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NEW QUESTION: 1
近くの気象観測所からデータを収集します。次のデータを含むweather_dfという名前のパンダデータフレームがあります。
データは正午と深夜の12時間ごとに収集されます。
自動機械学習を使用して、今後7日間の気温を予測する時系列モデルを作成することを計画しています。トレーニングの最初のラウンドでは、最大50の異なるモデルをトレーニングする必要があります。
Azure Machine Learning SDKを使用して自動機械学習実験を実行し、これらのモデルをトレーニングする必要があります。
自動機械学習の実行を構成する必要があります。
AutoMLConfig定義をどのように完了する必要がありますか？回答するには、回答領域で適切なオプションを選択します。
注：正しい選択はそれぞれ1ポイントの価値があります。
Answer:
Explanation:
Explanation
Box 1: forcasting
Task: The type of task to run. Values can be 'classification', 'regression', or 'forecasting' depending on the type of automated ML problem to solve.
Box 2: temperature
The training data to be used within the experiment. It should contain both training features and a label column (optionally a sample weights column).
Box 3: observation_time
time_column_name: The name of the time column. This parameter is required when forecasting to specify the datetime column in the input data used for building the time series and inferring its frequency. This setting is being deprecated. Please use forecasting_parameters instead.
Box 4: 7
"predicts temperature over the next seven days"
max_horizon: The desired maximum forecast horizon in units of time-series frequency. The default value is 1.
Units are based on the time interval of your training data, e.g., monthly, weekly that the forecaster should predict out. When task type is forecasting, this parameter is required.
Box 5: 50
"For the initial round of training, you want to train a maximum of 50 different models." Iterations: The total number of different algorithm and parameter combinations to test during an automated ML experiment.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.auto
NEW QUESTION: 2
AWS에서 새로운 애플리케이션을 개발하고 실행해야 하며 Elastic Beanstalk 및 CloudFormation이 광범위한 AWS 리소스를위한 배포 메커니즘으로 도움이 될 수 있음을 알고 있습니다. 다음 중 Elastic Beanstalk와 CloudFormation의 차이점을 가장 잘 설명하는 문장은 무엇입니까?
A. Elastic Beanstalk은 CloudFormation보다 응용 프로그램 배포가 빠릅니다.
B. CloudFormation은 Elastic Beanstalk보다 응용 프로그램을 배포 할 때 더 빠릅니다.
C. CloudFormation은 Elastic Beanstalk보다 훨씬 강력합니다. 사용자 정의 리소스를 실제로 디자인하고 스크립트 할 수 있기 때문입니다.
D. Elastic Beanstalk은 탄성 부하 분산을 사용하고 CloudFormation은 사용하지 않습니다.
Answer: C
Explanation:
설명:
이러한 서비스는 서로를 보완하도록 설계되었습니다. AWS Elastic Beanstalk은 클라우드에서 애플리케이션을 쉽게 개발하고 실행할 수있는 환경을 제공합니다. 이 도구는 개발자 도구와 통합되어 있어 응용 프로그램의 수명주기를 관리 할 수있는 원 스톱 환경을 제공합니다. AWS CloudFormation은 광범위한 AWS 리소스를 위한 편리한 배포 메커니즘입니다. 기존의 엔터프라이즈 응용 프로그램, 레거시 응용 프로그램, 다양한 AWS 자원을 사용하여 작성된 응용 프로그램 및 컨테이너 기반 솔루션 (AWS Elastic Beanstalk을 사용하여 작성된 응용 프로그램 포함)과 같은 다양한 유형의 응용 프로그램의 인프라 요구를 지원합니다.
AWS CloudFormation에는 두 가지 새로운 개념이 도입되었습니다. 템플릿, 애플리케이션 및 스택을 실행하기 위해 배포해야하는 모든 AWS 리소스를 설명하는 JSON 형식의 텍스트 기반 파일, 단일 파일로 생성 및 관리되는 AWS 리소스 집합 AWS CloudFormation이 템플릿을 인스턴스화 할 때의 단위입니다.
참조 : http://aws.amazon.com/cloudformation/faqs/
NEW QUESTION: 3
Answer:
Explanation:
Explanation
+ Address registration - allows neighboring Cisco devices to exchange the management ip addresses
+ Global addressing - Enables Frame Relay to identify interfaces in same manner as LAN
+ Multicasting - Provides most efficient transmission of routing protocol messages and support address resolution
+ Simple flow control - Supports devices that are unable to use congestion notification
+ Virtual circuit - Prevents data from being transmitted in black hole