스턴스를 선택하기 전에 먼저 그룹에서 사용하는 다른 가용 영역보다 많은 인스턴스가 있는 가용 영역을 식별합니다. 모든 가용 영역이 동일한 수의 인스턴스를 갖는 경우, 가용 영역을 식별합니다.
http://docs.aws.amazon.com/AutoScaling/latest/DeveloperGuide/us-termination-policy.html

NEW QUESTION: 2
Which of the following actions must you take in order to reach the NetCache firmware prompt ("OK prompt")?
A. enter the prompt command
B. set the environment variable, floppy-boot?, to off
C. enter the halt command
D. press Esc
Answer: C

NEW QUESTION: 3
트래픽을 전달할수 있어야 하는 3 개의 VPC가 있습니다. 이것을 어떻게 달성할수 있습니까?
두 가지 정답을 선택하십시오.
A. 각 VPC를 다른 모든 VPC에 피어링하여 전체 메시 피어링을 만듭니다.
B. VPC 피어링은 2017 년 12 월 기준으로 전 이적 피어링을 허용합니다.
C. 그들 사이에 VPN을 생성하고 그에 따라 라우팅 테이블을 조정하십시오.
D. 전 이적 피어링을 활성화하려면 AWS에 문의하십시오.
Answer: A,C
Explanation:
VPN instances can be used to create transitive peering. Full mesh peering is the only way to use peering to allow all VPCs to communicate with all other VPCs. Transitive peering is not possible.

NEW QUESTION: 4
注：この質問は、同じシナリオを提示する一連の質問の一部です。シリーズの各質問には、指定された目標を達成する可能性のある独自のソリューションが含まれています。一部の質問セットには複数の正しい解決策がある場合がありますが、他の質問セットには正しい解決策がない場合があります。
このセクションの質問に回答した後は、その質問に戻ることはできません。その結果、これらの質問はレビュー画面に表示されません。
履歴データに基づいて気象条件を予測するモデルを作成します。
データストアからデータを読み込み、処理されたデータを機械学習モデルのトレーニングスクリプトに渡すために、処理スクリプトを実行するパイプラインを作成する必要があります。
解決策：次のコードを実行します。

ソリューションは目標を達成していますか？
A. いいえ
B. はい
Answer: B
Explanation:
The two steps are present: process_step and train_step
Note:
Data used in pipeline can be produced by one step and consumed in another step by providing a PipelineData object as an output of one step and an input of one or more subsequent steps.
PipelineData objects are also used when constructing Pipelines to describe step dependencies. To specify that a step requires the output of another step as input, use a PipelineData object in the constructor of both steps.
For example, the pipeline train step depends on the process_step_output output of the pipeline process step:
from azureml.pipeline.core import Pipeline, PipelineData
from azureml.pipeline.steps import PythonScriptStep
datastore = ws.get_default_datastore()
process_step_output = PipelineData("processed_data", datastore=datastore) process_step = PythonScriptStep(script_name="process.py", arguments=["--data_for_train", process_step_output], outputs=[process_step_output], compute_target=aml_compute, source_directory=process_directory) train_step = PythonScriptStep(script_name="train.py", arguments=["--data_for_train", process_step_output], inputs=[process_step_output], compute_target=aml_compute, source_directory=train_directory) pipeline = Pipeline(workspace=ws, steps=[process_step, train_step]) Reference:
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azure-ml-py