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NEW QUESTION: 1
A e-commerce network has many devices that often need to be upgraded on a regular basis. What technology will ensure IP packets continue to be forwarded even during a device failover?
A. enhanced object tracking
B. route processor redundancy
C. optimized edge routing
D. nonstop forwarding
E. stateful switchover
Answer: D
NEW QUESTION: 2
Sie haben ein Azure-Abonnement.
Sie müssen eine benutzerdefinierte Richtlinie implementieren, die die folgenden Anforderungen erfüllt:
* Stellt sicher, dass für jede neue Ressourcengruppe im Abonnement ein Tag namens organization auf den Wert Contoso festgelegt ist.
* Stellt sicher, dass eine Ressourcengruppe über das Azure-Portal erstellt werden kann.
* Stellt sicher, dass die Kompatibilitätsberichte im Azure-Portal korrekt sind.
Wie sollten Sie die Richtlinie vervollständigen? Wählen Sie zum Beantworten die entsprechenden Optionen im Bereich Antworten aus.
Answer:
Explanation:
Explanation
References: https://docs.microsoft.com/en-us/azure/governance/policy/concepts/definition-structure
NEW QUESTION: 3
Azure MachineLearningのリモートコンピューティングでトレーニング実験を実行しています。
実験は、mlflowおよびazureml-contrib-runパッケージを含むconda環境を使用するように構成されています。
実験で生成されたメトリックを追跡するためのロギングパッケージとしてMLflowを使用する必要があります。
実験用のスクリプトを完了する必要があります。
コードをどのように完成させ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: import mlflow
Import the mlflow and Workspace classes to access MLflow's tracking URI and configure your workspace.
Box 2: mlflow.start_run()
Set the MLflow experiment name with set_experiment() and start your training run with start_run().
Box 3: mlflow.log_metric(' ..')
Use log_metric() to activate the MLflow logging API and begin logging your training run metrics.
Box 4: mlflow.end_run()
Close the run:
run.endRun()
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow