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NEW QUESTION: 1
Joe, ein Benutzer, hat ein neues mobiles Gerät gekauft, das ein anderes Betriebssystem als sein vorheriges verwendet.
Joe möchte seine Kontakte und Termine von seinem Unternehmenskonto kopieren, ohne sie manuell einzugeben.
Welche der folgenden Möglichkeiten wäre die BESTE Möglichkeit für einen Techniker, Joe zu unterstützen?
A. Verwenden Sie NFC, um alle kompatiblen Daten von einem Gerät auf das andere zu übertragen.
B. Stellen Sie eine Bluetooth-Verbindung zwischen Joes Geräten her und kopieren Sie seine Kontakte und seinen Kalender.
C. Melden Sie sich beim gleichen E-Mail-Anbieter auf dem neuen Gerät an und stellen Sie die Kontakte und Daten wieder her.
D. Sichern Sie das alte mobile Gerät auf einem PC und importieren Sie die Kontakte und den Zeitplan vom PC.
Answer: C

NEW QUESTION: 2
DRAG DROP


Answer:
Explanation:

NEW QUESTION: 3
어떤 품질 관리 및 제어 도구가 중첩 관계를 정의하는 체계적인 규칙 집합을 사용하는 분해 계층에서 부모 - 자식 관계를 시각화하는 데 유용합니까?
A. 네트워크 다이어그램
B. 선호도 다이어그램
C. 상호 관계 쌍극자
D. 트리 다이어그램
Answer: D

NEW QUESTION: 4
モデルトレーニング要件に適した早期停止基準を実装する必要があります。
ソリューションの開発に使用する必要がある3つのコードセグメントはどれですか？回答するには、適切なコードセグメントをコードセグメントのリストから回答領域に移動し、正しい順序で並べます。
注：回答の選択肢の複数の順序が正しいです。選択した正しい注文のいずれかのクレジットを受け取ります。

Answer:
Explanation:

Explanation:
You need to implement an early stopping criterion on models that provides savings without terminating promising jobs.
Truncation selection cancels a given percentage of lowest performing runs at each evaluation interval. Runs are compared based on their performance on the primary metric and the lowest X% are terminated.
Example:
from azureml.train.hyperdrive import TruncationSelectionPolicy
early_termination_policy = TruncationSelectionPolicy(evaluation_interval=1, truncation_percentage=20, delay_evaluation=5) Incorrect Answers:
Bandit is a termination policy based on slack factor/slack amount and evaluation interval. The policy early terminates any runs where the primary metric is not within the specified slack factor / slack amount with respect to the best performing training run.
Example:
from azureml.train.hyperdrive import BanditPolicy
early_termination_policy = BanditPolicy(slack_factor = 0.1, evaluation_interval=1, delay_evaluation=5 References:
https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-tune-hyperparameters