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NEW QUESTION: 1
You develop software solutions for a mobile delivery service. You are developing a mobile app that users can use to order from a restaurant in their area. The app uses the following workflow:
1. A driver selects the restaurants for which they will deliver orders.
2. Orders are sent to all available drivers in an area.
3. Only orders for the selected restaurants will appear for the driver.
4. The first driver to accept an order removes it from the list of available orders.
You need to implement an Azure Service Bus solution.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation
Box 1: Create a single Service Bus Namespace
To begin using Service Bus messaging entities in Azure, you must first create a namespace with a name that is unique across Azure. A namespace provides a scoping container for addressing Service Bus resources within your application.
Box 2: Create a Service Bus Topic for each restaurant for which a driver can receive messages.
Create topics.
Box 3: Create a Service Bus subscription for each restaurant for which a driver can receive orders.
Topics can have multiple, independent subscriptions.
References:
https://docs.microsoft.com/en-us/azure/service-bus-messaging/service-bus-messaging-overview
NEW QUESTION: 2
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
NEW QUESTION: 3
Ein Unternehmen hat beschlossen, einen Vertrag mit einem Cloud-basierten Dienstanbieter abzuschließen, um dessen Identität als Serviceangebot zu nutzen. Sie verwenden Open Authentication (OAuth) 2.0, um externe Benutzer bei den Diensten des Unternehmens zu authentifizieren.
Welche der folgenden Optionen muss der Endbenutzer im Rahmen des Authentifizierungsprozesses bereitstellen?
A. Ein Zugriffstoken
B. Ein Passwort
C. Ein Benutzername und ein Passwort
D. Ein Benutzername
Answer: A
NEW QUESTION: 4
Refer to the exhibit.
Which action effectively secures the controller?
A. Use NAT on the router before the controller.
B. Blacklist the public WAN.
C. Whitelist the Admins network and block all others.
D. Require complex passwords.
E. Enable HTTP to HTTPS redirect.
Answer: A